__cudaFatFormat.h		__cudaFatFormat.h
	skipping to change at line 225		skipping to change at line 225
	* the name of a file containing debug inform ation		* the name of a file containing debug inform ation
	* on the returned cubin will be returned, or NULL		* on the returned cubin will be returned, or NULL
	* will be returned when cubin or such debug info		* will be returned when cubin or such debug info
	* cannot be found.		* cannot be found.
	*/		*/
	void fatGetCubinForGpuWithPolicy( __cudaFatCudaBinary *binary, __cudaFatCom pilationPolicy policy, char* gpuName, char* *cubin, char* *dbgInfoFile );		void fatGetCubinForGpuWithPolicy( __cudaFatCudaBinary *binary, __cudaFatCom pilationPolicy policy, char* gpuName, char* *cubin, char* *dbgInfoFile );
	#define fatGetCubinForGpu(binary,gpuName,cubin,dbgInfoFile) \		#define fatGetCubinForGpu(binary,gpuName,cubin,dbgInfoFile) \
	fatGetCubinForGpuWithPolicy(binary,__cudaFatAvoidPTX,gpuName,cubi n,dbgInfoFile)		fatGetCubinForGpuWithPolicy(binary,__cudaFatAvoidPTX,gpuName,cubi n,dbgInfoFile)
			/* like fatGetCubinForGpuWithPolicy, but just return candidate,
			* don't compile ptx to cubin */
			void __cudaFatGetCandidateForGpuWithPolicy( __cudaFatCudaBinary *binary, __
			cudaFatCompilationPolicy policy, char* gpuName, char* *candidate, unsigned
			char *is_ptx, char* *dbgInfoFile );
	/*		/*
	* Function : Check if a binary will be JITed for the specified targ et architecture		* Function : Check if a binary will be JITed for the specified targ et architecture
	* Parameters : binary (I) Fat binary		* Parameters : binary (I) Fat binary
	* policy (I) Compilation policy, as described by fatGet CubinForGpuWithPolicy		* policy (I) Compilation policy, as described by fatGet CubinForGpuWithPolicy
	* gpuName (I) Name of target GPU		* gpuName (I) Name of target GPU
	* ptx (O) PTX string to be JITed. Must be freed by c aller.		* ptx (O) PTX string to be JITed. Must be freed by c aller.
	* Function Result : True if the given binary will be JITed; otherwise, Fal se		* Function Result : True if the given binary will be JITed; otherwise, Fal se
	*/		*/
	unsigned char fatCheckJitForGpuWithPolicy( __cudaFatCudaBinary *binary, __c udaFatCompilationPolicy policy, char* gpuName, char* *ptx );		unsigned char fatCheckJitForGpuWithPolicy( __cudaFatCudaBinary *binary, __c udaFatCompilationPolicy policy, char* gpuName, char* *ptx );
End of changes. 1 change blocks.
	0 lines changed or deleted		6 lines changed or added

	builtin_types.h		builtin_types.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	#include "device_types.h"		#include "device_types.h"
	#include "driver_types.h"		#include "driver_types.h"
End of changes. 6 change blocks.
	28 lines changed or deleted		41 lines changed or added

	channel_descriptor.h		channel_descriptor.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(__CHANNEL_DESCRIPTOR_H__)		#if !defined(__CHANNEL_DESCRIPTOR_H__)
	#define __CHANNEL_DESCRIPTOR_H__		#define __CHANNEL_DESCRIPTOR_H__
	#if defined(__cplusplus)		#if defined(__cplusplus)
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	skipping to change at line 130		skipping to change at line 144
	{		{
	int e = (int)sizeof(unsigned short) * 8;		int e = (int)sizeof(unsigned short) * 8;
	return cudaCreateChannelDesc(e, e, e, e, cudaChannelFormatKindFloat);		return cudaCreateChannelDesc(e, e, e, e, cudaChannelFormatKindFloat);
	}		}
	template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc< char>(void)		template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc< char>(void)
	{		{
	int e = (int)sizeof(char) * 8;		int e = (int)sizeof(char) * 8;
	#if __SIGNED_CHARS__		#if defined(_CHAR_UNSIGNED) || defined(__CHAR_UNSIGNED__)
	return cudaCreateChannelDesc(e, 0, 0, 0, cudaChannelFormatKindSigned);
	#else
	return cudaCreateChannelDesc(e, 0, 0, 0, cudaChannelFormatKindUnsigned);		return cudaCreateChannelDesc(e, 0, 0, 0, cudaChannelFormatKindUnsigned);
	#endif		#else /* _CHAR_UNSIGNED || __CHAR_UNSIGNED__ */
			return cudaCreateChannelDesc(e, 0, 0, 0, cudaChannelFormatKindSigned);
			#endif /* _CHAR_UNSIGNED || __CHAR_UNSIGNED__ */
	}		}
	template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc< signed char>(void)		template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc< signed char>(void)
	{		{
	int e = (int)sizeof(signed char) * 8;		int e = (int)sizeof(signed char) * 8;
	return cudaCreateChannelDesc(e, 0, 0, 0, cudaChannelFormatKindSigned);		return cudaCreateChannelDesc(e, 0, 0, 0, cudaChannelFormatKindSigned);
	}		}
	template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc< unsigned char>(void)		template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc< unsigned char>(void)
End of changes. 8 change blocks.
	32 lines changed or deleted		45 lines changed or added

	cl.h		cl.h
	skipping to change at line 24		skipping to change at line 24
	*		*
	* THE MATERIALS ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,		* THE MATERIALS ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
	* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF		* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
	* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.		* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
	* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY		* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
	* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,		* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
	* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE		* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
	* MATERIALS OR THE USE OR OTHER DEALINGS IN THE MATERIALS.		* MATERIALS OR THE USE OR OTHER DEALINGS IN THE MATERIALS.
	************************************************************************** ****/		************************************************************************** ****/
	/* $Revision: 11708 $ on $Date: 2010-06-14 12:06:24 +0530 (Mon, 14 Jun 2010 ) $ */		/* $Revision: 11985 $ on $Date: 2010-07-15 23:46:06 +0530 (Thu, 15 Jul 2010 ) $ */
	#ifndef __OPENCL_CL_H		#ifndef __OPENCL_CL_H
	#define __OPENCL_CL_H		#define __OPENCL_CL_H
	#ifdef __APPLE__		#ifdef __APPLE__
	#include <OpenCL/cl_platform.h>		#include <OpenCL/cl_platform.h>
	#else		#else
	#include <CL/cl_platform.h>		#include <CL/cl_platform.h>
	#endif		#endif
	skipping to change at line 148		skipping to change at line 148
	#define CL_INVALID_WORK_GROUP_SIZE -54		#define CL_INVALID_WORK_GROUP_SIZE -54
	#define CL_INVALID_WORK_ITEM_SIZE -55		#define CL_INVALID_WORK_ITEM_SIZE -55
	#define CL_INVALID_GLOBAL_OFFSET -56		#define CL_INVALID_GLOBAL_OFFSET -56
	#define CL_INVALID_EVENT_WAIT_LIST -57		#define CL_INVALID_EVENT_WAIT_LIST -57
	#define CL_INVALID_EVENT -58		#define CL_INVALID_EVENT -58
	#define CL_INVALID_OPERATION -59		#define CL_INVALID_OPERATION -59
	#define CL_INVALID_GL_OBJECT -60		#define CL_INVALID_GL_OBJECT -60
	#define CL_INVALID_BUFFER_SIZE -61		#define CL_INVALID_BUFFER_SIZE -61
	#define CL_INVALID_MIP_LEVEL -62		#define CL_INVALID_MIP_LEVEL -62
	#define CL_INVALID_GLOBAL_WORK_SIZE -63		#define CL_INVALID_GLOBAL_WORK_SIZE -63
			#define CL_INVALID_PROPERTY -64
	/* OpenCL Version */		/* OpenCL Version */
	#define CL_VERSION_1_0 1		#define CL_VERSION_1_0 1
	#define CL_VERSION_1_1 1		#define CL_VERSION_1_1 1
	/* cl_bool */		/* cl_bool */
	#define CL_FALSE 0		#define CL_FALSE 0
	#define CL_TRUE 1		#define CL_TRUE 1
	/* cl_platform_info */		/* cl_platform_info */
	skipping to change at line 782		skipping to change at line 783
	size_t /* cb */,		size_t /* cb */,
	void * /* ptr */,		void * /* ptr */,
	cl_uint /* num_events_in_wait_list */,		cl_uint /* num_events_in_wait_list */,
	const cl_event * /* event_wait_list */,		const cl_event * /* event_wait_list */,
	cl_event * /* event */) CL_API_SUFFIX__VERSION _1_0;		cl_event * /* event */) CL_API_SUFFIX__VERSION _1_0;
	extern CL_API_ENTRY cl_int CL_API_CALL		extern CL_API_ENTRY cl_int CL_API_CALL
	clEnqueueReadBufferRect(cl_command_queue /* command_queue */,		clEnqueueReadBufferRect(cl_command_queue /* command_queue */,
	cl_mem /* buffer */,		cl_mem /* buffer */,
	cl_bool /* blocking_read */,		cl_bool /* blocking_read */,
	const size_t * /* buffer_offset */,		const size_t * /* buffer_origin */,
	const size_t * /* host_offset */,		const size_t * /* host_origin */,
	const size_t * /* region */,		const size_t * /* region */,
	size_t /* buffer_row_pitch */,		size_t /* buffer_row_pitch */,
	size_t /* buffer_slice_pitch */,		size_t /* buffer_slice_pitch */,
	size_t /* host_row_pitch */,		size_t /* host_row_pitch */,
	size_t /* host_slice_pitch */,		size_t /* host_slice_pitch */,
	void * /* ptr */,		void * /* ptr */,
	cl_uint /* num_events_in_wait_list */,		cl_uint /* num_events_in_wait_list */,
	const cl_event * /* event_wait_list */,		const cl_event * /* event_wait_list */,
	cl_event * /* event */) CL_API_SUFFIX__VER SION_1_1;		cl_event * /* event */) CL_API_SUFFIX__VER SION_1_1;
	skipping to change at line 808		skipping to change at line 809
	size_t /* offset */,		size_t /* offset */,
	size_t /* cb */,		size_t /* cb */,
	const void * /* ptr */,		const void * /* ptr */,
	cl_uint /* num_events_in_wait_list */,		cl_uint /* num_events_in_wait_list */,
	const cl_event * /* event_wait_list */,		const cl_event * /* event_wait_list */,
	cl_event * /* event */) CL_API_SUFFIX__VERSION _1_0;		cl_event * /* event */) CL_API_SUFFIX__VERSION _1_0;
	extern CL_API_ENTRY cl_int CL_API_CALL		extern CL_API_ENTRY cl_int CL_API_CALL
	clEnqueueWriteBufferRect(cl_command_queue /* command_queue */,		clEnqueueWriteBufferRect(cl_command_queue /* command_queue */,
	cl_mem /* buffer */,		cl_mem /* buffer */,
	cl_bool /* blocking_read */,		cl_bool /* blocking_write */,
	const size_t * /* buffer_offset */,		const size_t * /* buffer_origin */,
	const size_t * /* host_offset */,		const size_t * /* host_origin */,
	const size_t * /* region */,		const size_t * /* region */,
	size_t /* buffer_row_pitch */,		size_t /* buffer_row_pitch */,
	size_t /* buffer_slice_pitch */,		size_t /* buffer_slice_pitch */,
	size_t /* host_row_pitch */,		size_t /* host_row_pitch */,
	size_t /* host_slice_pitch */,		size_t /* host_slice_pitch */,
	const void * /* ptr */,		const void * /* ptr */,
	cl_uint /* num_events_in_wait_list */,		cl_uint /* num_events_in_wait_list */,
	const cl_event * /* event_wait_list */,		const cl_event * /* event_wait_list */,
	cl_event * /* event */) CL_API_SUFFIX__VE RSION_1_1;		cl_event * /* event */) CL_API_SUFFIX__VE RSION_1_1;
End of changes. 4 change blocks.
	6 lines changed or deleted		7 lines changed or added

	cl_ext.h		cl_ext.h
	skipping to change at line 24		skipping to change at line 24
	*		*
	* THE MATERIALS ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,		* THE MATERIALS ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
	* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF		* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
	* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.		* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
	* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY		* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
	* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,		* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
	* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE		* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
	* MATERIALS OR THE USE OR OTHER DEALINGS IN THE MATERIALS.		* MATERIALS OR THE USE OR OTHER DEALINGS IN THE MATERIALS.
	************************************************************************** ****/		************************************************************************** ****/
	/* $Revision: 11687 $ on $Date: 2010-06-12 03:47:22 +0530 (Sat, 12 Jun 2010 ) $ */		/* $Revision: 11928 $ on $Date: 2010-07-13 21:34:56 +0530 (Tue, 13 Jul 2010 ) $ */
	/* cl_ext.h contains OpenCL extensions which don't have external */		/* cl_ext.h contains OpenCL extensions which don't have external */
	/* (OpenGL, D3D) dependencies. */		/* (OpenGL, D3D) dependencies. */
	#ifndef __CL_EXT_H		#ifndef __CL_EXT_H
	#define __CL_EXT_H		#define __CL_EXT_H
	#ifdef __cplusplus		#ifdef __cplusplus
	extern "C" {		extern "C" {
	#endif		#endif
	skipping to change at line 161		skipping to change at line 161
	(CL_API_CALL *clRetainDeviceEXT_fn)( cl_device_id /*device*/ ) CL_EXT_S UFFIX__VERSION_1_1;		(CL_API_CALL *clRetainDeviceEXT_fn)( cl_device_id /*device*/ ) CL_EXT_S UFFIX__VERSION_1_1;
	typedef cl_ulong cl_device_partition_property_ext;		typedef cl_ulong cl_device_partition_property_ext;
	extern CL_API_ENTRY cl_int CL_API_CALL		extern CL_API_ENTRY cl_int CL_API_CALL
	clCreateSubDevicesEXT( cl_device_id /*in_device*/,		clCreateSubDevicesEXT( cl_device_id /*in_device*/,
	const cl_device_partition_property_ext * /* pro perties */,		const cl_device_partition_property_ext * /* pro perties */,
	cl_uint /*num_entries*/,		cl_uint /*num_entries*/,
	cl_device_id * /*out_devices*/,		cl_device_id * /*out_devices*/,
	cl_uint * /*num_devices*/ ) CL_EXT_SUFFIX__VERS ION_1_1;		cl_uint * /*num_devices*/ ) CL_EXT_SUFFIX__VERS ION_1_1;
	extern CL_API_ENTRY cl_int		typedef CL_API_ENTRY cl_int
	( CL_API_CALL * clCreateSubDevicesEXT_fn)( cl_device_id /*in_device*/,		( CL_API_CALL * clCreateSubDevicesEXT_fn)( cl_device_id /*in_device*/,
	const cl_device_partition_p roperty_ext * /* properties */,		const cl_device_partition_p roperty_ext * /* properties */,
	cl_uint /*num_entries*/,		cl_uint /*num_entries*/,
	cl_device_id * /*out_device s*/,		cl_device_id * /*out_device s*/,
	cl_uint * /*num_devices*/ ) CL_EXT_SUFFIX__VERSION_1_1;		cl_uint * /*num_devices*/ ) CL_EXT_SUFFIX__VERSION_1_1;
	/* cl_device_partition_property_ext */		/* cl_device_partition_property_ext */
	#define CL_DEVICE_PARTITION_EQUALLY_EXT 0x4050		#define CL_DEVICE_PARTITION_EQUALLY_EXT 0x4050
	#define CL_DEVICE_PARTITION_BY_COUNTS_EXT 0x4051		#define CL_DEVICE_PARTITION_BY_COUNTS_EXT 0x4051
	#define CL_DEVICE_PARTITION_BY_NAMES_EXT 0x4052		#define CL_DEVICE_PARTITION_BY_NAMES_EXT 0x4052
End of changes. 2 change blocks.
	2 lines changed or deleted		2 lines changed or added

	cl_platform.h		cl_platform.h
	skipping to change at line 39		skipping to change at line 39
	#ifdef __APPLE__		#ifdef __APPLE__
	/* Contains #defines for AVAILABLE_MAC_OS_X_VERSION_10_6_AND_LATER belo w */		/* Contains #defines for AVAILABLE_MAC_OS_X_VERSION_10_6_AND_LATER belo w */
	#include <AvailabilityMacros.h>		#include <AvailabilityMacros.h>
	#endif		#endif
	#ifdef __cplusplus		#ifdef __cplusplus
	extern "C" {		extern "C" {
	#endif		#endif
	#if defined(_WIN32)		#if defined(_WIN32)
	#define CL_API_ENTRY		#define CL_API_ENTRY
	#define CL_API_CALL __stdcall		#define CL_API_CALL __stdcall
	#define CL_CALLBACK __stdcall		#define CL_CALLBACK __stdcall
	#else		#else
	#define CL_API_ENTRY		#define CL_API_ENTRY
	#define CL_API_CALL		#define CL_API_CALL
	#define CL_CALLBACK		#define CL_CALLBACK
	#endif		#endif
	#ifdef __APPLE__		#ifdef __APPLE__
	#define CL_EXTENSION_WEAK_LINK __attribute__((weak_imp ort))		#define CL_EXTENSION_WEAK_LINK __attribute__((weak_imp ort))
	#define CL_API_SUFFIX__VERSION_1_0 AVAILABLE_MAC_OS_X_VERS ION_10_6_AND_LATER		#define CL_API_SUFFIX__VERSION_1_0 AVAILABLE_MAC_OS_X_VERS ION_10_6_AND_LATER
	#define CL_EXT_SUFFIX__VERSION_1_0 CL_EXTENSION_WEAK_LINK AVAILABLE_MAC_OS_X_VERSION_10_6_AND_LATER		#define CL_EXT_SUFFIX__VERSION_1_0 CL_EXTENSION_WEAK_LINK AVAILABLE_MAC_OS_X_VERSION_10_6_AND_LATER
	#define CL_API_SUFFIX__VERSION_1_1 CL_EXTENSION_WEAK_LINK		#define CL_API_SUFFIX__VERSION_1_1 CL_EXTENSION_WEAK_LINK
	#define CL_EXT_SUFFIX__VERSION_1_1 CL_EXTENSION_WEAK_LINK		#define CL_EXT_SUFFIX__VERSION_1_1 CL_EXTENSION_WEAK_LINK
	#define CL_EXT_SUFFIX__VERSION_1_0_DEPRECATED CL_EXTENSION_WEAK_LINK AVAILABLE_MAC_OS_X_VERSION_10_6_AND_LATER		#define CL_EXT_SUFFIX__VERSION_1_0_DEPRECATED CL_EXTENSION_WEAK_LINK AVAILABLE_MAC_OS_X_VERSION_10_6_AND_LATER
	#else		#else
	skipping to change at line 301		skipping to change at line 301
	#define __CL_INT4__ 1		#define __CL_INT4__ 1
	#define __CL_FLOAT4__ 1		#define __CL_FLOAT4__ 1
	#endif		#endif
	#if defined( __SSE__ )		#if defined( __SSE__ )
	#if defined( __MINGW64__ )		#if defined( __MINGW64__ )
	#include <intrin.h>		#include <intrin.h>
	#else		#else
	#include <xmmintrin.h>		#include <xmmintrin.h>
	#endif		#endif
	#if defined( __GNUC__ )		#if defined( __GNUC__ ) && !defined( __ICC )
	typedef float __cl_float4 __attribute__((vector_size(16)));		typedef float __cl_float4 __attribute__((vector_size(16)));
	#else		#else
	typedef __m128 __cl_float4;		typedef __m128 __cl_float4;
	#endif		#endif
	#define __CL_FLOAT4__ 1		#define __CL_FLOAT4__ 1
	#endif		#endif
	#if defined( __SSE2__ )		#if defined( __SSE2__ )
	#if defined( __MINGW64__ )		#if defined( __MINGW64__ )
	#include <intrin.h>		#include <intrin.h>
	#else		#else
	#include <emmintrin.h>		#include <emmintrin.h>
	#endif		#endif
	#if defined( __GNUC__ )		#if defined( __GNUC__ ) && !defined( __ICC )
	typedef cl_uchar __cl_uchar16 __attribute__((vector_size(16)) );		typedef cl_uchar __cl_uchar16 __attribute__((vector_size(16)) );
	typedef cl_char __cl_char16 __attribute__((vector_size(16)) );		typedef cl_char __cl_char16 __attribute__((vector_size(16)) );
	typedef cl_ushort __cl_ushort8 __attribute__((vector_size(16)) );		typedef cl_ushort __cl_ushort8 __attribute__((vector_size(16)) );
	typedef cl_short __cl_short8 __attribute__((vector_size(16)) );		typedef cl_short __cl_short8 __attribute__((vector_size(16)) );
	typedef cl_uint __cl_uint4 __attribute__((vector_size(16)) );		typedef cl_uint __cl_uint4 __attribute__((vector_size(16)) );
	typedef cl_int __cl_int4 __attribute__((vector_size(16)) );		typedef cl_int __cl_int4 __attribute__((vector_size(16)) );
	typedef cl_ulong __cl_ulong2 __attribute__((vector_size(16)) );		typedef cl_ulong __cl_ulong2 __attribute__((vector_size(16)) );
	typedef cl_long __cl_long2 __attribute__((vector_size(16)) );		typedef cl_long __cl_long2 __attribute__((vector_size(16)) );
	typedef cl_double __cl_double2 __attribute__((vector_size(16)) );		typedef cl_double __cl_double2 __attribute__((vector_size(16)) );
	#else		#else
	skipping to change at line 349		skipping to change at line 349
	#define __CL_SHORT8__ 1		#define __CL_SHORT8__ 1
	#define __CL_INT4__ 1		#define __CL_INT4__ 1
	#define __CL_UINT4__ 1		#define __CL_UINT4__ 1
	#define __CL_ULONG2__ 1		#define __CL_ULONG2__ 1
	#define __CL_LONG2__ 1		#define __CL_LONG2__ 1
	#define __CL_DOUBLE2__ 1		#define __CL_DOUBLE2__ 1
	#endif		#endif
	#if defined( __MMX__ )		#if defined( __MMX__ )
	#include <mmintrin.h>		#include <mmintrin.h>
	#if defined( __GNUC__ )		#if defined( __GNUC__ ) && !defined( __ICC )
	typedef cl_uchar __cl_uchar8 __attribute__((vector_size(8))) ;		typedef cl_uchar __cl_uchar8 __attribute__((vector_size(8))) ;
	typedef cl_char __cl_char8 __attribute__((vector_size(8))) ;		typedef cl_char __cl_char8 __attribute__((vector_size(8))) ;
	typedef cl_ushort __cl_ushort4 __attribute__((vector_size(8))) ;		typedef cl_ushort __cl_ushort4 __attribute__((vector_size(8))) ;
	typedef cl_short __cl_short4 __attribute__((vector_size(8))) ;		typedef cl_short __cl_short4 __attribute__((vector_size(8))) ;
	typedef cl_uint __cl_uint2 __attribute__((vector_size(8))) ;		typedef cl_uint __cl_uint2 __attribute__((vector_size(8))) ;
	typedef cl_int __cl_int2 __attribute__((vector_size(8))) ;		typedef cl_int __cl_int2 __attribute__((vector_size(8))) ;
	typedef cl_ulong __cl_ulong1 __attribute__((vector_size(8))) ;		typedef cl_ulong __cl_ulong1 __attribute__((vector_size(8))) ;
	typedef cl_long __cl_long1 __attribute__((vector_size(8))) ;		typedef cl_long __cl_long1 __attribute__((vector_size(8))) ;
	typedef cl_float __cl_float2 __attribute__((vector_size(8))) ;		typedef cl_float __cl_float2 __attribute__((vector_size(8))) ;
	#else		#else
	skipping to change at line 387		skipping to change at line 387
	#define __CL_LONG1__ 1		#define __CL_LONG1__ 1
	#define __CL_FLOAT2__ 1		#define __CL_FLOAT2__ 1
	#endif		#endif
	#if defined( __AVX__ )		#if defined( __AVX__ )
	#if defined( __MINGW64__ )		#if defined( __MINGW64__ )
	#include <intrin.h>		#include <intrin.h>
	#else		#else
	#include <immintrin.h>		#include <immintrin.h>
	#endif		#endif
	#if defined( __GNUC__ )		#if defined( __GNUC__ ) && !defined( __ICC )
	typedef cl_float __cl_float8 __attribute__((vector_size(32)) );		typedef cl_float __cl_float8 __attribute__((vector_size(32)) );
	typedef cl_double __cl_double4 __attribute__((vector_size(32)) );		typedef cl_double __cl_double4 __attribute__((vector_size(32)) );
	#else		#else
	typedef __m256 __cl_float8;		typedef __m256 __cl_float8;
	typedef __m256d __cl_double4;		typedef __m256d __cl_double4;
	#endif		#endif
	#define __CL_FLOAT8__ 1		#define __CL_FLOAT8__ 1
	#define __CL_DOUBLE4__ 1		#define __CL_DOUBLE4__ 1
	#endif		#endif
End of changes. 6 change blocks.
	10 lines changed or deleted		10 lines changed or added

	common_functions.h		common_functions.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(__COMMON_FUNCTIONS_H__)		#if !defined(__COMMON_FUNCTIONS_H__)
	#define __COMMON_FUNCTIONS_H__		#define __COMMON_FUNCTIONS_H__
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	skipping to change at line 65		skipping to change at line 79
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern _CRTIMP __host__ __device__ clock_t __cdecl clock(void) __THROW;		extern _CRTIMP __host__ __device__ clock_t __cdecl clock(void) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ void* __cdecl memset(void*, int, size_ t) __THROW;		extern __host__ __device__ void* __cdecl memset(void*, int, size_ t) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ void* __cdecl memcpy(void*, const void *, size_t) __THROW;		extern __host__ __device__ void* __cdecl memcpy(void*, const void *, size_t) __THROW;
	}		}
	#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 200		#if defined(__CUDA_ARCH__)
			#include <new>
			#if defined (__GNUC__)
			#define STD \
			std::
			#else /* __GNUC__ */
			#define STD
			#endif /* __GNUC__ */
			extern __host__ __device__ void* __cdecl operator new(STD size_t,
			void*) throw();
			extern __host__ __device__ void* __cdecl operator new[](STD size_
			t, void*) throw();
			extern __host__ __device__ void __cdecl operator delete(void*, v
			oid*) throw();
			extern __host__ __device__ void __cdecl operator delete[](void*,
			void*) throw();
			#if __CUDA_ARCH__ >= 200
	#include <stdio.h>		#include <stdio.h>
	#include <stdlib.h>		#include <stdlib.h>
	extern "C"		extern "C"
	{		{
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern _CRTIMP __host__ __device__ int __cdecl printf(const char*, ...) ;		extern _CRTIMP __host__ __device__ int __cdecl printf(const char*, ...) ;
	extern _CRTIMP __host__ __device__ void* __cdecl malloc(size_t) __THROW;		extern _CRTIMP __host__ __device__ void* __cdecl malloc(size_t) __THROW;
	extern _CRTIMP __host__ __device__ void __cdecl free(void*) __THROW;		extern _CRTIMP __host__ __device__ void __cdecl free(void*) __THROW;
	}		}
	#endif /* __CUDA_ARCH__ && __CUDA_ARCH__ >= 200 */		#if defined (__GNUC__)
			#define BADALLOC \
			bad_alloc
			#else /* __GNUC__ */
			#define BADALLOC \
			...
			#endif /* __GNUC__ */
			extern __host__ __device__ void* __cdecl operator new(STD size_t)
			throw(STD BADALLOC);
			extern __host__ __device__ void* __cdecl operator new[](STD size_
			t) throw(STD BADALLOC);
			extern __host__ __device__ void __cdecl operator delete(void*) t
			hrow();
			extern __host__ __device__ void __cdecl operator delete[](void*)
			throw();
			#undef BADALLOC
			#endif /* __CUDA_ARCH__ >= 200 */
			#undef STD
			#endif /* __CUDA_ARCH__ */
	#endif /* __cplusplus && __CUDACC__ */		#endif /* __cplusplus && __CUDACC__ */
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	#include "math_functions.h"		#include "math_functions.h"
End of changes. 8 change blocks.
	30 lines changed or deleted		94 lines changed or added

	cuComplex.h		cuComplex.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(CU_COMPLEX_H_)		#if !defined(CU_COMPLEX_H_)
	#define CU_COMPLEX_H_		#define CU_COMPLEX_H_
	#if defined(__cplusplus)		#if defined(__cplusplus)
	extern "C" {		extern "C" {
	#endif /* __cplusplus */		#endif /* __cplusplus */
	#include <math.h> /* import fabsf, sqrt */		#include <math.h> /* import fabsf, sqrt */
End of changes. 6 change blocks.
	28 lines changed or deleted		41 lines changed or added

	cublas.h		cublas.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	/*		/*
	* This is the public header file for the CUBLAS library, defining the API		* This is the public header file for the CUBLAS library, defining the API
	*		*
	* CUBLAS is an implementation of BLAS (Basic Linear Algebra Subroutines)		* CUBLAS is an implementation of BLAS (Basic Linear Algebra Subroutines)
	* on top of the CUDA driver. It allows access to the computational resourc		* on top of the CUDA runtime.
	es
	* of NVIDIA GPUs. The library is self-contained at the API level, i.e. no
	* direct interaction with the CUDA driver is necessary.
	*
	* The basic model by which applications use the CUBLAS library is to creat
	e
	* matrix and vector object in GPU memory space, fill them with data, then
	* call a sequence of BLAS functions, and finally upload the results from G
	PU
	* memory space back to the host. To accomplish this, CUBLAS provides helpe
	r
	* functions for creating and destroying objects in GPU space, and to write
	* data to, and retrieve data from, these objects.
	*
	* For maximum compatibility with existing Fortran environments,
	* CUBLAS uses column-major storage and 1-based indexing. Calls to
	* CUBLAS functions look very similar to calls to the original Fortran
	* BLAS functions. For example, the Fortran function call
	*
	* SDOT(KRANK+1-J,W(I,J),MDW,W(J,J),MDW)
	*
	* would map to the CUBLAS function call made from C/C++ code:
	*
	* #define IDX2(i,j,lead_dim) (((j)*(lead_dim))+(i))
	* sdot(krank+1-j,w[IDX2(i,j,ldw)], mdw, w[IDX2(i,j,ldw)], mdw)
	*
	* Since the BLAS core functions (as opposed to the helper functions) do
	* not return error status directly (for reasons of compatibility with
	* existing BLAS libraries) CUBLAS provides a separate function to
	* retrieve the last error that was recorded, to aid in debugging.
	*
	* Currently, only a subset of the BLAS core functions is implemented.
	*
	* The interface to the CUBLAS library is the header file cublas.h.
	* Applications using CUBLAS need to link against the DSO cublas.so
	* (Linux) or the DLL cublas.dll (Win32).
	*/		*/
	#if !defined(CUBLAS_H_)		#if !defined(CUBLAS_H_)
	#define CUBLAS_H_		#define CUBLAS_H_
	#ifdef __MULTI_CORE__		#include <cuda_runtime.h>
	#error CUBLAS not supported on multicore		#include "cublas_api.h"
	#endif
	#ifndef CUBLASAPI
	#ifdef _WIN32
	#define CUBLASAPI __stdcall
	#else
	#define CUBLASAPI
	#endif
	#endif
	#if defined(__cplusplus)		#if defined(__cplusplus)
	extern "C" {		extern "C" {
	#endif /* __cplusplus */		#endif
	#include "driver_types.h"
	#include "cuComplex.h" /* import complex data type */
	/* CUBLAS status returns */
	#define CUBLAS_STATUS_SUCCESS 0x00000000
	#define CUBLAS_STATUS_NOT_INITIALIZED 0x00000001
	#define CUBLAS_STATUS_ALLOC_FAILED 0x00000003
	#define CUBLAS_STATUS_INVALID_VALUE 0x00000007
	#define CUBLAS_STATUS_ARCH_MISMATCH 0x00000008
	#define CUBLAS_STATUS_MAPPING_ERROR 0x0000000B
	#define CUBLAS_STATUS_EXECUTION_FAILED 0x0000000D
	#define CUBLAS_STATUS_INTERNAL_ERROR 0x0000000E
	/* CUBLAS data types */		/* CUBLAS data types */
	typedef unsigned int cublasStatus;		#define cublasStatus cublasStatus_t
	/* CUBLAS helper functions */
	/*
	* cublasStatus
	* cublasInit (void)
	*
	* initializes the CUBLAS library and must be called before any other
	* CUBLAS API function is invoked. It allocates hardware resources
	* necessary for accessing the GPU.
	*
	* Return Values
	* -------------
	* CUBLAS_STATUS_ALLOC_FAILED if resources could not be allocated
	* CUBLAS_STATUS_SUCCESS if CUBLAS library initialized successfull
	y
	*/
	cublasStatus CUBLASAPI cublasInit (void);		cublasStatus CUBLASAPI cublasInit (void);
	/*
	* cublasStatus
	* cublasShutdown (void)
	*
	* releases CPU-side resources used by the CUBLAS library. The release of
	* GPU-side resources may be deferred until the application shuts down.
	*
	* Return Values
	* -------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_SUCCESS if CUBLAS library shut down successfully
	*/
	cublasStatus CUBLASAPI cublasShutdown (void);		cublasStatus CUBLASAPI cublasShutdown (void);
	/*
	* cublasStatus
	* cublasGetError (void)
	*
	* returns the last error that occurred on invocation of any of the
	* CUBLAS BLAS functions. While the CUBLAS helper functions return status
	* directly, the BLAS functions do not do so for improved
	* compatibility with existing environments that do not expect BLAS
	* functions to return status. Reading the error status via
	* cublasGetError() resets the internal error state to
	* CUBLAS_STATUS_SUCCESS.
	*/
	cublasStatus CUBLASAPI cublasGetError (void);		cublasStatus CUBLASAPI cublasGetError (void);
			cublasHandle_t CUBLASAPI cublasGetCurrentCtx(void);
			cublasStatus_t CUBLASAPI cublasSetError (cublasStatus_t error);
	/*		static __inline__ cublasStatus CUBLASAPI cublasGetVersion(int *version)
	* cublasStatus		{
	* cublasAlloc (int n, int elemSize, void **devicePtr)		cublasHandle_t handle = cublasGetCurrentCtx();
	*		return cublasGetVersion_v2(handle, version);
	* creates an object in GPU memory space capable of holding an array of		}
	* n elements, where each element requires elemSize bytes of storage. If
	* the function call is successful, a pointer to the object in GPU memory
	* space is placed in devicePtr. Note that this is a device pointer that
	* cannot be dereferenced in host code.
	*
	* Return Values
	* -------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n <= 0, or elemSize <= 0
	* CUBLAS_STATUS_ALLOC_FAILED if the object could not be allocated due
	to
	* lack of resources.
	* CUBLAS_STATUS_SUCCESS if storage was successfully allocated
	*/
	cublasStatus CUBLASAPI cublasAlloc (int n, int elemSize, void **devicePtr);
	/*
	* cublasStatus
	* cublasFree (const void *devicePtr)
	*
	* destroys the object in GPU memory space pointed to by devicePtr.
	*
	* Return Values
	* -------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INTERNAL_ERROR if the object could not be deallocated
	* CUBLAS_STATUS_SUCCESS if object was destroyed successfully
	*/
	cublasStatus CUBLASAPI cublasFree (const void *devicePtr);
	/*
	* cublasStatus
	* cublasSetVector (int n, int elemSize, const void *x, int incx,
	* void *y, int incy)
	*
	* copies n elements from a vector x in CPU memory space to a vector y
	* in GPU memory space. Elements in both vectors are assumed to have a
	* size of elemSize bytes. Storage spacing between consecutive elements
	* is incx for the source vector x and incy for the destination vector
	* y. In general, y points to an object, or part of an object, allocated
	* via cublasAlloc(). Column major format for two-dimensional matrices
	* is assumed throughout CUBLAS. Therefore, if the increment for a vector
	* is equal to 1, this access a column vector while using an increment
	* equal to the leading dimension of the respective matrix accesses a
	* row vector.
	*
	* Return Values
	* -------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library not been initialized
	* CUBLAS_STATUS_INVALID_VALUE if incx, incy, or elemSize <= 0
	* CUBLAS_STATUS_MAPPING_ERROR if an error occurred accessing GPU memory
	* CUBLAS_STATUS_SUCCESS if the operation completed successfully
	*/
	cublasStatus CUBLASAPI cublasSetVector (int n, int elemSize, const void *x,
	int incx, void *devicePtr, int incy
	);
	/*
	* cublasStatus
	* cublasGetVector (int n, int elemSize, const void *x, int incx,
	* void *y, int incy)
	*
	* copies n elements from a vector x in GPU memory space to a vector y
	* in CPU memory space. Elements in both vectors are assumed to have a
	* size of elemSize bytes. Storage spacing between consecutive elements
	* is incx for the source vector x and incy for the destination vector
	* y. In general, x points to an object, or part of an object, allocated
	* via cublasAlloc(). Column major format for two-dimensional matrices
	* is assumed throughout CUBLAS. Therefore, if the increment for a vector
	* is equal to 1, this access a column vector while using an increment
	* equal to the leading dimension of the respective matrix accesses a
	* row vector.
	*
	* Return Values
	* -------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library not been initialized
	* CUBLAS_STATUS_INVALID_VALUE if incx, incy, or elemSize <= 0
	* CUBLAS_STATUS_MAPPING_ERROR if an error occurred accessing GPU memory
	* CUBLAS_STATUS_SUCCESS if the operation completed successfully
	*/
	cublasStatus CUBLASAPI cublasGetVector (int n, int elemSize, const void *x,
	int incx, void *y, int incy);
	/*
	* cublasStatus
	* cublasSetMatrix (int rows, int cols, int elemSize, const void *A,
	* int lda, void *B, int ldb)
	*
	* copies a tile of rows x cols elements from a matrix A in CPU memory
	* space to a matrix B in GPU memory space. Each element requires storage
	* of elemSize bytes. Both matrices are assumed to be stored in column
	* major format, with the leading dimension (i.e. number of rows) of
	* source matrix A provided in lda, and the leading dimension of matrix B
	* provided in ldb. In general, B points to an object, or part of an
	* object, that was allocated via cublasAlloc().
	*
	* Return Values
	* -------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if rows or cols < 0, or elemSize, lda, or
	* ldb <= 0
	* CUBLAS_STATUS_MAPPING_ERROR if error occurred accessing GPU memory
	* CUBLAS_STATUS_SUCCESS if the operation completed successfully
	*/
	cublasStatus CUBLASAPI cublasSetMatrix (int rows, int cols, int elemSize,
	const void *A, int lda, void *B,
	int ldb);
	/*
	* cublasStatus
	* cublasGetMatrix (int rows, int cols, int elemSize, const void *A,
	* int lda, void *B, int ldb)
	*
	* copies a tile of rows x cols elements from a matrix A in GPU memory
	* space to a matrix B in CPU memory space. Each element requires storage
	* of elemSize bytes. Both matrices are assumed to be stored in column
	* major format, with the leading dimension (i.e. number of rows) of
	* source matrix A provided in lda, and the leading dimension of matrix B
	* provided in ldb. In general, A points to an object, or part of an
	* object, that was allocated via cublasAlloc().
	*
	* Return Values
	* -------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if rows, cols, eleSize, lda, or ldb <= 0
	* CUBLAS_STATUS_MAPPING_ERROR if error occurred accessing GPU memory
	* CUBLAS_STATUS_SUCCESS if the operation completed successfully
	*/
	cublasStatus CUBLASAPI cublasGetMatrix (int rows, int cols, int elemSize,
	const void *A, int lda, void *B,
	int ldb);
	/*
	* cublasStatus
	* cublasSetKernelStream ( cudaStream_t stream )
	*
	* set the CUBLAS stream in which all subsequent CUBLAS kernel launches wil
	l run.
	* By default, if the CUBLAS stream is not set, all kernels will use the NU
	LL
	* stream. This routine can be used to change the stream between kernels la
	unches
	* and can be used also to set the CUBLAS stream back to NULL.
	*
	* Return Values
	* -------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_SUCCESS if stream set successfully
	*/
	cublasStatus CUBLASAPI cublasSetKernelStream (cudaStream_t stream);
	/*
	* cublasStatus
	* cublasSetVectorAsync ( int n, int elemSize, const void *x, int incx,
	* void *y, int incy, cudaStream_t stream );
	*
	* cublasSetVectorAsync has the same functionnality as cublasSetVector
	* but the transfer is done asynchronously within the CUDA stream passed
	* in parameter.
	*
	* Return Values
	* -------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library not been initialized
	* CUBLAS_STATUS_INVALID_VALUE if incx, incy, or elemSize <= 0
	* CUBLAS_STATUS_MAPPING_ERROR if an error occurred accessing GPU memory
	* CUBLAS_STATUS_SUCCESS if the operation completed successfully
	*/
	cublasStatus CUBLASAPI cublasSetVectorAsync (int n, int elemSize,
	const void *hostPtr, int incx,
	void *devicePtr, int incy,
	cudaStream_t stream);
	/*
	* cublasStatus
	* cublasGetVectorAsync( int n, int elemSize, const void *x, int incx,
	* void *y, int incy, cudaStream_t stream)
	*
	* cublasGetVectorAsync has the same functionnality as cublasGetVector
	* but the transfer is done asynchronously within the CUDA stream passed
	* in parameter.
	*
	* Return Values
	* -------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library not been initialized
	* CUBLAS_STATUS_INVALID_VALUE if incx, incy, or elemSize <= 0
	* CUBLAS_STATUS_MAPPING_ERROR if an error occurred accessing GPU memory
	* CUBLAS_STATUS_SUCCESS if the operation completed successfully
	*/
	cublasStatus CUBLASAPI cublasGetVectorAsync (int n, int elemSize,
	const void *devicePtr, int inc
	x,
	void *hostPtr, int incy,
	cudaStream_t stream);
	/*
	* cublasStatus
	* cublasSetMatrixAsync (int rows, int cols, int elemSize, const void *A,
	* int lda, void *B, int ldb, cudaStream_t stream)
	*
	* cublasSetMatrixAsync has the same functionnality as cublasSetMatrix
	* but the transfer is done asynchronously within the CUDA stream passed
	* in parameter.
	*
	* Return Values
	* -------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if rows or cols < 0, or elemSize, lda, or
	* ldb <= 0
	* CUBLAS_STATUS_MAPPING_ERROR if error occurred accessing GPU memory
	* CUBLAS_STATUS_SUCCESS if the operation completed successfully
	*/
	cublasStatus CUBLASAPI cublasSetMatrixAsync (int rows, int cols, int elemSi
	ze,
	const void *A, int lda, void *
	B,
	int ldb, cudaStream_t stream);
	/*
	* cublasStatus
	* cublasGetMatrixAsync (int rows, int cols, int elemSize, const void *A,
	* int lda, void *B, int ldb, cudaStream_t stream)
	*
	* cublasGetMatrixAsync has the same functionnality as cublasGetMatrix
	* but the transfer is done asynchronously within the CUDA stream passed
	* in parameter.
	*
	* Return Values
	* -------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if rows, cols, eleSize, lda, or ldb <= 0
	* CUBLAS_STATUS_MAPPING_ERROR if error occurred accessing GPU memory
	* CUBLAS_STATUS_SUCCESS if the operation completed successfully
	*/
	cublasStatus CUBLASAPI cublasGetMatrixAsync (int rows, int cols, int elemSi
	ze,
	const void *A, int lda, void *
	B,
	int ldb, cudaStream_t stream);
	/* ---------------- CUBLAS single-precision BLAS1 functions ---------------
	- */
	/*
	* int
	* cublasIsamax (int n, const float *x, int incx)
	*
	* finds the smallest index of the maximum magnitude element of single
	* precision vector x; that is, the result is the first i, i = 0 to n - 1,
	* that maximizes abs(x[1 + i * incx])).
	*
	* Input
	* -----
	* n number of elements in input vector
	* x single precision vector with n elements
	* incx storage spacing between elements of x
	*
	* Output
	* ------
	* returns the smallest index (0 if n <= 0 or incx <= 0)
	*
	* Reference: http://www.netlib.org/blas/isamax.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	int CUBLASAPI cublasIsamax (int n, const float *x, int incx);
	/*
	* int
	* cublasIsamin (int n, const float *x, int incx)
	*
	* finds the smallest index of the minimum magnitude element of single
	* precision vector x; that is, the result is the first i, i = 0 to n - 1,
	* that minimizes abs(x[1 + i * incx])).
	*
	* Input
	* -----
	* n number of elements in input vector
	* x single precision vector with n elements
	* incx storage spacing between elements of x
	*
	* Output
	* ------
	* returns the smallest index (0 if n <= 0 or incx <= 0)
	*
	* Reference: http://www.netlib.org/scilib/blass.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	int CUBLASAPI cublasIsamin (int n, const float *x, int incx);
	/*
	* float
	* cublasSasum (int n, const float *x, int incx)
	*
	* computes the sum of the absolute values of the elements of single
	* precision vector x; that is, the result is the sum from i = 0 to n - 1 o
	f
	* abs(x[1 + i * incx]).
	*
	* Input
	* -----
	* n number of elements in input vector
	* x single precision vector with n elements
	* incx storage spacing between elements of x
	*
	* Output
	* ------
	* returns the single precision sum of absolute values
	* (0 if n <= 0 or incx <= 0, or if an error occurs)
	*
	* Reference: http://www.netlib.org/blas/sasum.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	float CUBLASAPI cublasSasum (int n, const float *x, int incx);
	/*
	* void
	* cublasSaxpy (int n, float alpha, const float *x, int incx, float *y,
	* int incy)
	*
	* multiplies single precision vector x by single precision scalar alpha
	* and adds the result to single precision vector y; that is, it overwrites
	* single precision y with single precision alpha * x + y. For i = 0 to n -
	1,
	* it replaces y[ly + i * incy] with alpha * x[lx + i * incx] + y[ly + i *
	* incy], where lx = 1 if incx >= 0, else lx = 1 +(1 - n) * incx, and ly is
	* defined in a similar way using incy.
	*
	* Input
	* -----
	* n number of elements in input vectors
	* alpha single precision scalar multiplier
	* x single precision vector with n elements
	* incx storage spacing between elements of x
	* y single precision vector with n elements
	* incy storage spacing between elements of y
	*
	* Output
	* ------
	* y single precision result (unchanged if n <= 0)
	*
	* Reference: http://www.netlib.org/blas/saxpy.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasSaxpy (int n, float alpha, const float *x, int incx,
	float *y, int incy);
	/*
	* void
	* cublasScopy (int n, const float *x, int incx, float *y, int incy)
	*
	* copies the single precision vector x to the single precision vector y. F
	or
	* i = 0 to n-1, copies x[lx + i * incx] to y[ly + i * incy], where lx = 1
	if
	* incx >= 0, else lx = 1 + (1 - n) * incx, and ly is defined in a similar
	* way using incy.
	*
	* Input
	* -----
	* n number of elements in input vectors
	* x single precision vector with n elements
	* incx storage spacing between elements of x
	* y single precision vector with n elements
	* incy storage spacing between elements of y
	*
	* Output
	* ------
	* y contains single precision vector x
	*
	* Reference: http://www.netlib.org/blas/scopy.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasScopy (int n, const float *x, int incx, float *y,
	int incy);
	/*
	* float
	* cublasSdot (int n, const float *x, int incx, const float *y, int incy)
	*
	* computes the dot product of two single precision vectors. It returns the
	* dot product of the single precision vectors x and y if successful, and
	* 0.0f otherwise. It computes the sum for i = 0 to n - 1 of x[lx + i *
	* incx] * y[ly + i * incy], where lx = 1 if incx >= 0, else lx = 1 + (1 -
	n)
	* *incx, and ly is defined in a similar way using incy.
	*
	* Input
	* -----
	* n number of elements in input vectors
	* x single precision vector with n elements
	* incx storage spacing between elements of x
	* y single precision vector with n elements
	* incy storage spacing between elements of y
	*
	* Output
	* ------
	* returns single precision dot product (zero if n <= 0)
	*
	* Reference: http://www.netlib.org/blas/sdot.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has nor been initialize
	d
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to execute on GPU
	*/
	float CUBLASAPI cublasSdot (int n, const float *x, int incx, const float *y
	,
	int incy);
	/*
	* float
	* cublasSnrm2 (int n, const float *x, int incx)
	*
	* computes the Euclidean norm of the single precision n-vector x (with
	* storage increment incx). This code uses a multiphase model of
	* accumulation to avoid intermediate underflow and overflow.
	*
	* Input
	* -----
	* n number of elements in input vector
	* x single precision vector with n elements
	* incx storage spacing between elements of x
	*
	* Output
	* ------
	* returns Euclidian norm (0 if n <= 0 or incx <= 0, or if an error occurs)
	*
	* Reference: http://www.netlib.org/blas/snrm2.f
	* Reference: http://www.netlib.org/slatec/lin/snrm2.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	float CUBLASAPI cublasSnrm2 (int n, const float *x, int incx);
	/*
	* void
	* cublasSrot (int n, float *x, int incx, float *y, int incy, float sc,
	* float ss)
	*
	* multiplies a 2x2 matrix ( sc ss) with the 2xn matrix ( transpose(x) )
	* (-ss sc) ( transpose(y) )
	*
	* The elements of x are in x[lx + i * incx], i = 0 ... n - 1, where lx = 1
	if
	* incx >= 0, else lx = 1 + (1 - n) * incx, and similarly for y using ly an
	d
	* incy.
	*
	* Input
	* -----
	* n number of elements in input vectors
	* x single precision vector with n elements
	* incx storage spacing between elements of x
	* y single precision vector with n elements
	* incy storage spacing between elements of y
	* sc element of rotation matrix
	* ss element of rotation matrix
	*
	* Output
	* ------
	* x rotated vector x (unchanged if n <= 0)
	* y rotated vector y (unchanged if n <= 0)
	*
	* Reference http://www.netlib.org/blas/srot.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasSrot (int n, float *x, int incx, float *y, int incy,
	float sc, float ss);
	/*
	* void
	* cublasSrotg (float *host_sa, float *host_sb, float *host_sc, float *host
	_ss)
	*
	* constructs the Givens tranformation
	*
	* ( sc ss )
	* G = ( ) , sc^2 + ss^2 = 1,
	* (-ss sc )
	*
	* which zeros the second entry of the 2-vector transpose(sa, sb).
	*
	* The quantity r = (+/-) sqrt (sa^2 + sb^2) overwrites sa in storage. The
	* value of sb is overwritten by a value z which allows sc and ss to be
	* recovered by the following algorithm:
	*
	* if z=1 set sc = 0.0 and ss = 1.0
	* if abs(z) < 1 set sc = sqrt(1-z^2) and ss = z
	* if abs(z) > 1 set sc = 1/z and ss = sqrt(1-sc^2)
	*
	* The function srot (n, x, incx, y, incy, sc, ss) normally is called next
	* to apply the transformation to a 2 x n matrix.
	* Note that this function is provided for completeness and run exclusively
	* on the Host.
	*
	* Input
	* -----
	* sa single precision scalar
	* sb single precision scalar
	*
	* Output
	* ------
	* sa single precision r
	* sb single precision z
	* sc single precision result
	* ss single precision result
	*
	* Reference: http://www.netlib.org/blas/srotg.f
	*
	* This function does not set any error status.
	*/
	void CUBLASAPI cublasSrotg (float *host_sa, float *host_sb, float *host_sc,
	float *host_ss);
	/*
	* void
	* cublasSrotm (int n, float *x, int incx, float *y, int incy,
	* const float* sparam)
	*
	* applies the modified Givens transformation, h, to the 2 x n matrix
	*
	* ( transpose(x) )
	* ( transpose(y) )
	*
	* The elements of x are in x[lx + i * incx], i = 0 to n-1, where lx = 1 if
	* incx >= 0, else lx = 1 + (1 - n) * incx, and similarly for y using ly an
	d
	* incy. With sparam[0] = sflag, h has one of the following forms:
	*
	* sflag = -1.0f sflag = 0.0f sflag = 1.0f sflag = -2.0f
	*
	* (sh00 sh01) (1.0f sh01) (sh00 1.0f) (1.0f 0.0f)
	* h = ( ) ( ) ( ) ( )
	* (sh10 sh11) (sh10 1.0f) (-1.0f sh11) (0.0f 1.0f)
	*
	* Input
	* -----
	* n number of elements in input vectors
	* x single precision vector with n elements
	* incx storage spacing between elements of x
	* y single precision vector with n elements
	* incy storage spacing between elements of y
	* sparam 5-element vector. sparam[0] is sflag described above. sparam[1]
	* through sparam[4] contain the 2x2 rotation matrix h: sparam[1]
	* contains sh00, sparam[2] contains sh10, sparam[3] contains sh01,
	* and sprams[4] contains sh11.
	*
	* Output
	* ------
	* x rotated vector x (unchanged if n <= 0)
	* y rotated vector y (unchanged if n <= 0)
	*
	* Reference: http://www.netlib.org/blas/srotm.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasSrotm(int n, float *x, int incx, float *y, int incy,
	const float* sparam);
	/*
	* void
	* cublasSrotmg (float *host_psd1, float *host_psd2, float *host_psx1, cons
	t float *host_psy1,
	* float *host_sparam)
	*
	* constructs the modified Givens transformation matrix h which zeros
	* the second component of the 2-vector transpose(sqrt(sd1)*sx1,sqrt(sd2)*s
	y1).
	* With sparam[0] = sflag, h has one of the following forms:
	*
	* sflag = -1.0f sflag = 0.0f sflag = 1.0f sflag = -2.0f
	*
	* (sh00 sh01) (1.0f sh01) (sh00 1.0f) (1.0f 0.0f)
	* h = ( ) ( ) ( ) ( )
	* (sh10 sh11) (sh10 1.0f) (-1.0f sh11) (0.0f 1.0f)
	*
	* sparam[1] through sparam[4] contain sh00, sh10, sh01, sh11,
	* respectively. Values of 1.0f, -1.0f, or 0.0f implied by the value
	* of sflag are not stored in sparam.
	* Note that this function is provided for completeness and run exclusively
	* on the Host.
	*
	* Input
	* -----
	* sd1 single precision scalar
	* sd2 single precision scalar
	* sx1 single precision scalar
	* sy1 single precision scalar
	*
	* Output
	* ------
	* sd1 changed to represent the effect of the transformation
	* sd2 changed to represent the effect of the transformation
	* sx1 changed to represent the effect of the transformation
	* sparam 5-element vector. sparam[0] is sflag described above. sparam[1]
	* through sparam[4] contain the 2x2 rotation matrix h: sparam[1]
	* contains sh00, sparam[2] contains sh10, sparam[3] contains sh01,
	* and sprams[4] contains sh11.
	*
	* Reference: http://www.netlib.org/blas/srotmg.f
	*
	* This functions does not set any error status.
	*/
	void CUBLASAPI cublasSrotmg (float *host_sd1, float *host_sd2, float *host_
	sx1,
	const float *host_sy1, float* host_sparam);
	/*
	* void
	* sscal (int n, float alpha, float *x, int incx)
	*
	* replaces single precision vector x with single precision alpha * x. For
	i
	* = 0 to n - 1, it replaces x[ix + i * incx] with alpha * x[ix + i * incx]
	,
	* where ix = 1 if incx >= 0, else ix = 1 + (1 - n) * incx.
	*
	* Input
	* -----
	* n number of elements in input vectors
	* alpha single precision scalar multiplier
	* x single precision vector with n elements
	* incx storage spacing between elements of x
	*
	* Output
	* ------
	* x single precision result (unchanged if n <= 0 or incx <= 0)
	*
	* Reference: http://www.netlib.org/blas/sscal.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasSscal (int n, float alpha, float *x, int incx);
	/*
	* void
	* cublasSswap (int n, float *x, int incx, float *y, int incy)
	*
	* replaces single precision vector x with single precision alpha * x. For
	i
	* = 0 to n - 1, it replaces x[ix + i * incx] with alpha * x[ix + i * incx]
	,
	* where ix = 1 if incx >= 0, else ix = 1 + (1 - n) * incx.
	*
	* Input
	* -----
	* n number of elements in input vectors
	* alpha single precision scalar multiplier
	* x single precision vector with n elements
	* incx storage spacing between elements of x
	*
	* Output
	* ------
	* x single precision result (unchanged if n <= 0 or incx <= 0)
	*
	* Reference: http://www.netlib.org/blas/sscal.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasSswap (int n, float *x, int incx, float *y, int incy);
	/* ----------------- CUBLAS single-complex BLAS1 functions ----------------
	- */
	/*
	* void
	* cublasCaxpy (int n, cuComplex alpha, const cuComplex *x, int incx,
	* cuComplex *y, int incy)
	*
	* multiplies single-complex vector x by single-complex scalar alpha and ad
	ds
	* the result to single-complex vector y; that is, it overwrites single-com
	plex
	* y with single-complex alpha * x + y. For i = 0 to n - 1, it replaces
	* y[ly + i * incy] with alpha * x[lx + i * incx] + y[ly + i * incy], where
	* lx = 0 if incx >= 0, else lx = 1 + (1 - n) * incx, and ly is defined in
	a
	* similar way using incy.
	*
	* Input
	* -----
	* n number of elements in input vectors
	* alpha single-complex scalar multiplier
	* x single-complex vector with n elements
	* incx storage spacing between elements of x
	* y single-complex vector with n elements
	* incy storage spacing between elements of y
	*
	* Output
	* ------
	* y single-complex result (unchanged if n <= 0)
	*
	* Reference: http://www.netlib.org/blas/caxpy.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasCaxpy (int n, cuComplex alpha, const cuComplex *x,
	int incx, cuComplex *y, int incy);
	/*
	* void
	* cublasCcopy (int n, const cuComplex *x, int incx, cuComplex *y, int incy
	)
	*
	* copies the single-complex vector x to the single-complex vector y. For
	* i = 0 to n-1, copies x[lx + i * incx] to y[ly + i * incy], where lx = 1
	if
	* incx >= 0, else lx = 1 + (1 - n) * incx, and ly is defined in a similar
	* way using incy.
	*
	* Input
	* -----
	* n number of elements in input vectors
	* x single-complex vector with n elements
	* incx storage spacing between elements of x
	* y single-complex vector with n elements
	* incy storage spacing between elements of y
	*
	* Output
	* ------
	* y contains single complex vector x
	*
	* Reference: http://www.netlib.org/blas/ccopy.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasCcopy (int n, const cuComplex *x, int incx, cuComplex
	*y,
	int incy);
	/*
	* void
	* cublasZcopy (int n, const cuDoubleComplex *x, int incx, cuDoubleComplex
	*y, int incy)
	*
	* copies the double-complex vector x to the double-complex vector y. For
	* i = 0 to n-1, copies x[lx + i * incx] to y[ly + i * incy], where lx = 1
	if
	* incx >= 0, else lx = 1 + (1 - n) * incx, and ly is defined in a similar
	* way using incy.
	*
	* Input
	* -----
	* n number of elements in input vectors
	* x double-complex vector with n elements
	* incx storage spacing between elements of x
	* y double-complex vector with n elements
	* incy storage spacing between elements of y
	*
	* Output
	* ------
	* y contains double complex vector x
	*
	* Reference: http://www.netlib.org/blas/zcopy.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZcopy (int n, const cuDoubleComplex *x, int incx, cuDo
	ubleComplex *y,
	int incy);
	/*
	* void
	* cublasCscal (int n, cuComplex alpha, cuComplex *x, int incx)
	*
	* replaces single-complex vector x with single-complex alpha * x. For i
	* = 0 to n - 1, it replaces x[ix + i * incx] with alpha * x[ix + i * incx]
	,
	* where ix = 1 if incx >= 0, else ix = 1 + (1 - n) * incx.
	*
	* Input
	* -----
	* n number of elements in input vectors
	* alpha single-complex scalar multiplier
	* x single-complex vector with n elements
	* incx storage spacing between elements of x
	*
	* Output
	* ------
	* x single-complex result (unchanged if n <= 0 or incx <= 0)
	*
	* Reference: http://www.netlib.org/blas/cscal.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasCscal (int n, cuComplex alpha, cuComplex *x, int incx)
	;
	/*
	* void
	* cublasCrotg (cuComplex *host_ca, cuComplex cb, float *host_sc, cuComplex
	*host_cs)
	*
	* constructs the complex Givens tranformation
	*
	* ( sc cs )
	* G = ( ) , sc^2 + cabs(cs)^2 = 1,
	* (-cs sc )
	*
	* which zeros the second entry of the complex 2-vector transpose(ca, cb).
	*
	* The quantity ca/cabs(ca)*norm(ca,cb) overwrites ca in storage. The
	* function crot (n, x, incx, y, incy, sc, cs) is normally called next
	* to apply the transformation to a 2 x n matrix.
	* Note that this function is provided for completeness and run exclusively
	* on the Host.
	*
	* Input
	* -----
	* ca single-precision complex precision scalar
	* cb single-precision complex scalar
	*
	* Output
	* ------
	* ca single-precision complex ca/cabs(ca)*norm(ca,cb)
	* sc single-precision cosine component of rotation matrix
	* cs single-precision complex sine component of rotation matrix
	*
	* Reference: http://www.netlib.org/blas/crotg.f
	*
	* This function does not set any error status.
	*/
	__host__ void CUBLASAPI cublasCrotg (cuComplex *host_ca, cuComplex cb, floa
	t *host_sc,
	cuComplex *host_cs);
	/*
	* void
	* cublasCrot (int n, cuComplex *x, int incx, cuComplex *y, int incy, float
	sc,
	* cuComplex cs)
	*
	* multiplies a 2x2 matrix ( sc cs) with the 2xn matrix ( transpose(x
	) )
	* (-conj(cs) sc) ( transpose(y
	) )
	*
	* The elements of x are in x[lx + i * incx], i = 0 ... n - 1, where lx = 1
	if
	* incx >= 0, else lx = 1 + (1 - n) * incx, and similarly for y using ly an
	d
	* incy.
	*
	* Input
	* -----
	* n number of elements in input vectors
	* x single-precision complex vector with n elements
	* incx storage spacing between elements of x
	* y single-precision complex vector with n elements
	* incy storage spacing between elements of y
	* sc single-precision cosine component of rotation matrix
	* cs single-precision complex sine component of rotation matrix
	*
	* Output
	* ------
	* x rotated single-precision complex vector x (unchanged if n <= 0)
	* y rotated single-precision complex vector y (unchanged if n <= 0)
	*
	* Reference: http://netlib.org/lapack/explore-html/crot.f.html
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasCrot (int n, cuComplex *x, int incx, cuComplex *y,
	int incy, float c, cuComplex s);
	/*		static __inline__ cublasStatus CUBLASAPI cublasAlloc (int n, int elemSize,
	* void		void **devicePtr)
	* csrot (int n, cuComplex *x, int incx, cuCumplex *y, int incy, float c,		{
	* float s)		cudaError_t cudaStat;
	*		unsigned long long int testSize;
	* multiplies a 2x2 rotation matrix ( c s) with a 2xn matrix ( transpose(x)		size_t allocSize;
	)		*devicePtr = 0;
	* (-s c) ( transpose(y)
	)
	*
	* The elements of x are in x[lx + i * incx], i = 0 ... n - 1, where lx = 1
	if
	* incx >= 0, else lx = 1 + (1 - n) * incx, and similarly for y using ly an
	d
	* incy.
	*
	* Input
	* -----
	* n number of elements in input vectors
	* x single-precision complex vector with n elements
	* incx storage spacing between elements of x
	* y single-precision complex vector with n elements
	* incy storage spacing between elements of y
	* c cosine component of rotation matrix
	* s sine component of rotation matrix
	*
	* Output
	* ------
	* x rotated vector x (unchanged if n <= 0)
	* y rotated vector y (unchanged if n <= 0)
	*
	* Reference http://www.netlib.org/blas/csrot.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasCsrot (int n, cuComplex *x, int incx, cuComplex *y,
	int incy, float c, float s);
	/*		if ((n <= 0) || (elemSize <= 0)) {
	* void		return CUBLAS_STATUS_INVALID_VALUE;
	* cublasCsscal (int n, float alpha, cuComplex *x, int incx)		}
	*		allocSize = (size_t)elemSize * (size_t)n;
	* replaces single-complex vector x with single-complex alpha * x. For i		testSize = (unsigned long long int)elemSize * (unsigned long long int)n
	* = 0 to n - 1, it replaces x[ix + i * incx] with alpha * x[ix + i * incx]		;
	,		if (testSize != (unsigned long long int)allocSize) { /* size_t overflow
	* where ix = 1 if incx >= 0, else ix = 1 + (1 - n) * incx.		! */
	*		return CUBLAS_STATUS_ALLOC_FAILED;
	* Input		}
	* -----		cudaStat = cudaMalloc (devicePtr, allocSize);
	* n number of elements in input vectors		if (cudaStat != cudaSuccess) {
	* alpha single precision scalar multiplier		return CUBLAS_STATUS_ALLOC_FAILED;
	* x single-complex vector with n elements		}
	* incx storage spacing between elements of x		return CUBLAS_STATUS_SUCCESS;
	*		}
	* Output
	* ------
	* x single-complex result (unchanged if n <= 0 or incx <= 0)
	*
	* Reference: http://www.netlib.org/blas/csscal.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasCsscal (int n, float alpha, cuComplex *x, int incx);
	/*		static __inline__ cublasStatus CUBLASAPI cublasFree (void *devicePtr)
	* void		{
	* cublasCswap (int n, const cuComplex *x, int incx, cuComplex *y, int incy		cudaError_t cudaStat;
	)
	*
	* interchanges the single-complex vector x with the single-complex vector
	y.
	* For i = 0 to n-1, interchanges x[lx + i * incx] with y[ly + i * incy], w
	here
	* lx = 1 if incx >= 0, else lx = 1 + (1 - n) * incx, and ly is defined in
	a
	* similar way using incy.
	*
	* Input
	* -----
	* n number of elements in input vectors
	* x single-complex vector with n elements
	* incx storage spacing between elements of x
	* y single-complex vector with n elements
	* incy storage spacing between elements of y
	*
	* Output
	* ------
	* x contains-single complex vector y
	* y contains-single complex vector x
	*
	* Reference: http://www.netlib.org/blas/cswap.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasCswap (int n, cuComplex *x, int incx, cuComplex *y,
	int incy);
	/*		if (devicePtr) {
	* void		cudaStat = cudaFree (devicePtr);
	* cublasZswap (int n, const cuDoubleComplex *x, int incx, cuDoubleComplex		if (cudaStat != cudaSuccess) {
	*y, int incy)		/* should never fail, except when there is internal corruption*
	*		/
	* interchanges the double-complex vector x with the double-complex vector		return CUBLAS_STATUS_INTERNAL_ERROR;
	y.		}
	* For i = 0 to n-1, interchanges x[lx + i * incx] with y[ly + i * incy], w		}
	here		return CUBLAS_STATUS_SUCCESS;
	* lx = 1 if incx >= 0, else lx = 1 + (1 - n) * incx, and ly is defined in		}
	a
	* similar way using incy.
	*
	* Input
	* -----
	* n number of elements in input vectors
	* x double-complex vector with n elements
	* incx storage spacing between elements of x
	* y double-complex vector with n elements
	* incy storage spacing between elements of y
	*
	* Output
	* ------
	* x contains-double complex vector y
	* y contains-double complex vector x
	*
	* Reference: http://www.netlib.org/blas/zswap.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZswap (int n, cuDoubleComplex *x, int incx, cuDoubleCo
	mplex *y,
	int incy);
	/*		static __inline__ cublasStatus CUBLASAPI cublasSetKernelStream (cudaStream_
	* cuComplex		t stream)
	* cdotu (int n, const cuComplex *x, int incx, const cuComplex *y, int incy		{
	)		cublasHandle_t handle = cublasGetCurrentCtx();
	*		return cublasSetStream_v2(handle, stream);
	* computes the dot product of two single-complex vectors. It returns the		}
	* dot product of the single-complex vectors x and y if successful, and com
	plex
	* zero otherwise. It computes the sum for i = 0 to n - 1 of x[lx + i * inc
	x] *
	* y[ly + i * incy], where lx = 1 if incx >= 0, else lx = 1 + (1 - n) * inc
	x;
	* ly is defined in a similar way using incy.
	*
	* Input
	* -----
	* n number of elements in input vectors
	* x single-complex vector with n elements
	* incx storage spacing between elements of x
	* y single-complex vector with n elements
	* incy storage spacing between elements of y
	*
	* Output
	* ------
	* returns single-complex dot product (zero if n <= 0)
	*
	* Reference: http://www.netlib.org/blas/cdotu.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has nor been initialize
	d
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to execute on GPU
	*/
	cuComplex CUBLASAPI cublasCdotu (int n, const cuComplex *x, int incx,
	const cuComplex *y, int incy);
	/*		/* Helper functions */
	* cuComplex		static __inline__ cublasOperation_t convertToOp( char trans )
	* cublasCdotc (int n, const cuComplex *x, int incx, const cuComplex *y,		{
	* int incy)		switch(trans) {
	*		case 'N':
	* computes the dot product of two single-complex vectors. It returns the		case 'n':
	* dot product of the single-complex vectors x and y if successful, and com		return CUBLAS_OP_N;
	plex		case 't':
	* zero otherwise. It computes the sum for i = 0 to n - 1 of x[lx + i * inc		case 'T':
	x] *		return CUBLAS_OP_T;
	* y[ly + i * incy], where lx = 1 if incx >= 0, else lx = 1 + (1 - n) * inc		case 'C':
	x;		case 'c':
	* ly is defined in a similar way using incy.		return CUBLAS_OP_C;
	*		default:
	* Input		return CUBLAS_OP_N;
	* -----		}
	* n number of elements in input vectors
	* x single-complex vector with n elements
	* incx storage spacing between elements of x
	* y single-complex vector with n elements
	* incy storage spacing between elements of y
	*
	* Output
	* ------
	* returns single-complex dot product (zero if n <= 0)
	*
	* Reference: http://www.netlib.org/blas/cdotc.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has nor been initialize
	d
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to execute on GPU
	*/
	cuComplex CUBLASAPI cublasCdotc (int n, const cuComplex *x, int incx,
	const cuComplex *y, int incy);
	/*		}
	* int		static __inline__ cublasFillMode_t convertToFillMode( char uplo )
	* cublasIcamax (int n, const float *x, int incx)		{
	*		switch (uplo) {
	* finds the smallest index of the element having maximum absolute value		case 'U':
	* in single-complex vector x; that is, the result is the first i, i = 0		case 'u':
	* to n - 1 that maximizes abs(real(x[1+i*incx]))+abs(imag(x[1 + i * incx])		return CUBLAS_FILL_MODE_UPPER;
	).		case 'L':
	*		case 'l':
	* Input		default:
	* -----		return CUBLAS_FILL_MODE_LOWER;
	* n number of elements in input vector		}
	* x single-complex vector with n elements		}
	* incx storage spacing between elements of x
	*
	* Output
	* ------
	* returns the smallest index (0 if n <= 0 or incx <= 0)
	*
	* Reference: http://www.netlib.org/blas/icamax.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	int CUBLASAPI cublasIcamax (int n, const cuComplex *x, int incx);
	/*		static __inline__ cublasDiagType_t convertToDiagType( char diag )
	* int		{
	* cublasIcamin (int n, const float *x, int incx)		switch (diag) {
	*		case 'U':
	* finds the smallest index of the element having minimum absolute value		case 'u':
	* in single-complex vector x; that is, the result is the first i, i = 0		return CUBLAS_DIAG_UNIT;
	* to n - 1 that minimizes abs(real(x[1+i*incx]))+abs(imag(x[1 + i * incx])		case 'N':
	).		case 'n':
	*		default:
	* Input		return CUBLAS_DIAG_NON_UNIT;
	* -----		}
	* n number of elements in input vector		}
	* x single-complex vector with n elements
	* incx storage spacing between elements of x
	*
	* Output
	* ------
	* returns the smallest index (0 if n <= 0 or incx <= 0)
	*
	* Reference: see ICAMAX.
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	int CUBLASAPI cublasIcamin (int n, const cuComplex *x, int incx);
	/*		static __inline__ cublasSideMode_t convertToSideMode( char side )
	* float		{
	* cublasScasum (int n, const cuDouble *x, int incx)		switch (side) {
	*		case 'R':
	* takes the sum of the absolute values of a complex vector and returns a		case 'r':
	* single precision result. Note that this is not the L1 norm of the vector		return CUBLAS_SIDE_RIGHT;
	.		case 'L':
	* The result is the sum from 0 to n-1 of abs(real(x[ix+i*incx])) +		case 'l':
	* abs(imag(x(ix+i*incx))), where ix = 1 if incx <= 0, else ix = 1+(1-n)*in		default:
	cx.		return CUBLAS_SIDE_LEFT;
	*		}
	* Input		}
	* -----
	* n number of elements in input vector
	* x single-complex vector with n elements
	* incx storage spacing between elements of x
	*
	* Output
	* ------
	* returns the single precision sum of absolute values of real and imaginar
	y
	* parts (0 if n <= 0 or incx <= 0, or if an error occurs)
	*
	* Reference: http://www.netlib.org/blas/scasum.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	float CUBLASAPI cublasScasum (int n, const cuComplex *x, int incx);
	/*		/* ---------------- CUBLAS BLAS1 functions ---------------- */
	* float		/* NRM2 */
	* cublasScnrm2 (int n, const cuComplex *x, int incx)		static __inline__ float CUBLASAPI cublasSnrm2 (int n, const float *x, int i
	*		ncx)
	* computes the Euclidean norm of the single-complex n-vector x. This code		{
	* uses simple scaling to avoid intermediate underflow and overflow.		float result;
	*		cublasHandle_t handle = cublasGetCurrentCtx();
	* Input		cublasStatus_t error = cublasSnrm2_v2(handle, n, x, incx, &result);
	* -----		cublasSetError(error);
	* n number of elements in input vector		return result;
	* x single-complex vector with n elements		}
	* incx storage spacing between elements of x		static __inline__ double CUBLASAPI cublasDnrm2 (int n, const double *x, int
	*		incx)
	* Output		{
	* ------		double result;
	* returns Euclidian norm (0 if n <= 0 or incx <= 0, or if an error occurs)		cublasHandle_t handle = cublasGetCurrentCtx();
	*		cublasStatus_t error = cublasDnrm2_v2(handle, n, x, incx, &result);
	* Reference: http://www.netlib.org/blas/scnrm2.f		cublasSetError(error);
	*		return result;
	* Error status for this function can be retrieved via cublasGetError().		}
	*		static __inline__ float CUBLASAPI cublasScnrm2 (int n, const cuComplex *x,
	* Error Status		int incx)
	* ------------		{
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize		float result;
	d		cublasHandle_t handle = cublasGetCurrentCtx();
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU		cublasStatus_t error = cublasScnrm2_v2(handle, n, x, incx, &result);
	*/		cublasSetError(error);
	float CUBLASAPI cublasScnrm2 (int n, const cuComplex *x, int incx);		return result;
			}
			static __inline__ double CUBLASAPI cublasDznrm2 (int n, const cuDoubleCompl
			ex *x, int incx)
			{
			double result;
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasDznrm2_v2(handle, n, x, incx, &result);
			cublasSetError(error);
			return result;
			}
			/*------------------------------------------------------------------------*
			/
			/* DOT */
			static __inline__ float CUBLASAPI cublasSdot (int n, const float *x, int in
			cx, const float *y,
			int incy)
			{
			float result;
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasSdot_v2(handle, n, x, incx, y, incy, &resu
			lt);
			cublasSetError(error);
			return result;
			}
			static __inline__ double CUBLASAPI cublasDdot (int n, const double *x, int
			incx, const double *y,
			int incy)
			{
			double result;
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasDdot_v2(handle, n, x, incx, y, incy, &resu
			lt);
			cublasSetError(error);
			return result;
			}
			static __inline__ cuComplex CUBLASAPI cublasCdotu (int n, const cuComplex *
			x, int incx, const cuComplex *y,
			int incy)
			{
			cuComplex result;
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasCdotu_v2(handle, n, x, incx, y, incy, &res
			ult);
			cublasSetError(error);
			return result;
			}
			static __inline__ cuComplex CUBLASAPI cublasCdotc (int n, const cuComplex *
			x, int incx, const cuComplex *y,
			int incy)
			{
			cuComplex result;
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasCdotc_v2(handle, n, x, incx, y, incy, &res
			ult);
			cublasSetError(error);
			return result;
			}
	/* ----------------- CUBLAS double-complex BLAS1 functions ----------------		static __inline__ cuDoubleComplex CUBLASAPI cublasZdotu (int n, const cuDou
	- */		bleComplex *x, int incx, const cuDoubleComplex *y,
			int incy)
			{
			cuDoubleComplex result;
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasZdotu_v2(handle, n, x, incx, y, incy, &res
			ult);
			cublasSetError(error);
			return result;
			}
			static __inline__ cuDoubleComplex CUBLASAPI cublasZdotc (int n, const cuDou
			bleComplex *x, int incx, const cuDoubleComplex *y,
			int incy)
			{
			cuDoubleComplex result;
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasZdotc_v2(handle, n, x, incx, y, incy, &res
			ult);
			cublasSetError(error);
			return result;
			}
			/*------------------------------------------------------------------------*
			/
			/* SCAL */
			static __inline__ void CUBLASAPI cublasSscal (int n, float alpha, float *x,
			int incx)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasSscal_v2(handle, n, &alpha, x, incx);
			cublasSetError(error);
			}
			static __inline__ void CUBLASAPI cublasDscal (int n, double alpha, double *
			x, int incx)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasDscal_v2(handle, n, &alpha, x, incx);
			cublasSetError(error);
			}
			static __inline__ void CUBLASAPI cublasCscal (int n, cuComplex alpha, cuCom
			plex *x, int incx)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasCscal_v2(handle, n, &alpha, x, incx);
			cublasSetError(error);
			}
			static __inline__ void CUBLASAPI cublasZscal (int n, cuDoubleComplex alpha,
			cuDoubleComplex *x, int incx)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasZscal_v2(handle, n, &alpha, x, incx);
			cublasSetError(error);
			}
	/*		static __inline__ void CUBLASAPI cublasCsscal (int n, float alpha, cuComple
	* void		x *x, int incx)
	* cublasZaxpy (int n, cuDoubleComplex alpha, const cuDoubleComplex *x, int		{
	incx,		cublasHandle_t handle = cublasGetCurrentCtx();
	* cuDoubleComplex *y, int incy)		cublasStatus_t error = cublasCsscal_v2(handle, n, &alpha, x, incx);
	*		cublasSetError(error);
	* multiplies double-complex vector x by double-complex scalar alpha and ad		}
	ds		static __inline__ void CUBLASAPI cublasZdscal (int n, double alpha, cuDoubl
	* the result to double-complex vector y; that is, it overwrites double-com		eComplex *x, int incx)
	plex		{
	* y with double-complex alpha * x + y. For i = 0 to n - 1, it replaces		cublasHandle_t handle = cublasGetCurrentCtx();
	* y[ly + i * incy] with alpha * x[lx + i * incx] + y[ly + i * incy], where		cublasStatus_t error = cublasZdscal_v2(handle, n, &alpha, x, incx);
	* lx = 0 if incx >= 0, else lx = 1 + (1 - n) * incx, and ly is defined in		cublasSetError(error);
	a		}
	* similar way using incy.		/*------------------------------------------------------------------------*
	*		/
	* Input		/* AXPY */
	* -----		static __inline__ void CUBLASAPI cublasSaxpy (int n, float alpha, const flo
	* n number of elements in input vectors		at *x, int incx,
	* alpha double-complex scalar multiplier		float *y, int incy)
	* x double-complex vector with n elements		{
	* incx storage spacing between elements of x		cublasHandle_t handle = cublasGetCurrentCtx();
	* y double-complex vector with n elements		cublasStatus_t error = cublasSaxpy_v2(handle, n, &alpha, x, incx, y, in
	* incy storage spacing between elements of y		cy);
	*		cublasSetError(error);
	* Output		}
	* ------		static __inline__ void CUBLASAPI cublasDaxpy (int n, double alpha, const do
	* y double-complex result (unchanged if n <= 0)		uble *x,
	*		int incx, double *y, int incy)
	* Reference: http://www.netlib.org/blas/zaxpy.f		{
	*		cublasHandle_t handle = cublasGetCurrentCtx();
	* Error status for this function can be retrieved via cublasGetError().		cublasStatus_t error = cublasDaxpy_v2(handle, n, &alpha, x, incx, y, in
	*		cy);
	* Error Status		cublasSetError(error);
	* ------------		}
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize		static __inline__ void CUBLASAPI cublasCaxpy (int n, cuComplex alpha, const
	d		cuComplex *x,
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU		int incx, cuComplex *y, int incy)
	*/		{
	void CUBLASAPI cublasZaxpy (int n, cuDoubleComplex alpha, const cuDoubleCom		cublasHandle_t handle = cublasGetCurrentCtx();
	plex *x,		cublasStatus_t error = cublasCaxpy_v2(handle, n, &alpha, x, incx, y, in
	int incx, cuDoubleComplex *y, int incy);		cy);
			cublasSetError(error);
			}
			static __inline__ void CUBLASAPI cublasZaxpy (int n, cuDoubleComplex alpha,
			const cuDoubleComplex *x,
			int incx, cuDoubleComplex *y, int incy)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasZaxpy_v2(handle, n, &alpha, x, incx, y, in
			cy);
			cublasSetError(error);
			}
			/*------------------------------------------------------------------------*
			/
			/* COPY */
			static __inline__ void CUBLASAPI cublasScopy (int n, const float *x, int in
			cx, float *y,
			int incy)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasScopy_v2(handle,n,x,incx,y,incy);
			cublasSetError(error);
			}
			static __inline__ void CUBLASAPI cublasDcopy (int n, const double *x, int i
			ncx, double *y,
			int incy)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasDcopy_v2(handle,n,x,incx,y,incy);
			cublasSetError(error);
			}
			static __inline__ void CUBLASAPI cublasCcopy (int n, const cuComplex *x, in
			t incx, cuComplex *y,
			int incy)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasCcopy_v2(handle,n,x,incx,y,incy);
			cublasSetError(error);
			}
			static __inline__ void CUBLASAPI cublasZcopy (int n, const cuDoubleComplex
			*x, int incx, cuDoubleComplex *y,
			int incy)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasZcopy_v2(handle,n,x,incx,y,incy);
			cublasSetError(error);
			}
			/*------------------------------------------------------------------------*
			/
			/* SWAP */
			static __inline__ void CUBLASAPI cublasSswap (int n, float *x, int incx, fl
			oat *y, int incy)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasSswap_v2(handle,n,x,incx,y,incy);
			cublasSetError(error);
			}
			static __inline__ void CUBLASAPI cublasDswap (int n, double *x, int incx, d
			ouble *y, int incy)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasDswap_v2(handle,n,x,incx,y,incy);
			cublasSetError(error);
			}
			static __inline__ void CUBLASAPI cublasCswap (int n, cuComplex *x, int incx
			, cuComplex *y, int incy)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasCswap_v2(handle,n,x,incx,y,incy);
			cublasSetError(error);
			}
			static __inline__ void CUBLASAPI cublasZswap (int n, cuDoubleComplex *x, in
			t incx, cuDoubleComplex *y, int incy)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasZswap_v2(handle,n,x,incx,y,incy);
			cublasSetError(error);
			}
			/*------------------------------------------------------------------------*
			/
			/* AMAX */
			static __inline__ int CUBLASAPI cublasIsamax (int n, const float *x, int in
			cx)
			{
			int result;
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasIsamax_v2(handle, n, x, incx, &result);
			cublasSetError(error);
			return result;
			}
	/*		static __inline__ int CUBLASAPI cublasIdamax (int n, const double *x, int i
	* cuDoubleComplex		ncx)
	* zdotu (int n, const cuDoubleComplex *x, int incx, const cuDoubleComplex		{
	*y, int incy)		int result;
	*		cublasHandle_t handle = cublasGetCurrentCtx();
	* computes the dot product of two double-complex vectors. It returns the		cublasStatus_t error = cublasIdamax_v2(handle, n, x, incx, &result);
	* dot product of the double-complex vectors x and y if successful, and dou		cublasSetError(error);
	ble-complex		return result;
	* zero otherwise. It computes the sum for i = 0 to n - 1 of x[lx + i * inc		}
	x] *
	* y[ly + i * incy], where lx = 1 if incx >= 0, else lx = 1 + (1 - n) * inc
	x;
	* ly is defined in a similar way using incy.
	*
	* Input
	* -----
	* n number of elements in input vectors
	* x double-complex vector with n elements
	* incx storage spacing between elements of x
	* y double-complex vector with n elements
	* incy storage spacing between elements of y
	*
	* Output
	* ------
	* returns double-complex dot product (zero if n <= 0)
	*
	* Reference: http://www.netlib.org/blas/zdotu.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has nor been initialize
	d
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to execute on GPU
	*/
	cuDoubleComplex CUBLASAPI cublasZdotu (int n, const cuDoubleComplex *x, int
	incx,
	const cuDoubleComplex *y, int incy);
	/*		static __inline__ int CUBLASAPI cublasIcamax (int n, const cuComplex *x, in
	* cuDoubleComplex		t incx)
	* cublasZdotc (int n, const cuDoubleComplex *x, int incx, const cuDoubleCo		{
	mplex *y, int incy)		int result;
	*		cublasHandle_t handle = cublasGetCurrentCtx();
	* computes the dot product of two double-precision complex vectors. It ret		cublasStatus_t error = cublasIcamax_v2(handle, n, x, incx, &result);
	urns the		cublasSetError(error);
	* dot product of the double-precision complex vectors conjugate(x) and y i		return result;
	f successful,
	* and double-precision complex zero otherwise. It computes the
	* sum for i = 0 to n - 1 of conjugate(x[lx + i * incx]) * y[ly + i * incy
	],
	* where lx = 1 if incx >= 0, else lx = 1 + (1 - n) * incx;
	* ly is defined in a similar way using incy.
	*
	* Input
	* -----
	* n number of elements in input vectors
	* x double-precision complex vector with n elements
	* incx storage spacing between elements of x
	* y double-precision complex vector with n elements
	* incy storage spacing between elements of y
	*
	* Output
	* ------
	* returns double-complex dot product (zero if n <= 0)
	*
	* Reference: http://www.netlib.org/blas/zdotc.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has nor been initialize
	d
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to execute on GPU
	*/
	cuDoubleComplex CUBLASAPI cublasZdotc( int n, const cuDoubleComplex *x, int
	incx,
	const cuDoubleComplex *y, int incy )
	;
	/*		}
	* void
	* cublasZscal (int n, cuComplex alpha, cuComplex *x, int incx)
	*
	* replaces double-complex vector x with double-complex alpha * x. For i
	* = 0 to n - 1, it replaces x[ix + i * incx] with alpha * x[ix + i * incx]
	,
	* where ix = 1 if incx >= 0, else ix = 1 + (1 - n) * incx.
	*
	* Input
	* -----
	* n number of elements in input vectors
	* alpha double-complex scalar multiplier
	* x double-complex vector with n elements
	* incx storage spacing between elements of x
	*
	* Output
	* ------
	* x double-complex result (unchanged if n <= 0 or incx <= 0)
	*
	* Reference: http://www.netlib.org/blas/zscal.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZscal (int n, cuDoubleComplex alpha, cuDoubleComplex *
	x, int incx);
	/*		static __inline__ int CUBLASAPI cublasIzamax (int n, const cuDoubleComplex
	* void		*x, int incx)
	* cublasZdscal (int n, double alpha, cuDoubleComplex *x, int incx)		{
	*		int result;
	* replaces double-complex vector x with double-complex alpha * x. For i		cublasHandle_t handle = cublasGetCurrentCtx();
	* = 0 to n - 1, it replaces x[ix + i * incx] with alpha * x[ix + i * incx]		cublasStatus_t error = cublasIzamax_v2(handle, n, x, incx, &result);
	,		cublasSetError(error);
	* where ix = 1 if incx >= 0, else ix = 1 + (1 - n) * incx.		return result;
	*		}
	* Input		/*------------------------------------------------------------------------*
	* -----		/
	* n number of elements in input vectors		/* AMIN */
	* alpha double precision scalar multiplier		static __inline__ int CUBLASAPI cublasIsamin (int n, const float *x, int in
	* x double-complex vector with n elements		cx)
	* incx storage spacing between elements of x		{
	*		int result;
	* Output		cublasHandle_t handle = cublasGetCurrentCtx();
	* ------		cublasStatus_t error = cublasIsamin_v2(handle, n, x, incx, &result);
	* x double-complex result (unchanged if n <= 0 or incx <= 0)		cublasSetError(error);
	*		return result;
	* Reference: http://www.netlib.org/blas/zdscal.f		}
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZdscal (int n, double alpha, cuDoubleComplex *x,
	int incx);
	/*		static __inline__ int CUBLASAPI cublasIdamin (int n, const double *x, int i
	* double		ncx)
	* cublasDznrm2 (int n, const cuDoubleComplex *x, int incx)		{
	*		int result;
	* computes the Euclidean norm of the double precision complex n-vector x.		cublasHandle_t handle = cublasGetCurrentCtx();
	This code		cublasStatus_t error = cublasIdamin_v2(handle, n, x, incx, &result);
	* uses simple scaling to avoid intermediate underflow and overflow.		cublasSetError(error);
	*		return result;
	* Input		}
	* -----
	* n number of elements in input vector
	* x double-complex vector with n elements
	* incx storage spacing between elements of x
	*
	* Output
	* ------
	* returns Euclidian norm (0 if n <= 0 or incx <= 0, or if an error occurs)
	*
	* Reference: http://www.netlib.org/blas/dznrm2.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	double CUBLASAPI cublasDznrm2 (int n, const cuDoubleComplex *x, int incx);
	/*		static __inline__ int CUBLASAPI cublasIcamin (int n, const cuComplex *x, in
	* void		t incx)
	* cublasZrotg (cuDoubleComplex *host_ca, cuDoubleComplex cb, double *host_		{
	sc, double *host_cs)		int result;
	*		cublasHandle_t handle = cublasGetCurrentCtx();
	* constructs the complex Givens tranformation		cublasStatus_t error = cublasIcamin_v2(handle, n, x, incx, &result);
	*		cublasSetError(error);
	* ( sc cs )		return result;
	* G = ( ) , sc^2 + cabs(cs)^2 = 1,		}
	* (-cs sc )
	*
	* which zeros the second entry of the complex 2-vector transpose(ca, cb).
	*
	* The quantity ca/cabs(ca)*norm(ca,cb) overwrites ca in storage. The
	* function crot (n, x, incx, y, incy, sc, cs) is normally called next
	* to apply the transformation to a 2 x n matrix.
	* Note that this function is provided for completeness and run exclusively
	* on the Host.
	*
	* Input
	* -----
	* ca double-precision complex precision scalar
	* cb double-precision complex scalar
	*
	* Output
	* ------
	* ca double-precision complex ca/cabs(ca)*norm(ca,cb)
	* sc double-precision cosine component of rotation matrix
	* cs double-precision complex sine component of rotation matrix
	*
	* Reference: http://www.netlib.org/blas/zrotg.f
	*
	* This function does not set any error status.
	*/
	void CUBLASAPI cublasZrotg (cuDoubleComplex *host_ca, cuDoubleComplex cb, d
	ouble *host_sc,
	cuDoubleComplex *host_cs);
	/*		static __inline__ int CUBLASAPI cublasIzamin (int n, const cuDoubleComplex
	* cublasZrot (int n, cuDoubleComplex *x, int incx, cuDoubleComplex *y, int		*x, int incx)
	incy, double sc,		{
	* cuDoubleComplex cs)		int result;
	*		cublasHandle_t handle = cublasGetCurrentCtx();
	* multiplies a 2x2 matrix ( sc cs) with the 2xn matrix ( transpose(x		cublasStatus_t error = cublasIzamin_v2(handle, n, x, incx, &result);
	) )		cublasSetError(error);
	* (-conj(cs) sc) ( transpose(y		return result;
	) )		}
	*		/*------------------------------------------------------------------------*
	* The elements of x are in x[lx + i * incx], i = 0 ... n - 1, where lx = 1		/
	if		/* ASUM */
	* incx >= 0, else lx = 1 + (1 - n) * incx, and similarly for y using ly an		static __inline__ float CUBLASAPI cublasSasum (int n, const float *x, int i
	d		ncx)
	* incy.		{
	*		float result;
	* Input		cublasHandle_t handle = cublasGetCurrentCtx();
	* -----		cublasStatus_t error = cublasSasum_v2(handle, n, x, incx, &result);
	* n number of elements in input vectors		cublasSetError(error);
	* x double-precision complex vector with n elements		return result;
	* incx storage spacing between elements of x		}
	* y double-precision complex vector with n elements		static __inline__ double CUBLASAPI cublasDasum (int n, const double *x, int
	* incy storage spacing between elements of y		incx)
	* sc double-precision cosine component of rotation matrix		{
	* cs double-precision complex sine component of rotation matrix		double result;
	*		cublasHandle_t handle = cublasGetCurrentCtx();
	* Output		cublasStatus_t error = cublasDasum_v2(handle, n, x, incx, &result);
	* ------		cublasSetError(error);
	* x rotated double-precision complex vector x (unchanged if n <= 0)		return result;
	* y rotated double-precision complex vector y (unchanged if n <= 0)		}
	*		static __inline__ float CUBLASAPI cublasScasum (int n, const cuComplex *x,
	* Reference: http://netlib.org/lapack/explore-html/zrot.f.html		int incx)
	*		{
	* Error status for this function can be retrieved via cublasGetError().		float result;
	*		cublasHandle_t handle = cublasGetCurrentCtx();
	* Error Status		cublasStatus_t error = cublasScasum_v2(handle, n, x, incx, &result);
	* ------------		cublasSetError(error);
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize		return result;
	d		}
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support		static __inline__ double CUBLASAPI cublasDzasum (int n, const cuDoubleCompl
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU		ex *x, int incx)
	*/		{
	void CUBLASAPI cublasZrot (int n, cuDoubleComplex *x, int incx,		double result;
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasDzasum_v2(handle, n, x, incx, &result);
			cublasSetError(error);
			return result;
			}
			/*------------------------------------------------------------------------*
			/
			/* ROT */
			static __inline__ void CUBLASAPI cublasSrot (int n, float *x, int incx, flo
			at *y, int incy,
			float sc, float ss)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasSrot_v2(handle, n, x, incx, y, incy, &sc,
			&ss);
			cublasSetError(error);
			}
			static __inline__ void CUBLASAPI cublasDrot (int n, double *x, int incx, do
			uble *y, int incy,
			double sc, double ss)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasDrot_v2(handle, n, x, incx, y, incy, &sc,
			&ss);
			cublasSetError(error);
			}
			static __inline__ void CUBLASAPI cublasCrot (int n, cuComplex *x, int incx,
			cuComplex *y,
			int incy, float c, cuComplex s)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasCrot_v2(handle, n, x, incx, y, incy, &c, &
			s);
			cublasSetError(error);
			}
			static __inline__ void CUBLASAPI cublasZrot (int n, cuDoubleComplex *x, int
			incx,
	cuDoubleComplex *y, int incy, double sc,		cuDoubleComplex *y, int incy, double sc,
	cuDoubleComplex cs);		cuDoubleComplex cs)
			{
	/*		cublasHandle_t handle = cublasGetCurrentCtx();
	* void		cublasStatus_t error = cublasZrot_v2(handle, n, x, incx, y, incy, &sc,
	* zdrot (int n, cuDoubleComplex *x, int incx, cuCumplex *y, int incy, doub		&cs);
	le c,		cublasSetError(error);
	* double s)		}
	*
	* multiplies a 2x2 matrix ( c s) with the 2xn matrix ( transpose(x) )
	* (-s c) ( transpose(y) )
	*
	* The elements of x are in x[lx + i * incx], i = 0 ... n - 1, where lx = 1
	if
	* incx >= 0, else lx = 1 + (1 - n) * incx, and similarly for y using ly an
	d
	* incy.
	*
	* Input
	* -----
	* n number of elements in input vectors
	* x double-precision complex vector with n elements
	* incx storage spacing between elements of x
	* y double-precision complex vector with n elements
	* incy storage spacing between elements of y
	* c cosine component of rotation matrix
	* s sine component of rotation matrix
	*
	* Output
	* ------
	* x rotated vector x (unchanged if n <= 0)
	* y rotated vector y (unchanged if n <= 0)
	*
	* Reference http://www.netlib.org/blas/zdrot.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZdrot (int n, cuDoubleComplex *x, int incx,
	cuDoubleComplex *y, int incy, double c, double
	s);
	/*
	* int
	* cublasIzamax (int n, const double *x, int incx)
	*
	* finds the smallest index of the element having maximum absolute value
	* in double-complex vector x; that is, the result is the first i, i = 0
	* to n - 1 that maximizes abs(real(x[1+i*incx]))+abs(imag(x[1 + i * incx])
	).
	*
	* Input
	* -----
	* n number of elements in input vector
	* x double-complex vector with n elements
	* incx storage spacing between elements of x
	*
	* Output
	* ------
	* returns the smallest index (0 if n <= 0 or incx <= 0)
	*
	* Reference: http://www.netlib.org/blas/izamax.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	int CUBLASAPI cublasIzamax (int n, const cuDoubleComplex *x, int incx);
	/*
	* int
	* cublasIzamin (int n, const cuDoubleComplex *x, int incx)
	*
	* finds the smallest index of the element having minimum absolute value
	* in double-complex vector x; that is, the result is the first i, i = 0
	* to n - 1 that minimizes abs(real(x[1+i*incx]))+abs(imag(x[1 + i * incx])
	).
	*
	* Input
	* -----
	* n number of elements in input vector
	* x double-complex vector with n elements
	* incx storage spacing between elements of x
	*
	* Output
	* ------
	* returns the smallest index (0 if n <= 0 or incx <= 0)
	*
	* Reference: Analogous to IZAMAX, see there.
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	int CUBLASAPI cublasIzamin (int n, const cuDoubleComplex *x, int incx);
	/*
	* double
	* cublasDzasum (int n, const cuDoubleComplex *x, int incx)
	*
	* takes the sum of the absolute values of a complex vector and returns a
	* double precision result. Note that this is not the L1 norm of the vector
	.
	* The result is the sum from 0 to n-1 of abs(real(x[ix+i*incx])) +
	* abs(imag(x(ix+i*incx))), where ix = 1 if incx <= 0, else ix = 1+(1-n)*in
	cx.
	*
	* Input
	* -----
	* n number of elements in input vector
	* x double-complex vector with n elements
	* incx storage spacing between elements of x
	*
	* Output
	* ------
	* returns the double precision sum of absolute values of real and imaginar
	y
	* parts (0 if n <= 0 or incx <= 0, or if an error occurs)
	*
	* Reference: http://www.netlib.org/blas/dzasum.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	double CUBLASAPI cublasDzasum (int n, const cuDoubleComplex *x, int incx);
	/* --------------- CUBLAS single precision BLAS2 functions ---------------
	- */
	/*
	* void
	* cublasSgbmv (char trans, int m, int n, int kl, int ku, float alpha,
	* const float *A, int lda, const float *x, int incx, float be
	ta,
	* float *y, int incy)
	*
	* performs one of the matrix-vector operations
	*
	* y = alpha*op(A)*x + beta*y, op(A)=A or op(A) = transpose(A)
	*
	* alpha and beta are single precision scalars. x and y are single precisio
	n
	* vectors. A is an m by n band matrix consisting of single precision eleme
	nts
	* with kl sub-diagonals and ku super-diagonals.
	*
	* Input
	* -----
	* trans specifies op(A). If trans == 'N' or 'n', op(A) = A. If trans == '
	T',
	* 't', 'C', or 'c', op(A) = transpose(A)
	* m specifies the number of rows of the matrix A. m must be at least
	* zero.
	* n specifies the number of columns of the matrix A. n must be at lea
	st
	* zero.
	* kl specifies the number of sub-diagonals of matrix A. It must be at
	* least zero.
	* ku specifies the number of super-diagonals of matrix A. It must be a
	t
	* least zero.
	* alpha single precision scalar multiplier applied to op(A).
	* A single precision array of dimensions (lda, n). The leading
	* (kl + ku + 1) x n part of the array A must contain the band matri
	x A,
	* supplied column by column, with the leading diagonal of the matri
	x
	* in row (ku + 1) of the array, the first super-diagonal starting a
	t
	* position 2 in row ku, the first sub-diagonal starting at position
	1
	* in row (ku + 2), and so on. Elements in the array A that do not
	* correspond to elements in the band matrix (such as the top left
	* ku x ku triangle) are not referenced.
	* lda leading dimension of A. lda must be at least (kl + ku + 1).
	* x single precision array of length at least (1+(n-1)*abs(incx)) whe
	n
	* trans == 'N' or 'n' and at least (1+(m-1)*abs(incx)) otherwise.
	* incx storage spacing between elements of x. incx must not be zero.
	* beta single precision scalar multiplier applied to vector y. If beta i
	s
	* zero, y is not read.
	* y single precision array of length at least (1+(m-1)*abs(incy)) whe
	n
	* trans == 'N' or 'n' and at least (1+(n-1)*abs(incy)) otherwise. I
	f
	* beta is zero, y is not read.
	* incy storage spacing between elements of y. incy must not be zero.
	*
	* Output
	* ------
	* y updated according to y = alpha*op(A)*x + beta*y
	*
	* Reference: http://www.netlib.org/blas/sgbmv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n, kl, or ku < 0; if incx or incy == 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasSgbmv (char trans, int m, int n, int kl, int ku,
	float alpha, const float *A, int lda,
	const float *x, int incx, float beta, float *y,
	int incy);
	/*
	* cublasSgemv (char trans, int m, int n, float alpha, const float *A, int
	lda,
	* const float *x, int incx, float beta, float *y, int incy)
	*
	* performs one of the matrix-vector operations
	*
	* y = alpha * op(A) * x + beta * y,
	*
	* where op(A) is one of
	*
	* op(A) = A or op(A) = transpose(A)
	*
	* where alpha and beta are single precision scalars, x and y are single
	* precision vectors, and A is an m x n matrix consisting of single precisi
	on
	* elements. Matrix A is stored in column major format, and lda is the lead
	ing
	* dimension of the two-dimensional array in which A is stored.
	*
	* Input
	* -----
	* trans specifies op(A). If transa = 'n' or 'N', op(A) = A. If trans =
	* trans = 't', 'T', 'c', or 'C', op(A) = transpose(A)
	* m specifies the number of rows of the matrix A. m must be at least
	* zero.
	* n specifies the number of columns of the matrix A. n must be at lea
	st
	* zero.
	* alpha single precision scalar multiplier applied to op(A).
	* A single precision array of dimensions (lda, n) if trans = 'n' or
	* 'N'), and of dimensions (lda, m) otherwise. lda must be at least
	* max(1, m) and at least max(1, n) otherwise.
	* lda leading dimension of two-dimensional array used to store matrix A
	* x single precision array of length at least (1 + (n - 1) * abs(incx
	))
	* when trans = 'N' or 'n' and at least (1 + (m - 1) * abs(incx))
	* otherwise.
	* incx specifies the storage spacing between elements of x. incx must no
	t
	* be zero.
	* beta single precision scalar multiplier applied to vector y. If beta
	* is zero, y is not read.
	* y single precision array of length at least (1 + (m - 1) * abs(incy
	))
	* when trans = 'N' or 'n' and at least (1 + (n - 1) * abs(incy))
	* otherwise.
	* incy specifies the storage spacing between elements of x. incx must no
	t
	* be zero.
	*
	* Output
	* ------
	* y updated according to alpha * op(A) * x + beta * y
	*
	* Reference: http://www.netlib.org/blas/sgemv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if m or n are < 0, or if incx or incy ==
	0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasSgemv (char trans, int m, int n, float alpha,
	const float *A, int lda, const float *x, int in
	cx,
	float beta, float *y, int incy);
	/*		static __inline__ void CUBLASAPI cublasCsrot (int n, cuComplex *x, int incx
	* cublasSger (int m, int n, float alpha, const float *x, int incx,		, cuComplex *y,
	* const float *y, int incy, float *A, int lda)		int incy, float c, float s)
	*		{
	* performs the symmetric rank 1 operation		cublasHandle_t handle = cublasGetCurrentCtx();
	*		cublasStatus_t error = cublasCsrot_v2(handle, n, x, incx, y, incy, &c,
	* A = alpha * x * transpose(y) + A,		&s);
	*		cublasSetError(error);
	* where alpha is a single precision scalar, x is an m element single		}
	* precision vector, y is an n element single precision vector, and A		static __inline__ void CUBLASAPI cublasZdrot (int n, cuDoubleComplex *x, in
	* is an m by n matrix consisting of single precision elements. Matrix A		t incx,
	* is stored in column major format, and lda is the leading dimension of		cuDoubleComplex *y, int incy, double c, double
	* the two-dimensional array used to store A.		s)
	*		{
	* Input		cublasHandle_t handle = cublasGetCurrentCtx();
	* -----		cublasStatus_t error = cublasZdrot_v2(handle, n, x, incx, y, incy, &c,
	* m specifies the number of rows of the matrix A. It must be at least		&s);
	* zero.		cublasSetError(error);
	* n specifies the number of columns of the matrix A. It must be at		}
	* least zero.		/*------------------------------------------------------------------------*
	* alpha single precision scalar multiplier applied to x * transpose(y)		/
	* x single precision array of length at least (1 + (m - 1) * abs(incx		/* ROTG */
	))		static __inline__ void CUBLASAPI cublasSrotg (float *sa, float *sb, float *
	* incx specifies the storage spacing between elements of x. incx must no		sc, float *ss)
	t		{
	* be zero.		cublasHandle_t handle = cublasGetCurrentCtx();
	* y single precision array of length at least (1 + (n - 1) * abs(incy		cublasStatus_t error = cublasSrotg_v2(handle, sa, sb, sc, ss);
	))		cublasSetError(error);
	* incy specifies the storage spacing between elements of y. incy must no		}
	t		static __inline__ void CUBLASAPI cublasDrotg (double *sa, double *sb, doubl
	* be zero.		e *sc, double *ss)
	* A single precision array of dimensions (lda, n).		{
	* lda leading dimension of two-dimensional array used to store matrix A		cublasHandle_t handle = cublasGetCurrentCtx();
	*		cublasStatus_t error = cublasDrotg_v2(handle, sa, sb, sc, ss);
	* Output		cublasSetError(error);
	* ------		}
	* A updated according to A = alpha * x * transpose(y) + A		static __inline__ void CUBLASAPI cublasCrotg (cuComplex *ca, cuComplex cb,
	*		float *sc,
	* Reference: http://www.netlib.org/blas/sger.f		cuComplex *cs)
	*		{
	* Error status for this function can be retrieved via cublasGetError().		cublasHandle_t handle = cublasGetCurrentCtx();
	*		cublasStatus_t error = cublasCrotg_v2(handle, ca, &cb, sc, cs);
	* Error Status		cublasSetError(error);
	* ------------		}
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize		static __inline__ void CUBLASAPI cublasZrotg (cuDoubleComplex *ca, cuDouble
	d		Complex cb, double *sc,
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, incx == 0, incy == 0		cuDoubleComplex *cs)
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU		{
	*/		cublasHandle_t handle = cublasGetCurrentCtx();
	void CUBLASAPI cublasSger (int m, int n, float alpha, const float *x, int i		cublasStatus_t error = cublasZrotg_v2(handle, ca, &cb, sc, cs);
	ncx,		cublasSetError(error);
	const float *y, int incy, float *A, int lda);		}
			/*------------------------------------------------------------------------*
			/
			/* ROTM */
			static __inline__ void CUBLASAPI cublasSrotm(int n, float *x, int incx, flo
			at *y, int incy,
			const float* sparam)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasSrotm_v2(handle, n, x, incx, y, incy, spar
			am);
			cublasSetError(error);
			}
			static __inline__ void CUBLASAPI cublasDrotm(int n, double *x, int incx, do
			uble *y, int incy,
			const double* sparam)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasDrotm_v2(handle, n, x, incx, y, incy, spar
			am);
			cublasSetError(error);
			}
			/*------------------------------------------------------------------------*
			/
			/* ROTMG */
			static __inline__ void CUBLASAPI cublasSrotmg (float *sd1, float *sd2, floa
			t *sx1,
			const float *sy1, float* sparam)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasSrotmg_v2(handle, sd1, sd2, sx1, sy1, spar
			am);
			cublasSetError(error);
			}
	/*		static __inline__ void CUBLASAPI cublasDrotmg (double *sd1, double *sd2, do
	* void		uble *sx1,
	* cublasSsbmv (char uplo, int n, int k, float alpha, const float *A, int l		const double *sy1, double* sparam)
	da,		{
	* const float *x, int incx, float beta, float *y, int incy)		cublasHandle_t handle = cublasGetCurrentCtx();
	*		cublasStatus_t error = cublasDrotmg_v2(handle, sd1, sd2, sx1, sy1, spar
	* performs the matrix-vector operation		am);
	*		cublasSetError(error);
	* y := alpha*A*x + beta*y		}
	*
	* alpha and beta are single precision scalars. x and y are single precisio
	n
	* vectors with n elements. A is an n x n symmetric band matrix consisting
	* of single precision elements, with k super-diagonals and the same number
	* of sub-diagonals.
	*
	* Input
	* -----
	* uplo specifies whether the upper or lower triangular part of the symme
	tric
	* band matrix A is being supplied. If uplo == 'U' or 'u', the upper
	* triangular part is being supplied. If uplo == 'L' or 'l', the low
	er
	* triangular part is being supplied.
	* n specifies the number of rows and the number of columns of the
	* symmetric matrix A. n must be at least zero.
	* k specifies the number of super-diagonals of matrix A. Since the ma
	trix
	* is symmetric, this is also the number of sub-diagonals. k must be
	at
	* least zero.
	* alpha single precision scalar multiplier applied to A*x.
	* A single precision array of dimensions (lda, n). When uplo == 'U' o
	r
	* 'u', the leading (k + 1) x n part of array A must contain the upp
	er
	* triangular band of the symmetric matrix, supplied column by colum
	n,
	* with the leading diagonal of the matrix in row (k+1) of the array
	,
	* the first super-diagonal starting at position 2 in row k, and so
	on.
	* The top left k x k triangle of the array A is not referenced. Whe
	n
	* uplo == 'L' or 'l', the leading (k + 1) x n part of the array A m
	ust
	* contain the lower triangular band part of the symmetric matrix,
	* supplied column by column, with the leading diagonal of the matri
	x in
	* row 1 of the array, the first sub-diagonal starting at position 1
	in
	* row 2, and so on. The bottom right k x k triangle of the array A
	is
	* not referenced.
	* lda leading dimension of A. lda must be at least (k + 1).
	* x single precision array of length at least (1 + (n - 1) * abs(incx
	)).
	* incx storage spacing between elements of x. incx must not be zero.
	* beta single precision scalar multiplier applied to vector y. If beta i
	s
	* zero, y is not read.
	* y single precision array of length at least (1 + (n - 1) * abs(incy
	)).
	* If beta is zero, y is not read.
	* incy storage spacing between elements of y. incy must not be zero.
	*
	* Output
	* ------
	* y updated according to alpha*A*x + beta*y
	*
	* Reference: http://www.netlib.org/blas/ssbmv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_INVALID_VALUE if k or n < 0, or if incx or incy == 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasSsbmv (char uplo, int n, int k, float alpha,		/* --------------- CUBLAS BLAS2 functions ---------------- */
			/* GEMV */
			static __inline__ void CUBLASAPI cublasSgemv (char trans, int m, int n, flo
			at alpha,
	const float *A, int lda, const float *x, int in cx,		const float *A, int lda, const float *x, int in cx,
	float beta, float *y, int incy);		float beta, float *y, int incy)
	/*		{
	* void		cublasHandle_t handle = cublasGetCurrentCtx();
	* cublasSspmv (char uplo, int n, float alpha, const float *AP, const float		cublasStatus_t error = cublasSgemv_v2(handle, convertToOp(trans), m,n,
	*x,		&alpha, A, lda, x, incx, &beta, y, incy
	* int incx, float beta, float *y, int incy)		);
	*		cublasSetError(error);
	* performs the matrix-vector operation		}
	*		static __inline__ void CUBLASAPI cublasDgemv (char trans, int m, int n, dou
	* y = alpha * A * x + beta * y		ble alpha,
	*		const double *A, int lda, const double *x, int
	* Alpha and beta are single precision scalars, and x and y are single		incx,
	* precision vectors with n elements. A is a symmetric n x n matrix		double beta, double *y, int incy)
	* consisting of single precision elements that is supplied in packed form.		{
	*		cublasHandle_t handle = cublasGetCurrentCtx();
	* Input		cublasStatus_t error = cublasDgemv_v2(handle, convertToOp(trans), m,n,
	* -----		&alpha, A, lda, x, incx, &beta, y, incy
	* uplo specifies whether the matrix data is stored in the upper or the l		);
	ower		cublasSetError(error);
	* triangular part of array AP. If uplo == 'U' or 'u', then the uppe		}
	r		static __inline__ void CUBLASAPI cublasCgemv (char trans, int m, int n, cuC
	* triangular part of A is supplied in AP. If uplo == 'L' or 'l', th		omplex alpha,
	en		const cuComplex *A, int lda, const cuComplex *x
	* the lower triangular part of A is supplied in AP.		, int incx,
	* n specifies the number of rows and columns of the matrix A. It must		cuComplex beta, cuComplex *y, int incy)
	be		{
	* at least zero.		cublasHandle_t handle = cublasGetCurrentCtx();
	* alpha single precision scalar multiplier applied to A*x.		cublasStatus_t error = cublasCgemv_v2(handle, convertToOp(trans), m,n,
	* AP single precision array with at least ((n * (n + 1)) / 2) elements		&alpha, A, lda, x, incx, &beta, y, incy
	. If		);
	* uplo == 'U' or 'u', the array AP contains the upper triangular pa		cublasSetError(error);
	rt		}
	* of the symmetric matrix A, packed sequentially, column by column;		static __inline__ void CUBLASAPI cublasZgemv (char trans, int m, int n, cuD
	* that is, if i <= j, then A[i,j] is stored is AP[i+(j*(j+1)/2)]. I		oubleComplex alpha,
	f		const cuDoubleComplex *A, int lda, const cuDoub
	* uplo == 'L' or 'L', the array AP contains the lower triangular pa		leComplex *x, int incx,
	rt		cuDoubleComplex beta, cuDoubleComplex *y, int i
	* of the symmetric matrix A, packed sequentially, column by column;		ncy)
	* that is, if i >= j, then A[i,j] is stored in AP[i+((2*n-j+1)*j)/2		{
	].		cublasHandle_t handle = cublasGetCurrentCtx();
	* x single precision array of length at least (1 + (n - 1) * abs(incx		cublasStatus_t error = cublasZgemv_v2(handle, convertToOp(trans), m,n,
	)).		&alpha, A, lda, x, incx, &beta, y, incy
	* incx storage spacing between elements of x. incx must not be zero.		);
	* beta single precision scalar multiplier applied to vector y;		cublasSetError(error);
	* y single precision array of length at least (1 + (n - 1) * abs(incy		}
	)).		/*------------------------------------------------------------------------*
	* If beta is zero, y is not read.		/
	* incy storage spacing between elements of y. incy must not be zero.		/* GBMV */
	*		static __inline__ void CUBLASAPI cublasSgbmv (char trans, int m, int n, int
	* Output		kl, int ku,
	* ------		float alpha, const float *A, int lda,
	* y updated according to y = alpha*A*x + beta*y
	*
	* Reference: http://www.netlib.org/blas/sspmv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, or if incx or incy == 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasSspmv (char uplo, int n, float alpha, const float *AP,
	const float *x, int incx, float beta, float *y,		const float *x, int incx, float beta, float *y,
	int incy);		int incy)
			{
	/*		cublasHandle_t handle = cublasGetCurrentCtx();
	* void		cublasStatus_t error = cublasSgbmv_v2(handle, convertToOp(trans), m,n,k
	* cublasSspr (char uplo, int n, float alpha, const float *x, int incx,		l,ku,
	* float *AP)		&alpha, A, lda, x, incx, &beta, y, incy
	*		);
	* performs the symmetric rank 1 operation		cublasSetError(error);
	*		}
	* A = alpha * x * transpose(x) + A,		static __inline__ void CUBLASAPI cublasDgbmv (char trans, int m, int n, int
	*		kl, int ku,
	* where alpha is a single precision scalar and x is an n element single		double alpha, const double *A, int lda,
	* precision vector. A is a symmetric n x n matrix consisting of single		const double *x, int incx, double beta, double
	* precision elements that is supplied in packed form.		*y,
	*		int incy)
	* Input		{
	* -----		cublasHandle_t handle = cublasGetCurrentCtx();
	* uplo specifies whether the matrix data is stored in the upper or the l		cublasStatus_t error = cublasDgbmv_v2(handle, convertToOp(trans), m,n,k
	ower		l,ku,
	* triangular part of array AP. If uplo == 'U' or 'u', then the uppe		&alpha, A, lda, x, incx, &beta, y, incy
	r		);
	* triangular part of A is supplied in AP. If uplo == 'L' or 'l', th		cublasSetError(error);
	en		}
	* the lower triangular part of A is supplied in AP.		static __inline__ void CUBLASAPI cublasCgbmv (char trans, int m, int n, int
	* n specifies the number of rows and columns of the matrix A. It must		kl, int ku,
	be		cuComplex alpha, const cuComplex *A, int lda,
	* at least zero.		const cuComplex *x, int incx, cuComplex beta, c
	* alpha single precision scalar multiplier applied to x * transpose(x).		uComplex *y,
	* x single precision array of length at least (1 + (n - 1) * abs(incx		int incy)
	)).		{
	* incx storage spacing between elements of x. incx must not be zero.		cublasHandle_t handle = cublasGetCurrentCtx();
	* AP single precision array with at least ((n * (n + 1)) / 2) elements		cublasStatus_t error = cublasCgbmv_v2(handle, convertToOp(trans), m,n,k
	. If		l,ku,
	* uplo == 'U' or 'u', the array AP contains the upper triangular pa		&alpha, A, lda, x, incx, &beta, y, incy
	rt		);
	* of the symmetric matrix A, packed sequentially, column by column;		cublasSetError(error);
	* that is, if i <= j, then A[i,j] is stored is AP[i+(j*(j+1)/2)]. I		}
	f		static __inline__ void CUBLASAPI cublasZgbmv (char trans, int m, int n, int
	* uplo == 'L' or 'L', the array AP contains the lower triangular pa		kl, int ku,
	rt		cuDoubleComplex alpha, const cuDoubleComplex *A
	* of the symmetric matrix A, packed sequentially, column by column;		, int lda,
	* that is, if i >= j, then A[i,j] is stored in AP[i+((2*n-j+1)*j)/2		const cuDoubleComplex *x, int incx, cuDoubleCom
	].		plex beta, cuDoubleComplex *y,
	*		int incy)
	* Output		{
	* ------		cublasHandle_t handle = cublasGetCurrentCtx();
	* A updated according to A = alpha * x * transpose(x) + A		cublasStatus_t error = cublasZgbmv_v2(handle, convertToOp(trans), m,n,k
	*		l,ku,
	* Reference: http://www.netlib.org/blas/sspr.f		&alpha, A, lda, x, incx, &beta, y, incy
	*		);
	* Error status for this function can be retrieved via cublasGetError().		cublasSetError(error);
	*		}
	* Error Status		/*------------------------------------------------------------------------*
	* ------------		/
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize		/* TRMV */
	d		static __inline__ void CUBLASAPI cublasStrmv (char uplo, char trans, char d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, or incx == 0		iag, int n,
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU		const float *A, int lda, float *x, int incx)
	*/		{
	void CUBLASAPI cublasSspr (char uplo, int n, float alpha, const float *x,		cublasHandle_t handle = cublasGetCurrentCtx();
	int incx, float *AP);		cublasStatus_t error = cublasStrmv_v2(handle, convertToFillMode(uplo),
			convertToOp(trans),
	/*		convertToDiagType(diag), n, A, lda, x, i
	* void		ncx );
	* cublasSspr2 (char uplo, int n, float alpha, const float *x, int incx,		cublasSetError(error);
	* const float *y, int incy, float *AP)		}
	*		static __inline__ void CUBLASAPI cublasDtrmv (char uplo, char trans, char d
	* performs the symmetric rank 2 operation		iag, int n,
	*		const double *A, int lda, double *x, int incx)
	* A = alpha*x*transpose(y) + alpha*y*transpose(x) + A,		{
	*		cublasHandle_t handle = cublasGetCurrentCtx();
	* where alpha is a single precision scalar, and x and y are n element sing		cublasStatus_t error = cublasDtrmv_v2(handle, convertToFillMode(uplo),
	le		convertToOp(trans),
	* precision vectors. A is a symmetric n x n matrix consisting of single		convertToDiagType(diag), n, A, lda, x, i
	* precision elements that is supplied in packed form.		ncx );
	*		cublasSetError(error);
	* Input		}
	* -----		static __inline__ void CUBLASAPI cublasCtrmv (char uplo, char trans, char d
	* uplo specifies whether the matrix data is stored in the upper or the l		iag, int n,
	ower		const cuComplex *A, int lda, cuComplex *x, int
	* triangular part of array A. If uplo == 'U' or 'u', then only the		incx)
	* upper triangular part of A may be referenced and the lower triang		{
	ular		cublasHandle_t handle = cublasGetCurrentCtx();
	* part of A is inferred. If uplo == 'L' or 'l', then only the lower		cublasStatus_t error = cublasCtrmv_v2(handle, convertToFillMode(uplo),
	* triangular part of A may be referenced and the upper triangular p		convertToOp(trans),
	art		convertToDiagType(diag), n, A, lda, x, i
	* of A is inferred.		ncx );
	* n specifies the number of rows and columns of the matrix A. It must		cublasSetError(error);
	be		}
	* at least zero.		static __inline__ void CUBLASAPI cublasZtrmv (char uplo, char trans, char d
	* alpha single precision scalar multiplier applied to x * transpose(y) +		iag, int n,
	* y * transpose(x).		const cuDoubleComplex *A, int lda, cuDoubleComp
	* x single precision array of length at least (1 + (n - 1) * abs (inc		lex *x, int incx)
	x)).		{
	* incx storage spacing between elements of x. incx must not be zero.		cublasHandle_t handle = cublasGetCurrentCtx();
	* y single precision array of length at least (1 + (n - 1) * abs (inc		cublasStatus_t error = cublasZtrmv_v2(handle, convertToFillMode(uplo),
	y)).		convertToOp(trans),
	* incy storage spacing between elements of y. incy must not be zero.		convertToDiagType(diag), n, A, lda, x, i
	* AP single precision array with at least ((n * (n + 1)) / 2) elements		ncx );
	. If		cublasSetError(error);
	* uplo == 'U' or 'u', the array AP contains the upper triangular pa		}
	rt		/*------------------------------------------------------------------------*
	* of the symmetric matrix A, packed sequentially, column by column;		/
	* that is, if i <= j, then A[i,j] is stored is AP[i+(j*(j+1)/2)]. I		/* TBMV */
	f		static __inline__ void CUBLASAPI cublasStbmv (char uplo, char trans, char d
	* uplo == 'L' or 'L', the array AP contains the lower triangular pa		iag, int n, int k,
	rt		const float *A, int lda, float *x, int incx)
	* of the symmetric matrix A, packed sequentially, column by column;		{
	* that is, if i >= j, then A[i,j] is stored in AP[i+((2*n-j+1)*j)/2		cublasHandle_t handle = cublasGetCurrentCtx();
	].		cublasStatus_t error = cublasStbmv_v2(handle, convertToFillMode(uplo),
	*		convertToOp(trans),
	* Output		convertToDiagType(diag), n, k, A, lda, x
	* ------		, incx );
	* A updated according to A = alpha*x*transpose(y)+alpha*y*transpose(x		cublasSetError(error);
	)+A		}
	*		static __inline__ void CUBLASAPI cublasDtbmv (char uplo, char trans, char d
	* Reference: http://www.netlib.org/blas/sspr2.f		iag, int n, int k,
	*		const double *A, int lda, double *x, int incx)
	* Error status for this function can be retrieved via cublasGetError().		{
	*		cublasHandle_t handle = cublasGetCurrentCtx();
	* Error Status		cublasStatus_t error = cublasDtbmv_v2(handle, convertToFillMode(uplo),
	* ------------		convertToOp(trans),
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize		convertToDiagType(diag), n, k, A, lda, x
	d		, incx );
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, incx == 0, incy == 0		cublasSetError(error);
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU		}
	*/		static __inline__ void CUBLASAPI cublasCtbmv (char uplo, char trans, char d
	void CUBLASAPI cublasSspr2 (char uplo, int n, float alpha, const float *x,		iag, int n, int k,
	int incx, const float *y, int incy, float *AP);		const cuComplex *A, int lda, cuComplex *x, int
			incx)
	/*		{
	* void		cublasHandle_t handle = cublasGetCurrentCtx();
	* cublasSsymv (char uplo, int n, float alpha, const float *A, int lda,		cublasStatus_t error = cublasCtbmv_v2(handle, convertToFillMode(uplo),
	* const float *x, int incx, float beta, float *y, int incy)		convertToOp(trans),
	*		convertToDiagType(diag), n, k, A, lda, x
	* performs the matrix-vector operation		, incx );
	*		cublasSetError(error);
	* y = alpha*A*x + beta*y		}
	*		static __inline__ void CUBLASAPI cublasZtbmv (char uplo, char trans, char d
	* Alpha and beta are single precision scalars, and x and y are single		iag, int n, int k,
	* precision vectors, each with n elements. A is a symmetric n x n matrix		const cuDoubleComplex *A, int lda, cuDoubleComp
	* consisting of single precision elements that is stored in either upper o		lex *x, int incx)
	r		{
	* lower storage mode.		cublasHandle_t handle = cublasGetCurrentCtx();
	*		cublasStatus_t error = cublasZtbmv_v2(handle, convertToFillMode(uplo),
	* Input		convertToOp(trans),
	* -----		convertToDiagType(diag), n, k, A, lda, x
	* uplo specifies whether the upper or lower triangular part of the array		, incx );
	A		cublasSetError(error);
	* is to be referenced. If uplo == 'U' or 'u', the symmetric matrix		}
	A		/*------------------------------------------------------------------------*
	* is stored in upper storage mode, i.e. only the upper triangular p		/
	art		/* TPMV */
	* of A is to be referenced while the lower triangular part of A is		static __inline__ void CUBLASAPI cublasStpmv(char uplo, char trans, char di
	to		ag, int n, const float *AP, float *x, int incx)
	* be inferred. If uplo == 'L' or 'l', the symmetric matrix A is sto		{
	red		cublasHandle_t handle = cublasGetCurrentCtx();
	* in lower storage mode, i.e. only the lower triangular part of A i		cublasStatus_t error = cublasStpmv_v2(handle, convertToFillMode(uplo),
	s		convertToOp(trans),
	* to be referenced while the upper triangular part of A is to be		convertToDiagType(diag), n, AP, x, incx
	* inferred.		);
	* n specifies the number of rows and the number of columns of the		cublasSetError(error);
	* symmetric matrix A. n must be at least zero.		}
	* alpha single precision scalar multiplier applied to A*x.
	* A single precision array of dimensions (lda, n). If uplo == 'U' or
	'u',
	* the leading n x n upper triangular part of the array A must conta
	in
	* the upper triangular part of the symmetric matrix and the strictl
	y
	* lower triangular part of A is not referenced. If uplo == 'L' or '
	l',
	* the leading n x n lower triangular part of the array A must conta
	in
	* the lower triangular part of the symmetric matrix and the strictl
	y
	* upper triangular part of A is not referenced.
	* lda leading dimension of A. It must be at least max (1, n).
	* x single precision array of length at least (1 + (n - 1) * abs(incx
	)).
	* incx storage spacing between elements of x. incx must not be zero.
	* beta single precision scalar multiplier applied to vector y.
	* y single precision array of length at least (1 + (n - 1) * abs(incy
	)).
	* If beta is zero, y is not read.
	* incy storage spacing between elements of y. incy must not be zero.
	*
	* Output
	* ------
	* y updated according to y = alpha*A*x + beta*y
	*
	* Reference: http://www.netlib.org/blas/ssymv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, or if incx or incy == 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasSsymv (char uplo, int n, float alpha, const float *A,
	int lda, const float *x, int incx, float beta,
	float *y, int incy);
	/*
	* void
	* cublasSsyr (char uplo, int n, float alpha, const float *x, int incx,
	* float *A, int lda)
	*
	* performs the symmetric rank 1 operation
	*
	* A = alpha * x * transpose(x) + A,
	*
	* where alpha is a single precision scalar, x is an n element single
	* precision vector and A is an n x n symmetric matrix consisting of
	* single precision elements. Matrix A is stored in column major format,
	* and lda is the leading dimension of the two-dimensional array
	* containing A.
	*
	* Input
	* -----
	* uplo specifies whether the matrix data is stored in the upper or
	* the lower triangular part of array A. If uplo = 'U' or 'u',
	* then only the upper triangular part of A may be referenced.
	* If uplo = 'L' or 'l', then only the lower triangular part of
	* A may be referenced.
	* n specifies the number of rows and columns of the matrix A. It
	* must be at least 0.
	* alpha single precision scalar multiplier applied to x * transpose(x)
	* x single precision array of length at least (1 + (n - 1) * abs(incx
	))
	* incx specifies the storage spacing between elements of x. incx must
	* not be zero.
	* A single precision array of dimensions (lda, n). If uplo = 'U' or
	* 'u', then A must contain the upper triangular part of a symmetric
	* matrix, and the strictly lower triangular part is not referenced.
	* If uplo = 'L' or 'l', then A contains the lower triangular part
	* of a symmetric matrix, and the strictly upper triangular part is
	* not referenced.
	* lda leading dimension of the two-dimensional array containing A. lda
	* must be at least max(1, n).
	*
	* Output
	* ------
	* A updated according to A = alpha * x * transpose(x) + A
	*
	* Reference: http://www.netlib.org/blas/ssyr.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, or incx == 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasSsyr (char uplo, int n, float alpha, const float *x,
	int incx, float *A, int lda);
	/*
	* void
	* cublasSsyr2 (char uplo, int n, float alpha, const float *x, int incx,
	* const float *y, int incy, float *A, int lda)
	*
	* performs the symmetric rank 2 operation
	*
	* A = alpha*x*transpose(y) + alpha*y*transpose(x) + A,
	*
	* where alpha is a single precision scalar, x and y are n element single
	* precision vector and A is an n by n symmetric matrix consisting of singl
	e
	* precision elements.
	*
	* Input
	* -----
	* uplo specifies whether the matrix data is stored in the upper or the l
	ower
	* triangular part of array A. If uplo == 'U' or 'u', then only the
	* upper triangular part of A may be referenced and the lower triang
	ular
	* part of A is inferred. If uplo == 'L' or 'l', then only the lower
	* triangular part of A may be referenced and the upper triangular p
	art
	* of A is inferred.
	* n specifies the number of rows and columns of the matrix A. It must
	be
	* at least zero.
	* alpha single precision scalar multiplier applied to x * transpose(y) +
	* y * transpose(x).
	* x single precision array of length at least (1 + (n - 1) * abs (inc
	x)).
	* incx storage spacing between elements of x. incx must not be zero.
	* y single precision array of length at least (1 + (n - 1) * abs (inc
	y)).
	* incy storage spacing between elements of y. incy must not be zero.
	* A single precision array of dimensions (lda, n). If uplo == 'U' or
	'u',
	* then A must contains the upper triangular part of a symmetric mat
	rix,
	* and the strictly lower triangular parts is not referenced. If upl
	o ==
	* 'L' or 'l', then A contains the lower triangular part of a symmet
	ric
	* matrix, and the strictly upper triangular part is not referenced.
	* lda leading dimension of A. It must be at least max(1, n).
	*
	* Output
	* ------
	* A updated according to A = alpha*x*transpose(y)+alpha*y*transpose(x
	)+A
	*
	* Reference: http://www.netlib.org/blas/ssyr2.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, incx == 0, incy == 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasSsyr2 (char uplo, int n, float alpha, const float *x,
	int incx, const float *y, int incy, float *A,
	int lda);
	/*
	* void
	* cublasStbmv (char uplo, char trans, char diag, int n, int k, const float
	*A,
	* int lda, float *x, int incx)
	*
	* performs one of the matrix-vector operations x = op(A) * x, where op(A)
	= A
	* or op(A) = transpose(A). x is an n-element single precision vector, and
	A is
	* an n x n, unit or non-unit upper or lower triangular band matrix consist
	ing
	* of single precision elements.
	*
	* Input
	* -----
	* uplo specifies whether the matrix A is an upper or lower triangular ba
	nd
	* matrix. If uplo == 'U' or 'u', A is an upper triangular band matr
	ix.
	* If uplo == 'L' or 'l', A is a lower triangular band matrix.
	* trans specifies op(A). If transa == 'N' or 'n', op(A) = A. If trans ==
	'T',
	* 't', 'C', or 'c', op(A) = transpose(A).
	* diag specifies whether or not matrix A is unit triangular. If diag ==
	'U'
	* or 'u', A is assumed to be unit triangular. If diag == 'N' or 'n'
	, A
	* is not assumed to be unit triangular.
	* n specifies the number of rows and columns of the matrix A. n must
	be
	* at least zero. In the current implementation n must not exceed 40
	70.
	* k specifies the number of super- or sub-diagonals. If uplo == 'U' o
	r
	* 'u', k specifies the number of super-diagonals. If uplo == 'L' or
	* 'l', k specifies the number of sub-diagonals. k must at least be
	* zero.
	* A single precision array of dimension (lda, n). If uplo == 'U' or '
	u',
	* the leading (k + 1) x n part of the array A must contain the uppe
	r
	* triangular band matrix, supplied column by column, with the leadi
	ng
	* diagonal of the matrix in row (k + 1) of the array, the first
	* super-diagonal starting at position 2 in row k, and so on. The to
	p
	* left k x k triangle of the array A is not referenced. If uplo ==
	'L'
	* or 'l', the leading (k + 1) x n part of the array A must constain
	the
	* lower triangular band matrix, supplied column by column, with the
	* leading diagonal of the matrix in row 1 of the array, the first
	* sub-diagonal startingat position 1 in row 2, and so on. The botto
	m
	* right k x k triangle of the array is not referenced.
	* lda is the leading dimension of A. It must be at least (k + 1).
	* x single precision array of length at least (1 + (n - 1) * abs(incx
	)).
	* On entry, x contains the source vector. On exit, x is overwritten
	* with the result vector.
	* incx specifies the storage spacing for elements of x. incx must not be
	* zero.
	*
	* Output
	* ------
	* x updated according to x = op(A) * x
	*
	* Reference: http://www.netlib.org/blas/stbmv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, k < 0, or incx == 0
	* CUBLAS_STATUS_ALLOC_FAILED if function cannot allocate enough intern
	al scratch vector memory
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasStbmv (char uplo, char trans, char diag, int n, int k,
	const float *A, int lda, float *x, int incx);
	/*		static __inline__ void CUBLASAPI cublasDtpmv(char uplo, char trans, char di
	* void cublasStbsv (char uplo, char trans, char diag, int n, int k,		ag, int n, const double *AP, double *x, int incx)
	* const float *A, int lda, float *X, int incx)		{
	*		cublasHandle_t handle = cublasGetCurrentCtx();
	* solves one of the systems of equations op(A)*x = b, where op(A) is eithe		cublasStatus_t error = cublasDtpmv_v2(handle, convertToFillMode(uplo),
	r		convertToOp(trans),
	* op(A) = A or op(A) = transpose(A). b and x are n-element vectors, and A		convertToDiagType(diag), n, AP, x, incx
	is		);
	* an n x n unit or non-unit, upper or lower triangular band matrix with k		cublasSetError(error);
	+ 1
	* diagonals. No test for singularity or near-singularity is included in th
	is
	* function. Such tests must be performed before calling this function.
	*
	* Input
	* -----
	* uplo specifies whether the matrix is an upper or lower triangular band
	* matrix as follows: If uplo == 'U' or 'u', A is an upper triangula
	r
	* band matrix. If uplo == 'L' or 'l', A is a lower triangular band
	* matrix.
	* trans specifies op(A). If trans == 'N' or 'n', op(A) = A. If trans == '
	T',
	* 't', 'C', or 'c', op(A) = transpose(A).
	* diag specifies whether A is unit triangular. If diag == 'U' or 'u', A
	is
	* assumed to be unit triangular; thas is, diagonal elements are not
	* read and are assumed to be unity. If diag == 'N' or 'n', A is not
	* assumed to be unit triangular.
	* n specifies the number of rows and columns of the matrix A. n must
	be
	* at least zero.
	* k specifies the number of super- or sub-diagonals. If uplo == 'U' o
	r
	* 'u', k specifies the number of super-diagonals. If uplo == 'L' or
	* 'l', k specifies the number of sub-diagonals. k must be at least
	* zero.
	* A single precision array of dimension (lda, n). If uplo == 'U' or '
	u',
	* the leading (k + 1) x n part of the array A must contain the uppe
	r
	* triangular band matrix, supplied column by column, with the leadi
	ng
	* diagonal of the matrix in row (k + 1) of the array, the first sup
	er-
	* diagonal starting at position 2 in row k, and so on. The top left
	* k x k triangle of the array A is not referenced. If uplo == 'L' o
	r
	* 'l', the leading (k + 1) x n part of the array A must constain th
	e
	* lower triangular band matrix, supplied column by column, with the
	* leading diagonal of the matrix in row 1 of the array, the first
	* sub-diagonal starting at position 1 in row 2, and so on. The bott
	om
	* right k x k triangle of the array is not referenced.
	* x single precision array of length at least (1 + (n - 1) * abs(incx
	)).
	* On entry, x contains the n-element right-hand side vector b. On e
	xit,
	* it is overwritten with the solution vector x.
	* incx storage spacing between elements of x. incx must not be zero.
	*
	* Output
	* ------
	* x updated to contain the solution vector x that solves op(A) * x =
	b.
	*
	* Reference: http://www.netlib.org/blas/stbsv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if incx == 0, n < 0 or n > 4070
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasStbsv (char uplo, char trans, char diag, int n, int k,
	const float *A, int lda, float *x, int incx);
	/*		}
	* void
	* cublasStpmv (char uplo, char trans, char diag, int n, const float *AP,
	* float *x, int incx);
	*
	* performs one of the matrix-vector operations x = op(A) * x, where op(A)
	= A,
	* or op(A) = transpose(A). x is an n element single precision vector, and
	A
	* is an n x n, unit or non-unit, upper or lower triangular matrix composed
	* of single precision elements.
	*
	* Input
	* -----
	* uplo specifies whether the matrix A is an upper or lower triangular
	* matrix. If uplo == 'U' or 'u', then A is an upper triangular matr
	ix.
	* If uplo == 'L' or 'l', then A is a lower triangular matrix.
	* trans specifies op(A). If transa == 'N' or 'n', op(A) = A. If trans ==
	'T',
	* 't', 'C', or 'c', op(A) = transpose(A)
	* diag specifies whether or not matrix A is unit triangular. If diag ==
	'U'
	* or 'u', A is assumed to be unit triangular. If diag == 'N' or 'n'
	, A
	* is not assumed to be unit triangular.
	* n specifies the number of rows and columns of the matrix A. n must
	be
	* at least zero.
	* AP single precision array with at least ((n * (n + 1)) / 2) elements
	. If
	* uplo == 'U' or 'u', the array AP contains the upper triangular pa
	rt
	* of the symmetric matrix A, packed sequentially, column by column;
	* that is, if i <= j, then A[i,j] is stored in AP[i+(j*(j+1)/2)]. I
	f
	* uplo == 'L' or 'L', the array AP contains the lower triangular pa
	rt
	* of the symmetric matrix A, packed sequentially, column by column;
	* that is, if i >= j, then A[i,j] is stored in AP[i+((2*n-j+1)*j)/2
	].
	* x single precision array of length at least (1 + (n - 1) * abs(incx
	)).
	* On entry, x contains the source vector. On exit, x is overwritten
	* with the result vector.
	* incx specifies the storage spacing for elements of x. incx must not be
	* zero.
	*
	* Output
	* ------
	* x updated according to x = op(A) * x,
	*
	* Reference: http://www.netlib.org/blas/stpmv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if incx == 0 or if n < 0
	* CUBLAS_STATUS_ALLOC_FAILED if function cannot allocate enough intern
	al scratch vector memory
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasStpmv (char uplo, char trans, char diag, int n,
	const float *AP, float *x, int incx);
	/*		static __inline__ void CUBLASAPI cublasCtpmv(char uplo, char trans, char di
	* void		ag, int n, const cuComplex *AP, cuComplex *x, int incx)
	* cublasStpsv (char uplo, char trans, char diag, int n, const float *AP,		{
	* float *X, int incx)		cublasHandle_t handle = cublasGetCurrentCtx();
	*		cublasStatus_t error = cublasCtpmv_v2(handle, convertToFillMode(uplo),
	* solves one of the systems of equations op(A)*x = b, where op(A) is eithe		convertToOp(trans),
	r		convertToDiagType(diag), n, AP, x, incx
	* op(A) = A or op(A) = transpose(A). b and x are n element vectors, and A		);
	is		cublasSetError(error);
	* an n x n unit or non-unit, upper or lower triangular matrix. No test for
	* singularity or near-singularity is included in this function. Such tests
	* must be performed before calling this function.
	*
	* Input
	* -----
	* uplo specifies whether the matrix is an upper or lower triangular matr
	ix
	* as follows: If uplo == 'U' or 'u', A is an upper triangluar matri
	x.
	* If uplo == 'L' or 'l', A is a lower triangular matrix.
	* trans specifies op(A). If trans == 'N' or 'n', op(A) = A. If trans == '
	T',
	* 't', 'C', or 'c', op(A) = transpose(A).
	* diag specifies whether A is unit triangular. If diag == 'U' or 'u', A
	is
	* assumed to be unit triangular; thas is, diagonal elements are not
	* read and are assumed to be unity. If diag == 'N' or 'n', A is not
	* assumed to be unit triangular.
	* n specifies the number of rows and columns of the matrix A. n must
	be
	* at least zero. In the current implementation n must not exceed 40
	70.
	* AP single precision array with at least ((n*(n+1))/2) elements. If u
	plo
	* == 'U' or 'u', the array AP contains the upper triangular matrix
	A,
	* packed sequentially, column by column; that is, if i <= j, then
	* A[i,j] is stored is AP[i+(j*(j+1)/2)]. If uplo == 'L' or 'L', the
	* array AP contains the lower triangular matrix A, packed sequentia
	lly,
	* column by column; that is, if i >= j, then A[i,j] is stored in
	* AP[i+((2*n-j+1)*j)/2]. When diag = 'U' or 'u', the diagonal eleme
	nts
	* of A are not referenced and are assumed to be unity.
	* x single precision array of length at least (1 + (n - 1) * abs(incx
	)).
	* On entry, x contains the n-element right-hand side vector b. On e
	xit,
	* it is overwritten with the solution vector x.
	* incx storage spacing between elements of x. It must not be zero.
	*
	* Output
	* ------
	* x updated to contain the solution vector x that solves op(A) * x =
	b.
	*
	* Reference: http://www.netlib.org/blas/stpsv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if incx == 0, n < 0, or n > 4070
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasStpsv (char uplo, char trans, char diag, int n,
	const float *AP, float *x, int incx);
	/*		}
	* void
	* cublasStrmv (char uplo, char trans, char diag, int n, const float *A,
	* int lda, float *x, int incx);
	*
	* performs one of the matrix-vector operations x = op(A) * x, where op(A)
	=
	= A, or op(A) = transpose(A). x is an n-element single precision vector, a
	nd
	* A is an n x n, unit or non-unit, upper or lower, triangular matrix compo
	sed
	* of single precision elements.
	*
	* Input
	* -----
	* uplo specifies whether the matrix A is an upper or lower triangular
	* matrix. If uplo = 'U' or 'u', then A is an upper triangular matri
	x.
	* If uplo = 'L' or 'l', then A is a lower triangular matrix.
	* trans specifies op(A). If transa = 'N' or 'n', op(A) = A. If trans = 'T
	',
	* 't', 'C', or 'c', op(A) = transpose(A)
	* diag specifies whether or not matrix A is unit triangular. If diag = '
	U'
	* or 'u', A is assumed to be unit triangular. If diag = 'N' or 'n',
	A
	* is not assumed to be unit triangular.
	* n specifies the number of rows and columns of the matrix A. n must
	be
	* at least zero.
	* A single precision array of dimension (lda, n). If uplo = 'U' or 'u
	',
	* the leading n x n upper triangular part of the array A must conta
	in
	* the upper triangular matrix and the strictly lower triangular par
	t
	* of A is not referenced. If uplo = 'L' or 'l', the leading n x n l
	ower
	* triangular part of the array A must contain the lower triangular
	* matrix and the strictly upper triangular part of A is not referen
	ced.
	* When diag = 'U' or 'u', the diagonal elements of A are not refere
	nced
	* either, but are are assumed to be unity.
	* lda is the leading dimension of A. It must be at least max (1, n).
	* x single precision array of length at least (1 + (n - 1) * abs(incx
	) ).
	* On entry, x contains the source vector. On exit, x is overwritten
	* with the result vector.
	* incx specifies the storage spacing for elements of x. incx must not be
	* zero.
	*
	* Output
	* ------
	* x updated according to x = op(A) * x,
	*
	* Reference: http://www.netlib.org/blas/strmv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if incx == 0 or if n < 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasStrmv (char uplo, char trans, char diag, int n,
	const float *A, int lda, float *x, int incx);
	/*		static __inline__ void CUBLASAPI cublasZtpmv(char uplo, char trans, char di
	* void		ag, int n, const cuDoubleComplex *AP, cuDoubleComplex *x, int incx)
	* cublasStrsv (char uplo, char trans, char diag, int n, const float *A,		{
	* int lda, float *x, int incx)		cublasHandle_t handle = cublasGetCurrentCtx();
	*		cublasStatus_t error = cublasZtpmv_v2(handle, convertToFillMode(uplo),
	* solves a system of equations op(A) * x = b, where op(A) is either A or		convertToOp(trans),
	* transpose(A). b and x are single precision vectors consisting of n		convertToDiagType(diag), n, AP, x, incx
	* elements, and A is an n x n matrix composed of a unit or non-unit, upper		);
	* or lower triangular matrix. Matrix A is stored in column major format,		cublasSetError(error);
	* and lda is the leading dimension of the two-dimensional array containing
	* A.
	*
	* No test for singularity or near-singularity is included in this function
	.
	* Such tests must be performed before calling this function.
	*
	* Input
	* -----
	* uplo specifies whether the matrix data is stored in the upper or the
	* lower triangular part of array A. If uplo = 'U' or 'u', then only
	* the upper triangular part of A may be referenced. If uplo = 'L' o
	r
	* 'l', then only the lower triangular part of A may be referenced.
	* trans specifies op(A). If transa = 'n' or 'N', op(A) = A. If transa = '
	t',
	* 'T', 'c', or 'C', op(A) = transpose(A)
	* diag specifies whether or not A is a unit triangular matrix like so:
	* if diag = 'U' or 'u', A is assumed to be unit triangular. If
	* diag = 'N' or 'n', then A is not assumed to be unit triangular.
	* n specifies the number of rows and columns of the matrix A. It
	* must be at least 0.
	* A is a single precision array of dimensions (lda, n). If uplo = 'U'
	* or 'u', then A must contains the upper triangular part of a symme
	tric
	* matrix, and the strictly lower triangular parts is not referenced
	.
	* If uplo = 'L' or 'l', then A contains the lower triangular part o
	f
	* a symmetric matrix, and the strictly upper triangular part is not
	* referenced.
	* lda is the leading dimension of the two-dimensional array containing
	A.
	* lda must be at least max(1, n).
	* x single precision array of length at least (1 + (n - 1) * abs(incx
	)).
	* On entry, x contains the n element right-hand side vector b. On e
	xit,
	* it is overwritten with the solution vector x.
	* incx specifies the storage spacing between elements of x. incx must no
	t
	* be zero.
	*
	* Output
	* ------
	* x updated to contain the solution vector x that solves op(A) * x =
	b.
	*
	* Reference: http://www.netlib.org/blas/strsv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if incx == 0 or if n < 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasStrsv (char uplo, char trans, char diag, int n,
	const float *A, int lda, float *x, int incx);
	/* ----------------- CUBLAS double complex BLAS2 functions ----------------		}
	- */		/*------------------------------------------------------------------------*
			/
			/* TRSV */
			static __inline__ void CUBLASAPI cublasStrsv(char uplo, char trans, char di
			ag, int n, const float *A, int lda, float *x, int incx)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasStrsv_v2(handle, convertToFillMode(uplo),
			convertToOp(trans),
			convertToDiagType(diag), n, A, lda, x, i
			ncx );
			cublasSetError(error);
			}
	/*		static __inline__ void CUBLASAPI cublasDtrsv(char uplo, char trans, char di
	* void		ag, int n, const double *A, int lda, double *x, int incx)
	* cublasZtrmv (char uplo, char trans, char diag, int n, const cuDoubleComp		{
	lex *A,		cublasHandle_t handle = cublasGetCurrentCtx();
	* int lda, cuDoubleComplex *x, int incx);		cublasStatus_t error = cublasDtrsv_v2(handle, convertToFillMode(uplo),
	*		convertToOp(trans),
	* performs one of the matrix-vector operations x = op(A) * x,		convertToDiagType(diag), n, A, lda, x, i
	* where op(A) = A, or op(A) = transpose(A) or op(A) = conjugate(transpose(		ncx );
	A)).		cublasSetError(error);
	* x is an n-element double precision complex vector, and		}
	* A is an n x n, unit or non-unit, upper or lower, triangular matrix compo
	sed
	* of double precision complex elements.
	*
	* Input
	* -----
	* uplo specifies whether the matrix A is an upper or lower triangular
	* matrix. If uplo = 'U' or 'u', then A is an upper triangular matri
	x.
	* If uplo = 'L' or 'l', then A is a lower triangular matrix.
	* trans specifies op(A). If trans = 'n' or 'N', op(A) = A. If trans = 't'
	or
	* 'T', op(A) = transpose(A). If trans = 'c' or 'C', op(A) =
	* conjugate(transpose(A)).
	* diag specifies whether or not matrix A is unit triangular. If diag = '
	U'
	* or 'u', A is assumed to be unit triangular. If diag = 'N' or 'n',
	A
	* is not assumed to be unit triangular.
	* n specifies the number of rows and columns of the matrix A. n must
	be
	* at least zero.
	* A double precision array of dimension (lda, n). If uplo = 'U' or 'u
	',
	* the leading n x n upper triangular part of the array A must conta
	in
	* the upper triangular matrix and the strictly lower triangular par
	t
	* of A is not referenced. If uplo = 'L' or 'l', the leading n x n l
	ower
	* triangular part of the array A must contain the lower triangular
	* matrix and the strictly upper triangular part of A is not referen
	ced.
	* When diag = 'U' or 'u', the diagonal elements of A are not refere
	nced
	* either, but are are assumed to be unity.
	* lda is the leading dimension of A. It must be at least max (1, n).
	* x double precision array of length at least (1 + (n - 1) * abs(incx
	) ).
	* On entry, x contains the source vector. On exit, x is overwritten
	* with the result vector.
	* incx specifies the storage spacing for elements of x. incx must not be
	* zero.
	*
	* Output
	* ------
	* x updated according to x = op(A) * x,
	*
	* Reference: http://www.netlib.org/blas/ztrmv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if incx == 0 or if n < 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZtrmv (char uplo, char trans, char diag, int n,
	const cuDoubleComplex *A, int lda, cuDoubleComp
	lex *x,
	int incx);
	/*		static __inline__ void CUBLASAPI cublasCtrsv(char uplo, char trans, char di
	* void		ag, int n, const cuComplex *A, int lda, cuComplex *x, int incx)
	* cublasZgbmv (char trans, int m, int n, int kl, int ku, cuDoubleComplex a		{
	lpha,		cublasHandle_t handle = cublasGetCurrentCtx();
	* const cuDoubleComplex *A, int lda, const cuDoubleComplex *x		cublasStatus_t error = cublasCtrsv_v2(handle, convertToFillMode(uplo),
	, int incx, cuDoubleComplex beta,		convertToOp(trans),
	* cuDoubleComplex *y, int incy);		convertToDiagType(diag), n, A, lda, x, i
	*		ncx );
	* performs one of the matrix-vector operations		cublasSetError(error);
	*		}
	* y = alpha*op(A)*x + beta*y, op(A)=A or op(A) = transpose(A)
	*
	* alpha and beta are double precision complex scalars. x and y are double
	precision
	* complex vectors. A is an m by n band matrix consisting of double precisi
	on complex elements
	* with kl sub-diagonals and ku super-diagonals.
	*
	* Input
	* -----
	* trans specifies op(A). If trans == 'N' or 'n', op(A) = A. If trans == '
	T',
	* or 't', op(A) = transpose(A). If trans == 'C' or 'c',
	* op(A) = conjugate(transpose(A)).
	* m specifies the number of rows of the matrix A. m must be at least
	* zero.
	* n specifies the number of columns of the matrix A. n must be at lea
	st
	* zero.
	* kl specifies the number of sub-diagonals of matrix A. It must be at
	* least zero.
	* ku specifies the number of super-diagonals of matrix A. It must be a
	t
	* least zero.
	* alpha double precision complex scalar multiplier applied to op(A).
	* A double precision complex array of dimensions (lda, n). The leadin
	g
	* (kl + ku + 1) x n part of the array A must contain the band matri
	x A,
	* supplied column by column, with the leading diagonal of the matri
	x
	* in row (ku + 1) of the array, the first super-diagonal starting a
	t
	* position 2 in row ku, the first sub-diagonal starting at position
	1
	* in row (ku + 2), and so on. Elements in the array A that do not
	* correspond to elements in the band matrix (such as the top left
	* ku x ku triangle) are not referenced.
	* lda leading dimension of A. lda must be at least (kl + ku + 1).
	* x double precision complex array of length at least (1+(n-1)*abs(in
	cx)) when
	* trans == 'N' or 'n' and at least (1+(m-1)*abs(incx)) otherwise.
	* incx specifies the increment for the elements of x. incx must not be z
	ero.
	* beta double precision complex scalar multiplier applied to vector y. I
	f beta is
	* zero, y is not read.
	* y double precision complex array of length at least (1+(m-1)*abs(in
	cy)) when
	* trans == 'N' or 'n' and at least (1+(n-1)*abs(incy)) otherwise. I
	f
	* beta is zero, y is not read.
	* incy On entry, incy specifies the increment for the elements of y. inc
	y
	* must not be zero.
	*
	* Output
	* ------
	* y updated according to y = alpha*op(A)*x + beta*y
	*
	* Reference: http://www.netlib.org/blas/zgbmv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, or if incx or incy == 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZgbmv (char trans, int m, int n, int kl, int ku,
	cuDoubleComplex alpha, const cuDoubleComplex *A
	, int lda,
	const cuDoubleComplex *x, int incx, cuDoubleCom
	plex beta,
	cuDoubleComplex *y, int incy);
	/*		static __inline__ void CUBLASAPI cublasZtrsv(char uplo, char trans, char di
	* void		ag, int n, const cuDoubleComplex *A, int lda,
	* cublasZtbmv (char uplo, char trans, char diag, int n, int k, const cuDou		cuDoubleComplex *x, int incx)
	bleComplex *A,		{
	* int lda, cuDoubleComplex *x, int incx)		cublasHandle_t handle = cublasGetCurrentCtx();
	*		cublasStatus_t error = cublasZtrsv_v2(handle, convertToFillMode(uplo),
	* performs one of the matrix-vector operations x = op(A) * x, where op(A)		convertToOp(trans),
	= A,		convertToDiagType(diag), n, A, lda, x, i
	* op(A) = transpose(A) or op(A) = conjugate(transpose(A)). x is an n-eleme		ncx );
	nt		cublasSetError(error);
	* double precision complex vector, and A is an n x n, unit or non-unit, up		}
	per		/*------------------------------------------------------------------------*
	* or lower triangular band matrix composed of double precision complex ele		/
	ments.		/* TPSV */
	*		static __inline__ void CUBLASAPI cublasStpsv(char uplo, char trans, char di
	* Input		ag, int n, const float *AP,
	* -----		float *x, int incx)
	* uplo specifies whether the matrix A is an upper or lower triangular ba		{
	nd		cublasHandle_t handle = cublasGetCurrentCtx();
	* matrix. If uplo == 'U' or 'u', A is an upper triangular band matr		cublasStatus_t error = cublasStpsv_v2(handle, convertToFillMode(uplo),
	ix.		convertToOp(trans),
	* If uplo == 'L' or 'l', A is a lower triangular band matrix.		convertToDiagType(diag), n, AP, x, incx
	* trans specifies op(A). If transa == 'N' or 'n', op(A) = A. If trans ==		);
	'T',		cublasSetError(error);
	* or 't', op(A) = transpose(A). If trans == 'C' or 'c',		}
	* op(A) = conjugate(transpose(A)).
	* diag specifies whether or not matrix A is unit triangular. If diag ==
	'U'
	* or 'u', A is assumed to be unit triangular. If diag == 'N' or 'n'
	, A
	* is not assumed to be unit triangular.
	* n specifies the number of rows and columns of the matrix A. n must
	be
	* at least zero.
	* k specifies the number of super- or sub-diagonals. If uplo == 'U' o
	r
	* 'u', k specifies the number of super-diagonals. If uplo == 'L' or
	* 'l', k specifies the number of sub-diagonals. k must at least be
	* zero.
	* A double precision complex array of dimension (lda, n). If uplo ==
	'U' or 'u',
	* the leading (k + 1) x n part of the array A must contain the uppe
	r
	* triangular band matrix, supplied column by column, with the leadi
	ng
	* diagonal of the matrix in row (k + 1) of the array, the first
	* super-diagonal starting at position 2 in row k, and so on. The to
	p
	* left k x k triangle of the array A is not referenced. If uplo ==
	'L'
	* or 'l', the leading (k + 1) x n part of the array A must constain
	the
	* lower triangular band matrix, supplied column by column, with the
	* leading diagonal of the matrix in row 1 of the array, the first
	* sub-diagonal startingat position 1 in row 2, and so on. The botto
	m
	* right k x k triangle of the array is not referenced.
	* lda is the leading dimension of A. It must be at least (k + 1).
	* x double precision complex array of length at least (1 + (n - 1) *
	abs(incx)).
	* On entry, x contains the source vector. On exit, x is overwritten
	* with the result vector.
	* incx specifies the storage spacing for elements of x. incx must not be
	* zero.
	*
	* Output
	* ------
	* x updated according to x = op(A) * x
	*
	* Reference: http://www.netlib.org/blas/ztbmv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n or k < 0, or if incx == 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZtbmv (char uplo, char trans, char diag, int n,
	int k, const cuDoubleComplex *A, int lda, cuDoub
	leComplex *x,
	int incx);
	/*		static __inline__ void CUBLASAPI cublasDtpsv(char uplo, char trans, char di
	* void cublasZtbsv (char uplo, char trans, char diag, int n, int k,		ag, int n, const double *AP, double *x, int incx)
	* const cuDoubleComplex *A, int lda, cuDoubleComplex *X,		{
	int incx)		cublasHandle_t handle = cublasGetCurrentCtx();
	*		cublasStatus_t error = cublasDtpsv_v2(handle, convertToFillMode(uplo),
	* solves one of the systems of equations op(A)*x = b, where op(A) is eithe		convertToOp(trans),
	r		convertToDiagType(diag), n, AP, x, incx
	* op(A) = A , op(A) = transpose(A) or op(A) = conjugate(transpose(A)).		);
	* b and x are n element vectors, and A is an n x n unit or non-unit,		cublasSetError(error);
	* upper or lower triangular band matrix with k + 1 diagonals. No test		}
	* for singularity or near-singularity is included in this function.
	* Such tests must be performed before calling this function.
	*
	* Input
	* -----
	* uplo specifies whether the matrix is an upper or lower triangular band
	* matrix as follows: If uplo == 'U' or 'u', A is an upper triangula
	r
	* band matrix. If uplo == 'L' or 'l', A is a lower triangular band
	* matrix.
	* trans specifies op(A). If trans == 'N' or 'n', op(A) = A. If trans == '
	T',
	* 't', op(A) = transpose(A). If trans == 'C' or 'c',
	* op(A) = conjugate(transpose(A)).
	* diag specifies whether A is unit triangular. If diag == 'U' or 'u', A
	is
	* assumed to be unit triangular; thas is, diagonal elements are not
	* read and are assumed to be unity. If diag == 'N' or 'n', A is not
	* assumed to be unit triangular.
	* n specifies the number of rows and columns of the matrix A. n must
	be
	* at least zero.
	* k specifies the number of super- or sub-diagonals. If uplo == 'U' o
	r
	* 'u', k specifies the number of super-diagonals. If uplo == 'L' or
	* 'l', k specifies the number of sub-diagonals. k must at least be
	* zero.
	* A double precision complex array of dimension (lda, n). If uplo ==
	'U' or 'u',
	* the leading (k + 1) x n part of the array A must contain the uppe
	r
	* triangular band matrix, supplied column by column, with the leadi
	ng
	* diagonal of the matrix in row (k + 1) of the array, the first sup
	er-
	* diagonal starting at position 2 in row k, and so on. The top left
	* k x k triangle of the array A is not referenced. If uplo == 'L' o
	r
	* 'l', the leading (k + 1) x n part of the array A must constain th
	e
	* lower triangular band matrix, supplied column by column, with the
	* leading diagonal of the matrix in row 1 of the array, the first
	* sub-diagonal starting at position 1 in row 2, and so on. The bott
	om
	* right k x k triangle of the array is not referenced.
	* x double precision complex array of length at least (1+(n-1)*abs(in
	cx)).
	* incx storage spacing between elements of x. It must not be zero.
	*
	* Output
	* ------
	* x updated to contain the solution vector x that solves op(A) * x =
	b.
	*
	* Reference: http://www.netlib.org/blas/ztbsv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if incx == 0, n < 0 or n > 1016
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZtbsv (char uplo, char trans, char diag, int n,
	int k, const cuDoubleComplex *A, int lda, cuDou
	bleComplex *x,
	int incx);
	/*		static __inline__ void CUBLASAPI cublasCtpsv(char uplo, char trans, char di
	* void		ag, int n, const cuComplex *AP, cuComplex *x, int incx)
	* cublasZhemv (char uplo, int n, cuDoubleComplex alpha, const cuDoubleComp		{
	lex *A, int lda,		cublasHandle_t handle = cublasGetCurrentCtx();
	* const cuDoubleComplex *x, int incx, cuDoubleComplex beta, c		cublasStatus_t error = cublasCtpsv_v2(handle, convertToFillMode(uplo),
	uDoubleComplex *y, int incy)		convertToOp(trans),
	*		convertToDiagType(diag), n, AP, x, incx
	* performs the matrix-vector operation		);
	*		cublasSetError(error);
	* y = alpha*A*x + beta*y		}
	*
	* Alpha and beta are double precision complex scalars, and x and y are dou
	ble
	* precision complex vectors, each with n elements. A is a hermitian n x n
	matrix
	* consisting of double precision complex elements that is stored in either
	upper or
	* lower storage mode.
	*
	* Input
	* -----
	* uplo specifies whether the upper or lower triangular part of the array
	A
	* is to be referenced. If uplo == 'U' or 'u', the hermitian matrix
	A
	* is stored in upper storage mode, i.e. only the upper triangular p
	art
	* of A is to be referenced while the lower triangular part of A is
	to
	* be inferred. If uplo == 'L' or 'l', the hermitian matrix A is sto
	red
	* in lower storage mode, i.e. only the lower triangular part of A i
	s
	* to be referenced while the upper triangular part of A is to be
	* inferred.
	* n specifies the number of rows and the number of columns of the
	* hermitian matrix A. n must be at least zero.
	* alpha double precision complex scalar multiplier applied to A*x.
	* A double precision complex array of dimensions (lda, n). If uplo ==
	'U' or 'u',
	* the leading n x n upper triangular part of the array A must conta
	in
	* the upper triangular part of the hermitian matrix and the strictl
	y
	* lower triangular part of A is not referenced. If uplo == 'L' or '
	l',
	* the leading n x n lower triangular part of the array A must conta
	in
	* the lower triangular part of the hermitian matrix and the strictl
	y
	* upper triangular part of A is not referenced. The imaginary parts
	* of the diagonal elements need not be set, they are assumed to be
	zero.
	* lda leading dimension of A. It must be at least max (1, n).
	* x double precision complex array of length at least (1 + (n - 1) *
	abs(incx)).
	* incx storage spacing between elements of x. incx must not be zero.
	* beta double precision complex scalar multiplier applied to vector y.
	* y double precision complex array of length at least (1 + (n - 1) *
	abs(incy)).
	* If beta is zero, y is not read.
	* incy storage spacing between elements of y. incy must not be zero.
	*
	* Output
	* ------
	* y updated according to y = alpha*A*x + beta*y
	*
	* Reference: http://www.netlib.org/blas/zhemv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, or if incx or incy == 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZhemv (char uplo, int n, cuDoubleComplex alpha,
	const cuDoubleComplex *A, int lda, const cuDoub
	leComplex *x,
	int incx, cuDoubleComplex beta, cuDoubleComplex
	*y, int incy);
	/*		static __inline__ void CUBLASAPI cublasZtpsv(char uplo, char trans, char di
	* void		ag, int n, const cuDoubleComplex *AP,
	* cublasZhpmv (char uplo, int n, cuDoubleComplex alpha, const cuDoubleComp		cuDoubleComplex *x, int incx)
	lex *AP, const cuDoubleComplex *x,		{
	* int incx, cuDoubleComplex beta, cuDoubleComplex *y, int inc		cublasHandle_t handle = cublasGetCurrentCtx();
	y)		cublasStatus_t error = cublasZtpsv_v2(handle, convertToFillMode(uplo),
	*		convertToOp(trans),
	* performs the matrix-vector operation		convertToDiagType(diag), n, AP, x, incx
	*		);
	* y = alpha * A * x + beta * y		cublasSetError(error);
	*		}
	* Alpha and beta are double precision complex scalars, and x and y are dou		/*------------------------------------------------------------------------*
	ble		/
	* precision complex vectors with n elements. A is an hermitian n x n matri		/* TBSV */
	x		static __inline__ void CUBLASAPI cublasStbsv(char uplo, char trans,
	* consisting of double precision complex elements that is supplied in pack		char diag, int n, int k, const flo
	ed form.		at *A,
	*		int lda, float *x, int incx)
	* Input		{
	* -----		cublasHandle_t handle = cublasGetCurrentCtx();
	* uplo specifies whether the matrix data is stored in the upper or the l		cublasStatus_t error = cublasStbsv_v2(handle, convertToFillMode(uplo),
	ower		convertToOp(trans),
	* triangular part of array AP. If uplo == 'U' or 'u', then the uppe		convertToDiagType(diag), n, k, A, lda, x
	r		, incx );
	* triangular part of A is supplied in AP. If uplo == 'L' or 'l', th		cublasSetError(error);
	en		}
	* the lower triangular part of A is supplied in AP.
	* n specifies the number of rows and columns of the matrix A. It must
	be
	* at least zero.
	* alpha double precision complex scalar multiplier applied to A*x.
	* AP double precision complex array with at least ((n * (n + 1)) / 2)
	elements. If
	* uplo == 'U' or 'u', the array AP contains the upper triangular pa
	rt
	* of the hermitian matrix A, packed sequentially, column by column;
	* that is, if i <= j, then A[i,j] is stored is AP[i+(j*(j+1)/2)]. I
	f
	* uplo == 'L' or 'L', the array AP contains the lower triangular pa
	rt
	* of the hermitian matrix A, packed sequentially, column by column;
	* that is, if i >= j, then A[i,j] is stored in AP[i+((2*n-j+1)*j)/2
	].
	* The imaginary parts of the diagonal elements need not be set, the
	y
	* are assumed to be zero.
	* x double precision complex array of length at least (1 + (n - 1) *
	abs(incx)).
	* incx storage spacing between elements of x. incx must not be zero.
	* beta double precision complex scalar multiplier applied to vector y;
	* y double precision array of length at least (1 + (n - 1) * abs(incy
	)).
	* If beta is zero, y is not read.
	* incy storage spacing between elements of y. incy must not be zero.
	*
	* Output
	* ------
	* y updated according to y = alpha*A*x + beta*y
	*
	* Reference: http://www.netlib.org/blas/zhpmv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, or if incx or incy == 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZhpmv (char uplo, int n, cuDoubleComplex alpha,
	const cuDoubleComplex *AP, const cuDoubleComple
	x *x,
	int incx, cuDoubleComplex beta, cuDoubleComplex
	*y, int incy);
	/* ----------------- CUBLAS double complex BLAS3 functions ----------------		static __inline__ void CUBLASAPI cublasDtbsv(char uplo, char trans,
	- */		char diag, int n, int k, const dou
			ble *A,
			int lda, double *x, int incx)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasDtbsv_v2(handle, convertToFillMode(uplo),
			convertToOp(trans),
			convertToDiagType(diag), n, k, A, lda, x
			, incx );
			cublasSetError(error);
			}
			static __inline__ void CUBLASAPI cublasCtbsv(char uplo, char trans,
			char diag, int n, int k, const cuC
			omplex *A,
			int lda, cuComplex *x, int incx)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasCtbsv_v2(handle, convertToFillMode(uplo),
			convertToOp(trans),
			convertToDiagType(diag), n, k, A, lda, x
			, incx );
			cublasSetError(error);
			}
	/*		static __inline__ void CUBLASAPI cublasZtbsv(char uplo, char trans,
	* cublasZgemv (char trans, int m, int n, cuDoubleComplex alpha, const cuDo		char diag, int n, int k, const cuD
	ubleComplex *A, int lda,		oubleComplex *A,
	* const cuDoubleComplex *x, int incx, cuDoubleComplex beta, c		int lda, cuDoubleComplex *x, int i
	uDoubleComplex *y, int incy)		ncx)
	*		{
	* performs one of the matrix-vector operations		cublasHandle_t handle = cublasGetCurrentCtx();
	*		cublasStatus_t error = cublasZtbsv_v2(handle, convertToFillMode(uplo),
	* y = alpha * op(A) * x + beta * y,		convertToOp(trans),
	*		convertToDiagType(diag), n, k, A, lda, x
	* where op(A) is one of		, incx );
	*		cublasSetError(error);
	* op(A) = A or op(A) = transpose(A)		}
	*		/*------------------------------------------------------------------------*
	* where alpha and beta are double precision scalars, x and y are double		/
	* precision vectors, and A is an m x n matrix consisting of double precisi		/* SYMV/HEMV */
	on		static __inline__ void CUBLASAPI cublasSsymv (char uplo, int n, float alpha
	* elements. Matrix A is stored in column major format, and lda is the lead		, const float *A,
	ing		int lda, const float *x, int incx, float beta,
	* dimension of the two-dimensional array in which A is stored.		float *y, int incy)
	*		{
	* Input		cublasHandle_t handle = cublasGetCurrentCtx();
	* -----		cublasStatus_t error = cublasSsymv_v2(handle, convertToFillMode(uplo),
	* trans specifies op(A). If transa = 'n' or 'N', op(A) = A. If trans =		n, &alpha, A, lda, x, incx, &beta, y, in
	* trans = 't', 'T', 'c', or 'C', op(A) = transpose(A)		cy );
	* m specifies the number of rows of the matrix A. m must be at least		cublasSetError(error);
	* zero.		}
	* n specifies the number of columns of the matrix A. n must be at lea		static __inline__ void CUBLASAPI cublasDsymv (char uplo, int n, double alph
	st		a, const double *A,
	* zero.		int lda, const double *x, int incx, double beta
	* alpha double precision scalar multiplier applied to op(A).		,
	* A double precision array of dimensions (lda, n) if trans = 'n' or		double *y, int incy)
	* 'N'), and of dimensions (lda, m) otherwise. lda must be at least		{
	* max(1, m) and at least max(1, n) otherwise.		cublasHandle_t handle = cublasGetCurrentCtx();
	* lda leading dimension of two-dimensional array used to store matrix A		cublasStatus_t error = cublasDsymv_v2(handle, convertToFillMode(uplo),
	* x double precision array of length at least (1 + (n - 1) * abs(incx		n, &alpha, A, lda, x, incx, &beta, y, in
	))		cy );
	* when trans = 'N' or 'n' and at least (1 + (m - 1) * abs(incx))		cublasSetError(error);
	* otherwise.		}
	* incx specifies the storage spacing between elements of x. incx must no		static __inline__ void CUBLASAPI cublasChemv (char uplo, int n, cuComplex a
	t		lpha, const cuComplex *A,
	* be zero.		int lda, const cuComplex *x, int incx, cuComple
	* beta double precision scalar multiplier applied to vector y. If beta		x beta,
	* is zero, y is not read.		cuComplex *y, int incy)
	* y double precision array of length at least (1 + (m - 1) * abs(incy		{
	))		cublasHandle_t handle = cublasGetCurrentCtx();
	* when trans = 'N' or 'n' and at least (1 + (n - 1) * abs(incy))		cublasStatus_t error = cublasChemv_v2(handle, convertToFillMode(uplo),
	* otherwise.		n, &alpha, A, lda, x, incx, &beta, y, in
	* incy specifies the storage spacing between elements of x. incx must no		cy );
	t		cublasSetError(error);
	* be zero.		}
	*		static __inline__ void CUBLASAPI cublasZhemv (char uplo, int n, cuDoubleCom
	* Output		plex alpha, const cuDoubleComplex *A,
	* ------		int lda, const cuDoubleComplex *x, int incx, cu
	* y updated according to alpha * op(A) * x + beta * y		DoubleComplex beta,
	*		cuDoubleComplex *y, int incy)
	* Reference: http://www.netlib.org/blas/zgemv.f		{
	*		cublasHandle_t handle = cublasGetCurrentCtx();
	* Error status for this function can be retrieved via cublasGetError().		cublasStatus_t error = cublasZhemv_v2(handle, convertToFillMode(uplo),
	*		n, &alpha, A, lda, x, incx, &beta, y, in
	* Error Status		cy );
	* ------------		cublasSetError(error);
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize		}
	d		/*------------------------------------------------------------------------*
	* CUBLAS_STATUS_INVALID_VALUE if m or n are < 0, or if incx or incy ==		/
	0		/* SBMV/HBMV */
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU		static __inline__ void CUBLASAPI cublasSsbmv (char uplo, int n, int k, floa
	*/		t alpha,
	void CUBLASAPI cublasZgemv (char trans, int m, int n, cuDoubleComplex alpha		const float *A, int lda, const float *x, int in
	,		cx,
			float beta, float *y, int incy)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasSsbmv_v2(handle, convertToFillMode(uplo),n
			,k, &alpha,
			A, lda, x, incx, &beta, y, incy);
			cublasSetError(error);
			}
			static __inline__ void CUBLASAPI cublasDsbmv (char uplo, int n, int k, doub
			le alpha,
			const double *A, int lda, const double *x, int
			incx,
			double beta, double *y, int incy)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasDsbmv_v2(handle, convertToFillMode(uplo),n
			,k, &alpha,
			A, lda, x, incx, &beta, y, incy);
			cublasSetError(error);
			}
			static __inline__ void CUBLASAPI cublasChbmv (char uplo, int n, int k, cuCo
			mplex alpha,
			const cuComplex *A, int lda, const cuComplex *x
			, int incx,
			cuComplex beta, cuComplex *y, int incy)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasChbmv_v2(handle, convertToFillMode(uplo),n
			,k, &alpha,
			A, lda, x, incx, &beta, y, incy);
			cublasSetError(error);
			}
			static __inline__ void CUBLASAPI cublasZhbmv (char uplo, int n, int k, cuDo
			ubleComplex alpha,
	const cuDoubleComplex *A, int lda, const cuDoub leComplex *x, int incx,		const cuDoubleComplex *A, int lda, const cuDoub leComplex *x, int incx,
	cuDoubleComplex beta, cuDoubleComplex *y, int i		cuDoubleComplex beta, cuDoubleComplex *y, int i
	ncy);		ncy)
			{
	/*		cublasHandle_t handle = cublasGetCurrentCtx();
	* void		cublasStatus_t error = cublasZhbmv_v2(handle, convertToFillMode(uplo),n
	* cublasZtpmv (char uplo, char trans, char diag, int n, const cuDoubleComp		,k, &alpha,
	lex *AP,		A, lda, x, incx, &beta, y, incy);
	* cuDoubleComplex *x, int incx);		cublasSetError(error);
	*		}
	* performs one of the matrix-vector operations x = op(A) * x, where op(A)		/*------------------------------------------------------------------------*
	= A,		/
	* op(A) = transpose(A) or op(A) = conjugate(transpose(A)) . x is an n elem		/* SPMV/HPMV */
	ent		static __inline__ void CUBLASAPI cublasSspmv(char uplo, int n, float alpha,
	* double precision complex vector, and A is an n x n, unit or non-unit, up		const float *AP, const float *x,
	per		int incx, float beta, float *y, int in
	* or lower triangular matrix composed of double precision complex elements		cy)
	.		{
	*		cublasHandle_t handle = cublasGetCurrentCtx();
	* Input		cublasStatus_t error = cublasSspmv_v2(handle, convertToFillMode(uplo),n
	* -----		, &alpha,
	* uplo specifies whether the matrix A is an upper or lower triangular		AP, x, incx, &beta, y, incy);
	* matrix. If uplo == 'U' or 'u', then A is an upper triangular matr		cublasSetError(error);
	ix.		}
	* If uplo == 'L' or 'l', then A is a lower triangular matrix.		static __inline__ void CUBLASAPI cublasDspmv(char uplo, int n, double alpha
	* trans specifies op(A). If transa == 'N' or 'n', op(A) = A. If trans ==		,
	'T',		const double *AP, const double *x,
	* or 't', op(A) = transpose(A). If trans == 'C' or 'c',		int incx, double beta, double *y, int
	* op(A) = conjugate(transpose(A)).		incy)
	*		{
	* diag specifies whether or not matrix A is unit triangular. If diag ==		cublasHandle_t handle = cublasGetCurrentCtx();
	'U'		cublasStatus_t error = cublasDspmv_v2(handle, convertToFillMode(uplo),n
	* or 'u', A is assumed to be unit triangular. If diag == 'N' or 'n'		, &alpha,
	, A		AP, x, incx, &beta, y, incy);
	* is not assumed to be unit triangular.		cublasSetError(error);
	* n specifies the number of rows and columns of the matrix A. n must		}
	be		static __inline__ void CUBLASAPI cublasChpmv(char uplo, int n, cuComplex al
	* at least zero. In the current implementation n must not exceed 40		pha,
	70.		const cuComplex *AP, const cuComplex *
	* AP double precision complex array with at least ((n * (n + 1)) / 2)		x,
	elements. If		int incx, cuComplex beta, cuComplex *y
	* uplo == 'U' or 'u', the array AP contains the upper triangular pa		, int incy)
	rt		{
	* of the symmetric matrix A, packed sequentially, column by column;		cublasHandle_t handle = cublasGetCurrentCtx();
	* that is, if i <= j, then A[i,j] is stored in AP[i+(j*(j+1)/2)]. I		cublasStatus_t error = cublasChpmv_v2(handle, convertToFillMode(uplo),n
	f		, &alpha,
	* uplo == 'L' or 'L', the array AP contains the lower triangular pa		AP, x, incx, &beta, y, incy);
	rt		cublasSetError(error);
	* of the symmetric matrix A, packed sequentially, column by column;		}
	* that is, if i >= j, then A[i,j] is stored in AP[i+((2*n-j+1)*j)/2		static __inline__ void CUBLASAPI cublasZhpmv(char uplo, int n, cuDoubleComp
	].		lex alpha,
	* x double precision complex array of length at least (1 + (n - 1) *		const cuDoubleComplex *AP, const cuDou
	abs(incx)).		bleComplex *x,
	* On entry, x contains the source vector. On exit, x is overwritten		int incx, cuDoubleComplex beta, cuDoub
	* with the result vector.		leComplex *y, int incy)
	* incx specifies the storage spacing for elements of x. incx must not be		{
	* zero.		cublasHandle_t handle = cublasGetCurrentCtx();
	*		cublasStatus_t error = cublasZhpmv_v2(handle, convertToFillMode(uplo),n
	* Output		, &alpha,
	* ------		AP, x, incx, &beta, y, incy);
	* x updated according to x = op(A) * x,		cublasSetError(error);
	*		}
	* Reference: http://www.netlib.org/blas/ztpmv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if incx == 0 or n < 0
	* CUBLAS_STATUS_ALLOC_FAILED if function cannot allocate enough intern
	al scratch vector memory
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZtpmv (char uplo, char trans, char diag, int n,
	const cuDoubleComplex *AP, cuDoubleComplex *x,
	int incx);
	/*
	* void
	* cublasZtpsv (char uplo, char trans, char diag, int n, const cuDoubleComp
	lex *AP,
	* cuDoubleComplex *X, int incx)
	*
	* solves one of the systems of equations op(A)*x = b, where op(A) is eithe
	r
	* op(A) = A , op(A) = transpose(A) or op(A) = conjugate(transpose)). b and
	* x are n element complex vectors, and A is an n x n unit or non-unit,
	* upper or lower triangular matrix. No test for singularity or near-singul
	arity
	* is included in this routine. Such tests must be performed before calling
	this routine.
	*
	* Input
	* -----
	* uplo specifies whether the matrix is an upper or lower triangular matr
	ix
	* as follows: If uplo == 'U' or 'u', A is an upper triangluar matri
	x.
	* If uplo == 'L' or 'l', A is a lower triangular matrix.
	* trans specifies op(A). If trans == 'N' or 'n', op(A) = A. If trans == '
	T'
	* or 't', op(A) = transpose(A). If trans == 'C' or 'c', op(A) =
	* conjugate(transpose(A)).
	* diag specifies whether A is unit triangular. If diag == 'U' or 'u', A
	is
	* assumed to be unit triangular; thas is, diagonal elements are not
	* read and are assumed to be unity. If diag == 'N' or 'n', A is not
	* assumed to be unit triangular.
	* n specifies the number of rows and columns of the matrix A. n must
	be
	* at least zero.
	* AP double precision complex array with at least ((n*(n+1))/2) elemen
	ts.
	* If uplo == 'U' or 'u', the array AP contains the upper triangular
	* matrix A, packed sequentially, column by column; that is, if i <=
	j, then
	* A[i,j] is stored is AP[i+(j*(j+1)/2)]. If uplo == 'L' or 'L', the
	* array AP contains the lower triangular matrix A, packed sequentia
	lly,
	* column by column; that is, if i >= j, then A[i,j] is stored in
	* AP[i+((2*n-j+1)*j)/2]. When diag = 'U' or 'u', the diagonal eleme
	nts
	* of A are not referenced and are assumed to be unity.
	* x double precision complex array of length at least (1+(n-1)*abs(in
	cx)).
	* incx storage spacing between elements of x. It must not be zero.
	*
	* Output
	* ------
	* x updated to contain the solution vector x that solves op(A) * x =
	b.
	*
	* Reference: http://www.netlib.org/blas/ztpsv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if incx == 0 or if n < 0 or n > 2035
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZtpsv (char uplo, char trans, char diag, int n,
	const cuDoubleComplex *AP, cuDoubleComplex *x,
	int incx);
	/* ----------------- CUBLAS single complex BLAS2 functions ----------------
	- */
	/*
	* cublasCgemv (char trans, int m, int n, cuComplex alpha, const cuComplex
	*A,
	* int lda, const cuComplex *x, int incx, cuComplex beta, cuCo
	mplex *y,
	* int incy)
	*
	* performs one of the matrix-vector operations
	*
	* y = alpha * op(A) * x + beta * y,
	*
	* where op(A) is one of
	*
	* op(A) = A or op(A) = transpose(A) or op(A) = conjugate(transpose(
	A))
	*
	* where alpha and beta are single precision scalars, x and y are single
	* precision vectors, and A is an m x n matrix consisting of single precisi
	on
	* elements. Matrix A is stored in column major format, and lda is the lead
	ing
	* dimension of the two-dimensional array in which A is stored.
	*
	* Input
	* -----
	* trans specifies op(A). If transa = 'n' or 'N', op(A) = A. If trans =
	* trans = 't' or 'T', op(A) = transpose(A). If trans = 'c' or 'C',
	* op(A) = conjugate(transpose(A))
	* m specifies the number of rows of the matrix A. m must be at least
	* zero.
	* n specifies the number of columns of the matrix A. n must be at lea
	st
	* zero.
	* alpha single precision scalar multiplier applied to op(A).
	* A single precision array of dimensions (lda, n) if trans = 'n' or
	* 'N'), and of dimensions (lda, m) otherwise. lda must be at least
	* max(1, m) and at least max(1, n) otherwise.
	* lda leading dimension of two-dimensional array used to store matrix A
	* x single precision array of length at least (1 + (n - 1) * abs(incx
	))
	* when trans = 'N' or 'n' and at least (1 + (m - 1) * abs(incx))
	* otherwise.
	* incx specifies the storage spacing between elements of x. incx must no
	t
	* be zero.
	* beta single precision scalar multiplier applied to vector y. If beta
	* is zero, y is not read.
	* y single precision array of length at least (1 + (m - 1) * abs(incy
	))
	* when trans = 'N' or 'n' and at least (1 + (n - 1) * abs(incy))
	* otherwise.
	* incy specifies the storage spacing between elements of y. incy must no
	t
	* be zero.
	*
	* Output
	* ------
	* y updated according to alpha * op(A) * x + beta * y
	*
	* Reference: http://www.netlib.org/blas/cgemv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if m or n are < 0, or if incx or incy ==
	0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasCgemv (char trans, int m, int n, cuComplex alpha,
	const cuComplex *A, int lda, const cuComplex *x
	,
	int incx, cuComplex beta, cuComplex *y, int inc
	y);
	/*
	* void
	* cublasCgbmv (char trans, int m, int n, int kl, int ku, cuComplex alpha,
	* const cuComplex *A, int lda, const cuComplex *x, int incx,
	cuComplex beta,
	* cuComplex *y, int incy);
	*
	* performs one of the matrix-vector operations
	*
	* y = alpha*op(A)*x + beta*y, op(A)=A or op(A) = transpose(A)
	*
	* alpha and beta are single precision complex scalars. x and y are single
	precision
	* complex vectors. A is an m by n band matrix consisting of single precisi
	on complex elements
	* with kl sub-diagonals and ku super-diagonals.
	*
	* Input
	* -----
	* trans specifies op(A). If trans == 'N' or 'n', op(A) = A. If trans == '
	T',
	* or 't', op(A) = transpose(A). If trans == 'C' or 'c',
	* op(A) = conjugate(transpose(A)).
	* m specifies the number of rows of the matrix A. m must be at least
	* zero.
	* n specifies the number of columns of the matrix A. n must be at lea
	st
	* zero.
	* kl specifies the number of sub-diagonals of matrix A. It must be at
	* least zero.
	* ku specifies the number of super-diagonals of matrix A. It must be a
	t
	* least zero.
	* alpha single precision complex scalar multiplier applied to op(A).
	* A single precision complex array of dimensions (lda, n). The leadin
	g
	* (kl + ku + 1) x n part of the array A must contain the band matri
	x A,
	* supplied column by column, with the leading diagonal of the matri
	x
	* in row (ku + 1) of the array, the first super-diagonal starting a
	t
	* position 2 in row ku, the first sub-diagonal starting at position
	1
	* in row (ku + 2), and so on. Elements in the array A that do not
	* correspond to elements in the band matrix (such as the top left
	* ku x ku triangle) are not referenced.
	* lda leading dimension of A. lda must be at least (kl + ku + 1).
	* x single precision complex array of length at least (1+(n-1)*abs(in
	cx)) when
	* trans == 'N' or 'n' and at least (1+(m-1)*abs(incx)) otherwise.
	* incx specifies the increment for the elements of x. incx must not be z
	ero.
	* beta single precision complex scalar multiplier applied to vector y. I
	f beta is
	* zero, y is not read.
	* y single precision complex array of length at least (1+(m-1)*abs(in
	cy)) when
	* trans == 'N' or 'n' and at least (1+(n-1)*abs(incy)) otherwise. I
	f
	* beta is zero, y is not read.
	* incy On entry, incy specifies the increment for the elements of y. inc
	y
	* must not be zero.
	*
	* Output
	* ------
	* y updated according to y = alpha*op(A)*x + beta*y
	*
	* Reference: http://www.netlib.org/blas/cgbmv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, or if incx or incy == 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasCgbmv (char trans, int m, int n, int kl, int ku,
	cuComplex alpha, const cuComplex *A, int lda,
	const cuComplex *x, int incx, cuComplex beta,
	cuComplex *y, int incy);
	/*
	* void
	* cublasChemv (char uplo, int n, cuComplex alpha, const cuComplex *A, int
	lda,
	* const cuComplex *x, int incx, cuComplex beta, cuComplex *y,
	int incy)
	*
	* performs the matrix-vector operation
	*
	* y = alpha*A*x + beta*y
	*
	* Alpha and beta are single precision complex scalars, and x and y are sin
	gle
	* precision complex vectors, each with n elements. A is a hermitian n x n
	matrix
	* consisting of single precision complex elements that is stored in either
	upper or
	* lower storage mode.
	*
	* Input
	* -----
	* uplo specifies whether the upper or lower triangular part of the array
	A
	* is to be referenced. If uplo == 'U' or 'u', the hermitian matrix
	A
	* is stored in upper storage mode, i.e. only the upper triangular p
	art
	* of A is to be referenced while the lower triangular part of A is
	to
	* be inferred. If uplo == 'L' or 'l', the hermitian matrix A is sto
	red
	* in lower storage mode, i.e. only the lower triangular part of A i
	s
	* to be referenced while the upper triangular part of A is to be
	* inferred.
	* n specifies the number of rows and the number of columns of the
	* hermitian matrix A. n must be at least zero.
	* alpha single precision complex scalar multiplier applied to A*x.
	* A single precision complex array of dimensions (lda, n). If uplo ==
	'U' or 'u',
	* the leading n x n upper triangular part of the array A must conta
	in
	* the upper triangular part of the hermitian matrix and the strictl
	y
	* lower triangular part of A is not referenced. If uplo == 'L' or '
	l',
	* the leading n x n lower triangular part of the array A must conta
	in
	* the lower triangular part of the hermitian matrix and the strictl
	y
	* upper triangular part of A is not referenced. The imaginary parts
	* of the diagonal elements need not be set, they are assumed to be
	zero.
	* lda leading dimension of A. It must be at least max (1, n).
	* x single precision complex array of length at least (1 + (n - 1) *
	abs(incx)).
	* incx storage spacing between elements of x. incx must not be zero.
	* beta single precision complex scalar multiplier applied to vector y.
	* y single precision complex array of length at least (1 + (n - 1) *
	abs(incy)).
	* If beta is zero, y is not read.
	* incy storage spacing between elements of y. incy must not be zero.
	*
	* Output
	* ------
	* y updated according to y = alpha*A*x + beta*y
	*
	* Reference: http://www.netlib.org/blas/chemv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, or if incx or incy == 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasChemv (char uplo, int n, cuComplex alpha,
	const cuComplex *A, int lda, const cuComplex *x
	,
	int incx, cuComplex beta, cuComplex *y, int inc
	y);
	/*
	* void
	* cublasChbmv (char uplo, int n, int k, cuComplex alpha, const cuComplex *
	A, int lda,
	* const cuComplex *x, int incx, cuComplex beta, cuComplex *y,
	int incy)
	*
	* performs the matrix-vector operation
	*
	* y := alpha*A*x + beta*y
	*
	* alpha and beta are single precision complex scalars. x and y are single
	precision
	* complex vectors with n elements. A is an n by n hermitian band matrix co
	nsisting
	* of single precision complex elements, with k super-diagonals and the sam
	e number
	* of subdiagonals.
	*
	* Input
	* -----
	* uplo specifies whether the upper or lower triangular part of the hermi
	tian
	* band matrix A is being supplied. If uplo == 'U' or 'u', the upper
	* triangular part is being supplied. If uplo == 'L' or 'l', the low
	er
	* triangular part is being supplied.
	* n specifies the number of rows and the number of columns of the
	* hermitian matrix A. n must be at least zero.
	* k specifies the number of super-diagonals of matrix A. Since the ma
	trix
	* is hermitian, this is also the number of sub-diagonals. k must be
	at
	* least zero.
	* alpha single precision complex scalar multiplier applied to A*x.
	* A single precision complex array of dimensions (lda, n). When uplo
	== 'U' or
	* 'u', the leading (k + 1) x n part of array A must contain the upp
	er
	* triangular band of the hermitian matrix, supplied column by colum
	n,
	* with the leading diagonal of the matrix in row (k+1) of the array
	,
	* the first super-diagonal starting at position 2 in row k, and so
	on.
	* The top left k x k triangle of the array A is not referenced. Whe
	n
	* uplo == 'L' or 'l', the leading (k + 1) x n part of the array A m
	ust
	* contain the lower triangular band part of the hermitian matrix,
	* supplied column by column, with the leading diagonal of the matri
	x in
	* row 1 of the array, the first sub-diagonal starting at position 1
	in
	* row 2, and so on. The bottom right k x k triangle of the array A
	is
	* not referenced. The imaginary parts of the diagonal elements need
	* not be set, they are assumed to be zero.
	* lda leading dimension of A. lda must be at least (k + 1).
	* x single precision complex array of length at least (1 + (n - 1) *
	abs(incx)).
	* incx storage spacing between elements of x. incx must not be zero.
	* beta single precision complex scalar multiplier applied to vector y. I
	f beta is
	* zero, y is not read.
	* y single precision complex array of length at least (1 + (n - 1) *
	abs(incy)).
	* If beta is zero, y is not read.
	* incy storage spacing between elements of y. incy must not be zero.
	*
	* Output
	* ------
	* y updated according to alpha*A*x + beta*y
	*
	* Reference: http://www.netlib.org/blas/chbmv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if k or n < 0, or if incx or incy == 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasChbmv (char uplo, int n, int k, cuComplex alpha,
	const cuComplex *A, int lda, const cuComplex *x
	,
	int incx, cuComplex beta, cuComplex *y, int inc
	y);
	/*
	* void
	* cublasChpmv (char uplo, int n, cuComplex alpha, const cuComplex *AP, con
	st cuComplex *x,
	* int incx, cuComplex beta, cuComplex *y, int incy)
	*
	* performs the matrix-vector operation
	*
	* y = alpha * A * x + beta * y
	*
	* Alpha and beta are single precision complex scalars, and x and y are sin
	gle
	* precision complex vectors with n elements. A is an hermitian n x n matri
	x
	* consisting of single precision complex elements that is supplied in pack
	ed form.
	*
	* Input
	* -----
	* uplo specifies whether the matrix data is stored in the upper or the l
	ower
	* triangular part of array AP. If uplo == 'U' or 'u', then the uppe
	r
	* triangular part of A is supplied in AP. If uplo == 'L' or 'l', th
	en
	* the lower triangular part of A is supplied in AP.
	* n specifies the number of rows and columns of the matrix A. It must
	be
	* at least zero.
	* alpha single precision complex scalar multiplier applied to A*x.
	* AP single precision complex array with at least ((n * (n + 1)) / 2)
	elements. If
	* uplo == 'U' or 'u', the array AP contains the upper triangular pa
	rt
	* of the hermitian matrix A, packed sequentially, column by column;
	* that is, if i <= j, then A[i,j] is stored is AP[i+(j*(j+1)/2)]. I
	f
	* uplo == 'L' or 'L', the array AP contains the lower triangular pa
	rt
	* of the hermitian matrix A, packed sequentially, column by column;
	* that is, if i >= j, then A[i,j] is stored in AP[i+((2*n-j+1)*j)/2
	].
	* The imaginary parts of the diagonal elements need not be set, the
	y
	* are assumed to be zero.
	* x single precision complex array of length at least (1 + (n - 1) *
	abs(incx)).
	* incx storage spacing between elements of x. incx must not be zero.
	* beta single precision complex scalar multiplier applied to vector y;
	* y single precision array of length at least (1 + (n - 1) * abs(incy
	)).
	* If beta is zero, y is not read.
	* incy storage spacing between elements of y. incy must not be zero.
	*
	* Output
	* ------
	* y updated according to y = alpha*A*x + beta*y
	*
	* Reference: http://www.netlib.org/blas/chpmv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, or if incx or incy == 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasChpmv (char uplo, int n, cuComplex alpha,
	const cuComplex *AP, const cuComplex *x, int in
	cx,
	cuComplex beta, cuComplex *y, int incy);
	/*
	*
	* cublasCtrmv (char uplo, char trans, char diag, int n, const cuComplex *A
	,
	* int lda, cuComplex *x, int incx);
	*
	* performs one of the matrix-vector operations x = op(A) * x,
	* where op(A) = A, or op(A) = transpose(A) or op(A) = conjugate(transpose(
	A)).
	* x is an n-element signle precision complex vector, and
	* A is an n x n, unit or non-unit, upper or lower, triangular matrix compo
	sed
	* of single precision complex elements.
	*
	* Input
	* -----
	* uplo specifies whether the matrix A is an upper or lower triangular
	* matrix. If uplo = 'U' or 'u', then A is an upper triangular matri
	x.
	* If uplo = 'L' or 'l', then A is a lower triangular matrix.
	* trans specifies op(A). If trans = 'n' or 'N', op(A) = A. If trans = 't'
	or
	* 'T', op(A) = transpose(A). If trans = 'c' or 'C', op(A) =
	* conjugate(transpose(A)).
	* diag specifies whether or not matrix A is unit triangular. If diag = '
	U'
	* or 'u', A is assumed to be unit triangular. If diag = 'N' or 'n',
	A
	* is not assumed to be unit triangular.
	* n specifies the number of rows and columns of the matrix A. n must
	be
	* at least zero.
	* A single precision array of dimension (lda, n). If uplo = 'U' or 'u
	',
	* the leading n x n upper triangular part of the array A must conta
	in
	* the upper triangular matrix and the strictly lower triangular par
	t
	* of A is not referenced. If uplo = 'L' or 'l', the leading n x n l
	ower
	* triangular part of the array A must contain the lower triangular
	* matrix and the strictly upper triangular part of A is not referen
	ced.
	* When diag = 'U' or 'u', the diagonal elements of A are not refere
	nced
	* either, but are are assumed to be unity.
	* lda is the leading dimension of A. It must be at least max (1, n).
	* x single precision array of length at least (1 + (n - 1) * abs(incx
	) ).
	* On entry, x contains the source vector. On exit, x is overwritten
	* with the result vector.
	* incx specifies the storage spacing for elements of x. incx must not be
	* zero.
	*
	* Output
	* ------
	* x updated according to x = op(A) * x,
	*
	* Reference: http://www.netlib.org/blas/ctrmv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if incx == 0 or if n < 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasCtrmv (char uplo, char trans, char diag, int n,
	const cuComplex *A, int lda, cuComplex *x,
	int incx);
	/*		/*------------------------------------------------------------------------*
	* void		/
	* cublasCtbmv (char uplo, char trans, char diag, int n, int k, const cuCom		/* GER */
	plex *A,		static __inline__ void CUBLASAPI cublasSger (int m, int n, float alpha, con
	* int lda, cuComplex *x, int incx)		st float *x, int incx,
	*		const float *y, int incy, float *A, int lda)
	* performs one of the matrix-vector operations x = op(A) * x, where op(A)		{
	= A,		cublasHandle_t handle = cublasGetCurrentCtx();
	* op(A) = transpose(A) or op(A) = conjugate(transpose(A)). x is an n-eleme		cublasStatus_t error = cublasSger_v2(handle, m, n, &alpha,
	nt		x, incx, y, incy, A, lda);
	* single precision complex vector, and A is an n x n, unit or non-unit, up		cublasSetError(error);
	per		}
	* or lower triangular band matrix composed of single precision complex ele		static __inline__ void CUBLASAPI cublasDger (int m, int n, double alpha, co
	ments.		nst double *x, int incx,
	*		const double *y, int incy, double *A, int lda)
	* Input		{
	* -----		cublasHandle_t handle = cublasGetCurrentCtx();
	* uplo specifies whether the matrix A is an upper or lower triangular ba		cublasStatus_t error = cublasDger_v2(handle, m, n, &alpha,
	nd		x, incx, y, incy, A, lda);
	* matrix. If uplo == 'U' or 'u', A is an upper triangular band matr		cublasSetError(error);
	ix.		}
	* If uplo == 'L' or 'l', A is a lower triangular band matrix.
	* trans specifies op(A). If transa == 'N' or 'n', op(A) = A. If trans ==
	'T',
	* or 't', op(A) = transpose(A). If trans == 'C' or 'c',
	* op(A) = conjugate(transpose(A)).
	* diag specifies whether or not matrix A is unit triangular. If diag ==
	'U'
	* or 'u', A is assumed to be unit triangular. If diag == 'N' or 'n'
	, A
	* is not assumed to be unit triangular.
	* n specifies the number of rows and columns of the matrix A. n must
	be
	* at least zero.
	* k specifies the number of super- or sub-diagonals. If uplo == 'U' o
	r
	* 'u', k specifies the number of super-diagonals. If uplo == 'L' or
	* 'l', k specifies the number of sub-diagonals. k must at least be
	* zero.
	* A single precision complex array of dimension (lda, n). If uplo ==
	'U' or 'u',
	* the leading (k + 1) x n part of the array A must contain the uppe
	r
	* triangular band matrix, supplied column by column, with the leadi
	ng
	* diagonal of the matrix in row (k + 1) of the array, the first
	* super-diagonal starting at position 2 in row k, and so on. The to
	p
	* left k x k triangle of the array A is not referenced. If uplo ==
	'L'
	* or 'l', the leading (k + 1) x n part of the array A must constain
	the
	* lower triangular band matrix, supplied column by column, with the
	* leading diagonal of the matrix in row 1 of the array, the first
	* sub-diagonal startingat position 1 in row 2, and so on. The botto
	m
	* right k x k triangle of the array is not referenced.
	* lda is the leading dimension of A. It must be at least (k + 1).
	* x single precision complex array of length at least (1 + (n - 1) *
	abs(incx)).
	* On entry, x contains the source vector. On exit, x is overwritten
	* with the result vector.
	* incx specifies the storage spacing for elements of x. incx must not be
	* zero.
	*
	* Output
	* ------
	* x updated according to x = op(A) * x
	*
	* Reference: http://www.netlib.org/blas/ctbmv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n or k < 0, or if incx == 0
	* CUBLAS_STATUS_ALLOC_FAILED if function cannot allocate enough intern
	al scratch vector memory
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasCtbmv (char uplo, char trans, char diag, int n, int k,
	const cuComplex *A, int lda, cuComplex *x,
	int incx);
	/*
	* void
	* cublasCtpmv (char uplo, char trans, char diag, int n, const cuComplex *A
	P,
	* cuComplex *x, int incx);
	*
	* performs one of the matrix-vector operations x = op(A) * x, where op(A)
	= A,
	* op(A) = transpose(A) or op(A) = conjugate(transpose(A)) . x is an n elem
	ent
	* single precision complex vector, and A is an n x n, unit or non-unit, up
	per
	* or lower triangular matrix composed of single precision complex elements
	.
	*
	* Input
	* -----
	* uplo specifies whether the matrix A is an upper or lower triangular
	* matrix. If uplo == 'U' or 'u', then A is an upper triangular matr
	ix.
	* If uplo == 'L' or 'l', then A is a lower triangular matrix.
	* trans specifies op(A). If transa == 'N' or 'n', op(A) = A. If trans ==
	'T',
	* or 't', op(A) = transpose(A). If trans == 'C' or 'c',
	* op(A) = conjugate(transpose(A)).
	*
	* diag specifies whether or not matrix A is unit triangular. If diag ==
	'U'
	* or 'u', A is assumed to be unit triangular. If diag == 'N' or 'n'
	, A
	* is not assumed to be unit triangular.
	* n specifies the number of rows and columns of the matrix A. n must
	be
	* at least zero. In the current implementation n must not exceed 40
	70.
	* AP single precision complex array with at least ((n * (n + 1)) / 2)
	elements. If
	* uplo == 'U' or 'u', the array AP contains the upper triangular pa
	rt
	* of the symmetric matrix A, packed sequentially, column by column;
	* that is, if i <= j, then A[i,j] is stored in AP[i+(j*(j+1)/2)]. I
	f
	* uplo == 'L' or 'L', the array AP contains the lower triangular pa
	rt
	* of the symmetric matrix A, packed sequentially, column by column;
	* that is, if i >= j, then A[i,j] is stored in AP[i+((2*n-j+1)*j)/2
	].
	* x single precision complex array of length at least (1 + (n - 1) *
	abs(incx)).
	* On entry, x contains the source vector. On exit, x is overwritten
	* with the result vector.
	* incx specifies the storage spacing for elements of x. incx must not be
	* zero.
	*
	* Output
	* ------
	* x updated according to x = op(A) * x,
	*
	* Reference: http://www.netlib.org/blas/ctpmv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if incx == 0 or n < 0
	* CUBLAS_STATUS_ALLOC_FAILED if function cannot allocate enough intern
	al scratch vector memory
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasCtpmv (char uplo, char trans, char diag, int n,
	const cuComplex *AP, cuComplex *x, int incx);
	/*
	* void
	* cublasCtrsv (char uplo, char trans, char diag, int n, const cuComplex *A
	,
	* int lda, cuComplex *x, int incx)
	*
	* solves a system of equations op(A) * x = b, where op(A) is either A,
	* transpose(A) or conjugate(transpose(A)). b and x are single precision
	* complex vectors consisting of n elements, and A is an n x n matrix
	* composed of a unit or non-unit, upper or lower triangular matrix.
	* Matrix A is stored in column major format, and lda is the leading
	* dimension of the two-dimensional array containing A.
	*
	* No test for singularity or near-singularity is included in this function
	.
	* Such tests must be performed before calling this function.
	*
	* Input
	* -----
	* uplo specifies whether the matrix data is stored in the upper or the
	* lower triangular part of array A. If uplo = 'U' or 'u', then only
	* the upper triangular part of A may be referenced. If uplo = 'L' o
	r
	* 'l', then only the lower triangular part of A may be referenced.
	* trans specifies op(A). If transa = 'n' or 'N', op(A) = A. If transa = '
	t',
	* 'T', 'c', or 'C', op(A) = transpose(A)
	* diag specifies whether or not A is a unit triangular matrix like so:
	* if diag = 'U' or 'u', A is assumed to be unit triangular. If
	* diag = 'N' or 'n', then A is not assumed to be unit triangular.
	* n specifies the number of rows and columns of the matrix A. It
	* must be at least 0.
	* A is a single precision complex array of dimensions (lda, n). If up
	lo = 'U'
	* or 'u', then A must contains the upper triangular part of a symme
	tric
	* matrix, and the strictly lower triangular parts is not referenced
	.
	* If uplo = 'L' or 'l', then A contains the lower triangular part o
	f
	* a symmetric matrix, and the strictly upper triangular part is not
	* referenced.
	* lda is the leading dimension of the two-dimensional array containing
	A.
	* lda must be at least max(1, n).
	* x single precision complex array of length at least (1 + (n - 1) *
	abs(incx)).
	* On entry, x contains the n element right-hand side vector b. On e
	xit,
	* it is overwritten with the solution vector x.
	* incx specifies the storage spacing between elements of x. incx must no
	t
	* be zero.
	*
	* Output
	* ------
	* x updated to contain the solution vector x that solves op(A) * x =
	b.
	*
	* Reference: http://www.netlib.org/blas/ctrsv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if incx == 0 or if n < 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasCtrsv (char uplo, char trans, char diag, int n,
	const cuComplex *A, int lda, cuComplex *x,
	int incx);
	/*
	* void cublasCtbsv (char uplo, char trans, char diag, int n, int k,
	* const cuComplex *A, int lda, cuComplex *X, int incx)
	*
	* solves one of the systems of equations op(A)*x = b, where op(A) is eithe
	r
	* op(A) = A , op(A) = transpose(A) or op(A) = conjugate(transpose(A)).
	* b and x are n element vectors, and A is an n x n unit or non-unit,
	* upper or lower triangular band matrix with k + 1 diagonals. No test
	* for singularity or near-singularity is included in this function.
	* Such tests must be performed before calling this function.
	*
	* Input
	* -----
	* uplo specifies whether the matrix is an upper or lower triangular band
	* matrix as follows: If uplo == 'U' or 'u', A is an upper triangula
	r
	* band matrix. If uplo == 'L' or 'l', A is a lower triangular band
	* matrix.
	* trans specifies op(A). If trans == 'N' or 'n', op(A) = A. If trans == '
	T',
	* 't', op(A) = transpose(A). If trans == 'C' or 'c',
	* op(A) = conjugate(transpose(A)).
	* diag specifies whether A is unit triangular. If diag == 'U' or 'u', A
	is
	* assumed to be unit triangular; thas is, diagonal elements are not
	* read and are assumed to be unity. If diag == 'N' or 'n', A is not
	* assumed to be unit triangular.
	* n specifies the number of rows and columns of the matrix A. n must
	be
	* at least zero.
	* k specifies the number of super- or sub-diagonals. If uplo == 'U' o
	r
	* 'u', k specifies the number of super-diagonals. If uplo == 'L' or
	* 'l', k specifies the number of sub-diagonals. k must at least be
	* zero.
	* A single precision complex array of dimension (lda, n). If uplo ==
	'U' or 'u',
	* the leading (k + 1) x n part of the array A must contain the uppe
	r
	* triangular band matrix, supplied column by column, with the leadi
	ng
	* diagonal of the matrix in row (k + 1) of the array, the first sup
	er-
	* diagonal starting at position 2 in row k, and so on. The top left
	* k x k triangle of the array A is not referenced. If uplo == 'L' o
	r
	* 'l', the leading (k + 1) x n part of the array A must constain th
	e
	* lower triangular band matrix, supplied column by column, with the
	* leading diagonal of the matrix in row 1 of the array, the first
	* sub-diagonal starting at position 1 in row 2, and so on. The bott
	om
	* right k x k triangle of the array is not referenced.
	* x single precision complex array of length at least (1+(n-1)*abs(in
	cx)).
	* incx storage spacing between elements of x. It must not be zero.
	*
	* Output
	* ------
	* x updated to contain the solution vector x that solves op(A) * x =
	b.
	*
	* Reference: http://www.netlib.org/blas/ctbsv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if incx == 0, n < 0 or n > 2035
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasCtbsv (char uplo, char trans, char diag, int n, int k,
	const cuComplex *A, int lda, cuComplex *x,
	int incx);
	/*
	* void
	* cublasCtpsv (char uplo, char trans, char diag, int n, const cuComplex *A
	P,
	* cuComplex *X, int incx)
	*
	* solves one of the systems of equations op(A)*x = b, where op(A) is eithe
	r
	* op(A) = A , op(A) = transpose(A) or op(A) = conjugate(transpose)). b and
	* x are n element complex vectors, and A is an n x n unit or non-unit,
	* upper or lower triangular matrix. No test for singularity or near-singul
	arity
	* is included in this routine. Such tests must be performed before calling
	this routine.
	*
	* Input
	* -----
	* uplo specifies whether the matrix is an upper or lower triangular matr
	ix
	* as follows: If uplo == 'U' or 'u', A is an upper triangluar matri
	x.
	* If uplo == 'L' or 'l', A is a lower triangular matrix.
	* trans specifies op(A). If trans == 'N' or 'n', op(A) = A. If trans == '
	T'
	* or 't', op(A) = transpose(A). If trans == 'C' or 'c', op(A) =
	* conjugate(transpose(A)).
	* diag specifies whether A is unit triangular. If diag == 'U' or 'u', A
	is
	* assumed to be unit triangular; thas is, diagonal elements are not
	* read and are assumed to be unity. If diag == 'N' or 'n', A is not
	* assumed to be unit triangular.
	* n specifies the number of rows and columns of the matrix A. n must
	be
	* at least zero.
	* AP single precision complex array with at least ((n*(n+1))/2) elemen
	ts.
	* If uplo == 'U' or 'u', the array AP contains the upper triangular
	* matrix A, packed sequentially, column by column; that is, if i <=
	j, then
	* A[i,j] is stored is AP[i+(j*(j+1)/2)]. If uplo == 'L' or 'L', the
	* array AP contains the lower triangular matrix A, packed sequentia
	lly,
	* column by column; that is, if i >= j, then A[i,j] is stored in
	* AP[i+((2*n-j+1)*j)/2]. When diag = 'U' or 'u', the diagonal eleme
	nts
	* of A are not referenced and are assumed to be unity.
	* x single precision complex array of length at least (1+(n-1)*abs(in
	cx)).
	* incx storage spacing between elements of x. It must not be zero.
	*
	* Output
	* ------
	* x updated to contain the solution vector x that solves op(A) * x =
	b.
	*
	* Reference: http://www.netlib.org/blas/ctpsv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if incx == 0 or if n < 0 or n > 2035
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasCtpsv (char uplo, char trans, char diag, int n,
	const cuComplex *AP, cuComplex *x, int incx);
	/*		static __inline__ void CUBLASAPI cublasCgeru (int m, int n, cuComplex alpha
	* cublasCgeru (int m, int n, cuComplex alpha, const cuComplex *x, int incx		, const cuComplex *x,
	,
	* const cuComplex *y, int incy, cuComplex *A, int lda)
	*
	* performs the symmetric rank 1 operation
	*
	* A = alpha * x * transpose(y) + A,
	*
	* where alpha is a single precision complex scalar, x is an m element sing
	le
	* precision complex vector, y is an n element single precision complex vec
	tor, and A
	* is an m by n matrix consisting of single precision complex elements. Mat
	rix A
	* is stored in column major format, and lda is the leading dimension of
	* the two-dimensional array used to store A.
	*
	* Input
	* -----
	* m specifies the number of rows of the matrix A. It must be at least
	* zero.
	* n specifies the number of columns of the matrix A. It must be at
	* least zero.
	* alpha single precision complex scalar multiplier applied to x * transpo
	se(y)
	* x single precision complex array of length at least (1 + (m - 1) *
	abs(incx))
	* incx specifies the storage spacing between elements of x. incx must no
	t
	* be zero.
	* y single precision complex array of length at least (1 + (n - 1) *
	abs(incy))
	* incy specifies the storage spacing between elements of y. incy must no
	t
	* be zero.
	* A single precision complex array of dimensions (lda, n).
	* lda leading dimension of two-dimensional array used to store matrix A
	*
	* Output
	* ------
	* A updated according to A = alpha * x * transpose(y) + A
	*
	* Reference: http://www.netlib.org/blas/cgeru.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if m <0, n < 0, incx == 0, incy == 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasCgeru (int m, int n, cuComplex alpha, const cuComplex
	*x,
	int incx, const cuComplex *y, int incy,		int incx, const cuComplex *y, int incy,
	cuComplex *A, int lda);		cuComplex *A, int lda)
	/*		{
	* cublasCgerc (int m, int n, cuComplex alpha, const cuComplex *x, int incx		cublasHandle_t handle = cublasGetCurrentCtx();
	,		cublasStatus_t error = cublasCgeru_v2(handle, m, n, &alpha,
	* const cuComplex *y, int incy, cuComplex *A, int lda)		x, incx, y, incy, A, lda);
	*		cublasSetError(error);
	* performs the symmetric rank 1 operation		}
	*		static __inline__ void CUBLASAPI cublasCgerc (int m, int n, cuComplex alpha
	* A = alpha * x * conjugate(transpose(y)) + A,		, const cuComplex *x,
	*
	* where alpha is a single precision complex scalar, x is an m element sing
	le
	* precision complex vector, y is an n element single precision complex vec
	tor, and A
	* is an m by n matrix consisting of single precision complex elements. Mat
	rix A
	* is stored in column major format, and lda is the leading dimension of
	* the two-dimensional array used to store A.
	*
	* Input
	* -----
	* m specifies the number of rows of the matrix A. It must be at least
	* zero.
	* n specifies the number of columns of the matrix A. It must be at
	* least zero.
	* alpha single precision complex scalar multiplier applied to x * transpo
	se(y)
	* x single precision complex array of length at least (1 + (m - 1) *
	abs(incx))
	* incx specifies the storage spacing between elements of x. incx must no
	t
	* be zero.
	* y single precision complex array of length at least (1 + (n - 1) *
	abs(incy))
	* incy specifies the storage spacing between elements of y. incy must no
	t
	* be zero.
	* A single precision complex array of dimensions (lda, n).
	* lda leading dimension of two-dimensional array used to store matrix A
	*
	* Output
	* ------
	* A updated according to A = alpha * x * conjugate(transpose(y)) + A
	*
	* Reference: http://www.netlib.org/blas/cgerc.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if m <0, n < 0, incx == 0, incy == 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasCgerc (int m, int n, cuComplex alpha, const cuComplex
	*x,
	int incx, const cuComplex *y, int incy,		int incx, const cuComplex *y, int incy,
	cuComplex *A, int lda);		cuComplex *A, int lda)
	/*		{
	* void		cublasHandle_t handle = cublasGetCurrentCtx();
	* cublasCher (char uplo, int n, float alpha, const cuComplex *x, int incx,		cublasStatus_t error = cublasCgerc_v2(handle, m, n, &alpha,
	* cuComplex *A, int lda)		x, incx, y, incy, A, lda);
	*		cublasSetError(error);
	* performs the hermitian rank 1 operation		}
	*		static __inline__ void CUBLASAPI cublasZgeru (int m, int n, cuDoubleComplex
	* A = alpha * x * conjugate(transpose(x)) + A,		alpha, const cuDoubleComplex *x,
	*		int incx, const cuDoubleComplex *y, int incy,
	* where alpha is a single precision real scalar, x is an n element single		cuDoubleComplex *A, int lda)
	* precision complex vector and A is an n x n hermitian matrix consisting o		{
	f		cublasHandle_t handle = cublasGetCurrentCtx();
	* single precision complex elements. Matrix A is stored in column major fo		cublasStatus_t error = cublasZgeru_v2(handle, m, n, &alpha,
	rmat,		x, incx, y, incy, A, lda);
	* and lda is the leading dimension of the two-dimensional array		cublasSetError(error);
	* containing A.		}
	*		static __inline__ void CUBLASAPI cublasZgerc (int m, int n, cuDoubleComplex
	* Input		alpha, const cuDoubleComplex *x,
	* -----		int incx, const cuDoubleComplex *y, int incy,
	* uplo specifies whether the matrix data is stored in the upper or		cuDoubleComplex *A, int lda)
	* the lower triangular part of array A. If uplo = 'U' or 'u',		{
	* then only the upper triangular part of A may be referenced.		cublasHandle_t handle = cublasGetCurrentCtx();
	* If uplo = 'L' or 'l', then only the lower triangular part of		cublasStatus_t error = cublasZgerc_v2(handle, m, n, &alpha,
	* A may be referenced.		x, incx, y, incy, A, lda);
	* n specifies the number of rows and columns of the matrix A. It		cublasSetError(error);
	* must be at least 0.		}
	* alpha single precision real scalar multiplier applied to		/*------------------------------------------------------------------------*
	* x * conjugate(transpose(x))		/
	* x single precision complex array of length at least (1 + (n - 1) *		/* SYR/HER */
	abs(incx))		static __inline__ void CUBLASAPI cublasSsyr (char uplo, int n, float alpha,
	* incx specifies the storage spacing between elements of x. incx must		const float *x,
	* not be zero.		int incx, float *A, int lda)
	* A single precision complex array of dimensions (lda, n). If uplo =		{
	'U' or		cublasHandle_t handle = cublasGetCurrentCtx();
	* 'u', then A must contain the upper triangular part of a hermitian		cublasStatus_t error = cublasSsyr_v2(handle, convertToFillMode(uplo), n
	* matrix, and the strictly lower triangular part is not referenced.		, &alpha,
	* If uplo = 'L' or 'l', then A contains the lower triangular part		x, incx, A, lda);
	* of a hermitian matrix, and the strictly upper triangular part is		cublasSetError(error);
	* not referenced. The imaginary parts of the diagonal elements need		}
	* not be set, they are assumed to be zero, and on exit they		static __inline__ void CUBLASAPI cublasDsyr (char uplo, int n, double alpha
	* are set to zero.		, const double *x,
	* lda leading dimension of the two-dimensional array containing A. lda		int incx, double *A, int lda)
	* must be at least max(1, n).		{
	*		cublasHandle_t handle = cublasGetCurrentCtx();
	* Output		cublasStatus_t error = cublasDsyr_v2(handle, convertToFillMode(uplo), n
	* ------		, &alpha,
	* A updated according to A = alpha * x * conjugate(transpose(x)) + A		x, incx, A, lda);
	*		cublasSetError(error);
	* Reference: http://www.netlib.org/blas/cher.f		}
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, or incx == 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasCher (char uplo, int n, float alpha,
	const cuComplex *x, int incx, cuComplex *A,
	int lda);
	/*
	* void
	* cublasChpr (char uplo, int n, float alpha, const cuComplex *x, int incx,
	* cuComplex *AP)
	*
	* performs the hermitian rank 1 operation
	*
	* A = alpha * x * conjugate(transpose(x)) + A,
	*
	* where alpha is a single precision real scalar and x is an n element sing
	le
	* precision complex vector. A is a hermitian n x n matrix consisting of si
	ngle
	* precision complex elements that is supplied in packed form.
	*
	* Input
	* -----
	* uplo specifies whether the matrix data is stored in the upper or the l
	ower
	* triangular part of array AP. If uplo == 'U' or 'u', then the uppe
	r
	* triangular part of A is supplied in AP. If uplo == 'L' or 'l', th
	en
	* the lower triangular part of A is supplied in AP.
	* n specifies the number of rows and columns of the matrix A. It must
	be
	* at least zero.
	* alpha single precision real scalar multiplier applied to x * conjugate(
	transpose(x)).
	* x single precision array of length at least (1 + (n - 1) * abs(incx
	)).
	* incx storage spacing between elements of x. incx must not be zero.
	* AP single precision complex array with at least ((n * (n + 1)) / 2)
	elements. If
	* uplo == 'U' or 'u', the array AP contains the upper triangular pa
	rt
	* of the hermitian matrix A, packed sequentially, column by column;
	* that is, if i <= j, then A[i,j] is stored is AP[i+(j*(j+1)/2)]. I
	f
	* uplo == 'L' or 'L', the array AP contains the lower triangular pa
	rt
	* of the hermitian matrix A, packed sequentially, column by column;
	* that is, if i >= j, then A[i,j] is stored in AP[i+((2*n-j+1)*j)/2
	].
	* The imaginary parts of the diagonal elements need not be set, the
	y
	* are assumed to be zero, and on exit they are set to zero.
	*
	* Output
	* ------
	* A updated according to A = alpha * x * conjugate(transpose(x)) + A
	*
	* Reference: http://www.netlib.org/blas/chpr.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, or incx == 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasChpr (char uplo, int n, float alpha,
	const cuComplex *x, int incx, cuComplex *AP);
	/*		static __inline__ void CUBLASAPI cublasCher (char uplo, int n, float alpha,
	* void		const cuComplex *x, int incx, cuComplex *A, int
	* cublasChpr2 (char uplo, int n, cuComplex alpha, const cuComplex *x, int		lda)
	incx,		{
	* const cuComplex *y, int incy, cuComplex *AP)		cublasHandle_t handle = cublasGetCurrentCtx();
	*		cublasStatus_t error = cublasCher_v2(handle, convertToFillMode(uplo), n
	* performs the hermitian rank 2 operation		, &alpha,
	*		x, incx, A, lda);
	* A = alpha*x*conjugate(transpose(y)) + conjugate(alpha)*y*conjugate(tr		cublasSetError(error);
	anspose(x)) + A,		}
	*		static __inline__ void CUBLASAPI cublasZher (char uplo, int n, double alpha
	* where alpha is a single precision complex scalar, and x and y are n elem		,
	ent single		const cuDoubleComplex *x, int incx, cuDoubleComp
	* precision complex vectors. A is a hermitian n x n matrix consisting of s		lex *A, int lda)
	ingle		{
	* precision complex elements that is supplied in packed form.		cublasHandle_t handle = cublasGetCurrentCtx();
	*		cublasStatus_t error = cublasZher_v2(handle, convertToFillMode(uplo), n
	* Input		, &alpha,
	* -----		x, incx, A, lda);
	* uplo specifies whether the matrix data is stored in the upper or the l		cublasSetError(error);
	ower		}
	* triangular part of array A. If uplo == 'U' or 'u', then only the
	* upper triangular part of A may be referenced and the lower triang
	ular
	* part of A is inferred. If uplo == 'L' or 'l', then only the lower
	* triangular part of A may be referenced and the upper triangular p
	art
	* of A is inferred.
	* n specifies the number of rows and columns of the matrix A. It must
	be
	* at least zero.
	* alpha single precision complex scalar multiplier applied to x * conjuga
	te(transpose(y)) +
	* y * conjugate(transpose(x)).
	* x single precision complex array of length at least (1 + (n - 1) *
	abs (incx)).
	* incx storage spacing between elements of x. incx must not be zero.
	* y single precision complex array of length at least (1 + (n - 1) *
	abs (incy)).
	* incy storage spacing between elements of y. incy must not be zero.
	* AP single precision complex array with at least ((n * (n + 1)) / 2)
	elements. If
	* uplo == 'U' or 'u', the array AP contains the upper triangular pa
	rt
	* of the hermitian matrix A, packed sequentially, column by column;
	* that is, if i <= j, then A[i,j] is stored is AP[i+(j*(j+1)/2)]. I
	f
	* uplo == 'L' or 'L', the array AP contains the lower triangular pa
	rt
	* of the hermitian matrix A, packed sequentially, column by column;
	* that is, if i >= j, then A[i,j] is stored in AP[i+((2*n-j+1)*j)/2
	].
	* The imaginary parts of the diagonal elements need not be set, the
	y
	* are assumed to be zero, and on exit they are set to zero.
	*
	* Output
	* ------
	* A updated according to A = alpha*x*conjugate(transpose(y))
	* + conjugate(alpha)*y*conjugate(transpose(x
	))+A
	*
	* Reference: http://www.netlib.org/blas/chpr2.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, incx == 0, incy == 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasChpr2 (char uplo, int n, cuComplex alpha,
	const cuComplex *x, int incx, const cuComplex *
	y,
	int incy, cuComplex *AP);
	/*		/*------------------------------------------------------------------------*
	* void cublasCher2 (char uplo, int n, cuComplex alpha, const cuComplex *x,		/
	int incx,		/* SPR/HPR */
	* const cuComplex *y, int incy, cuComplex *A, int lda)		static __inline__ void CUBLASAPI cublasSspr (char uplo, int n, float alpha,
	*		const float *x,
	* performs the hermitian rank 2 operation		int incx, float *AP)
	*		{
	* A = alpha*x*conjugate(transpose(y)) + conjugate(alpha)*y*conjugate(tr		cublasHandle_t handle = cublasGetCurrentCtx();
	anspose(x)) + A,		cublasStatus_t error = cublasSspr_v2(handle, convertToFillMode(uplo), n
	*		, &alpha,
	* where alpha is a single precision complex scalar, x and y are n element		x, incx, AP);
	single		cublasSetError(error);
	* precision complex vector and A is an n by n hermitian matrix consisting		}
	of single		static __inline__ void CUBLASAPI cublasDspr (char uplo, int n, double alpha
	* precision complex elements.		, const double *x,
	*		int incx, double *AP)
	* Input		{
	* -----		cublasHandle_t handle = cublasGetCurrentCtx();
	* uplo specifies whether the matrix data is stored in the upper or the l		cublasStatus_t error = cublasDspr_v2(handle, convertToFillMode(uplo), n
	ower		, &alpha,
	* triangular part of array A. If uplo == 'U' or 'u', then only the		x, incx, AP);
	* upper triangular part of A may be referenced and the lower triang		cublasSetError(error);
	ular		}
	* part of A is inferred. If uplo == 'L' or 'l', then only the lower		static __inline__ void CUBLASAPI cublasChpr (char uplo, int n, float alpha,
	* triangular part of A may be referenced and the upper triangular p		const cuComplex *x,
	art		int incx, cuComplex *AP)
	* of A is inferred.		{
	* n specifies the number of rows and columns of the matrix A. It must		cublasHandle_t handle = cublasGetCurrentCtx();
	be		cublasStatus_t error = cublasChpr_v2(handle, convertToFillMode(uplo), n
	* at least zero.		, &alpha,
	* alpha single precision complex scalar multiplier applied to x * conjuga		x, incx, AP);
	te(transpose(y)) +		cublasSetError(error);
	* y * conjugate(transpose(x)).		}
	* x single precision array of length at least (1 + (n - 1) * abs (inc		static __inline__ void CUBLASAPI cublasZhpr (char uplo, int n, double alpha
	x)).		, const cuDoubleComplex *x,
	* incx storage spacing between elements of x. incx must not be zero.		int incx, cuDoubleComplex *AP)
	* y single precision array of length at least (1 + (n - 1) * abs (inc		{
	y)).		cublasHandle_t handle = cublasGetCurrentCtx();
	* incy storage spacing between elements of y. incy must not be zero.		cublasStatus_t error = cublasZhpr_v2(handle, convertToFillMode(uplo), n
	* A single precision complex array of dimensions (lda, n). If uplo ==		, &alpha,
	'U' or 'u',		x, incx, AP);
	* then A must contains the upper triangular part of a hermitian mat		cublasSetError(error);
	rix,		}
	* and the strictly lower triangular parts is not referenced. If upl		/*------------------------------------------------------------------------*
	o ==		/
	* 'L' or 'l', then A contains the lower triangular part of a hermit		/* SYR2/HER2 */
	ian		static __inline__ void CUBLASAPI cublasSsyr2 (char uplo, int n, float alpha
	* matrix, and the strictly upper triangular part is not referenced.		, const float *x,
	* The imaginary parts of the diagonal elements need not be set,		int incx, const float *y, int incy, float *A,
	* they are assumed to be zero, and on exit they are set to zero.		int lda)
	*		{
	* lda leading dimension of A. It must be at least max(1, n).		cublasHandle_t handle = cublasGetCurrentCtx();
	*		cublasStatus_t error = cublasSsyr2_v2(handle, convertToFillMode(uplo),
	* Output		n, &alpha,
	* ------		x, incx, y, incy, A, lda);
	* A updated according to A = alpha*x*conjugate(transpose(y))		cublasSetError(error);
	* + conjugate(alpha)*y*conjugate(transpose(x		}
	))+A		static __inline__ void CUBLASAPI cublasDsyr2 (char uplo, int n, double alph
	*		a, const double *x,
	* Reference: http://www.netlib.org/blas/cher2.f		int incx, const double *y, int incy, double *A,
	*		int lda)
	* Error status for this function can be retrieved via cublasGetError().		{
	*		cublasHandle_t handle = cublasGetCurrentCtx();
	* Error Status		cublasStatus_t error = cublasDsyr2_v2(handle, convertToFillMode(uplo),
	* ------------		n, &alpha,
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize		x, incx, y, incy, A, lda);
	d		cublasSetError(error);
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, incx == 0, incy == 0		}
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU		static __inline__ void CUBLASAPI cublasCher2 (char uplo, int n, cuComplex a
	*/		lpha, const cuComplex *x,
	void CUBLASAPI cublasCher2 (char uplo, int n, cuComplex alpha,		int incx, const cuComplex *y, int incy, cuCompl
	const cuComplex *x, int incx, const cuComplex *		ex *A,
	y,		int lda)
	int incy, cuComplex *A, int lda);		{
	void CUBLASAPI cublasChpr2 (char uplo, int n, cuComplex alpha,		cublasHandle_t handle = cublasGetCurrentCtx();
	const cuComplex *x, int incx, const cuComplex *		cublasStatus_t error = cublasCher2_v2(handle, convertToFillMode(uplo),
	y,		n, &alpha,
	int incy, cuComplex *AP);		x, incx, y, incy, A, lda);
			cublasSetError(error);
			}
			static __inline__ void CUBLASAPI cublasZher2 (char uplo, int n, cuDoubleCom
			plex alpha, const cuDoubleComplex *x,
			int incx, const cuDoubleComplex *y, int incy, c
			uDoubleComplex *A,
			int lda)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasZher2_v2(handle, convertToFillMode(uplo),
			n, &alpha,
			x, incx, y, incy, A, lda);
			cublasSetError(error);
			}
	/* ---------------- CUBLAS single precision BLAS3 functions ---------------		/*------------------------------------------------------------------------*
	- */		/
			/* SPR2/HPR2 */
			static __inline__ void CUBLASAPI cublasSspr2 (char uplo, int n, float alpha
			, const float *x,
			int incx, const float *y, int incy, float *AP)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasSspr2_v2(handle, convertToFillMode(uplo),
			n, &alpha,
			x, incx, y, incy, AP);
			cublasSetError(error);
			}
			static __inline__ void CUBLASAPI cublasDspr2 (char uplo, int n, double alph
			a,
			const double *x, int incx, const double *y,
			int incy, double *AP)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasDspr2_v2(handle, convertToFillMode(uplo),
			n, &alpha,
			x, incx, y, incy, AP);
			cublasSetError(error);
			}
			static __inline__ void CUBLASAPI cublasChpr2 (char uplo, int n, cuComplex a
			lpha,
			const cuComplex *x, int incx, const cuComplex *
			y,
			int incy, cuComplex *AP)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasChpr2_v2(handle, convertToFillMode(uplo),
			n, &alpha,
			x, incx, y, incy, AP);
			cublasSetError(error);
			}
			static __inline__ void CUBLASAPI cublasZhpr2 (char uplo, int n, cuDoubleCom
			plex alpha,
			const cuDoubleComplex *x, int incx, const cuDou
			bleComplex *y,
			int incy, cuDoubleComplex *AP)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasZhpr2_v2(handle, convertToFillMode(uplo),
			n, &alpha,
			x, incx, y, incy, AP);
			cublasSetError(error);
	/*		}
	* void		/* ------------------------BLAS3 Functions -------------------------------
	* cublasSgemm (char transa, char transb, int m, int n, int k, float alpha,		*/
	* const float *A, int lda, const float *B, int ldb, float bet		/* GEMM */
	a,		static __inline__ void CUBLASAPI cublasSgemm (char transa, char transb, int
	* float *C, int ldc)		m, int n, int k,
	*
	* computes the product of matrix A and matrix B, multiplies the result
	* by a scalar alpha, and adds the sum to the product of matrix C and
	* scalar beta. sgemm() performs one of the matrix-matrix operations:
	*
	* C = alpha * op(A) * op(B) + beta * C,
	*
	* where op(X) is one of
	*
	* op(X) = X or op(X) = transpose(X)
	*
	* alpha and beta are single precision scalars, and A, B and C are
	* matrices consisting of single precision elements, with op(A) an m x k
	* matrix, op(B) a k x n matrix, and C an m x n matrix. Matrices A, B,
	* and C are stored in column major format, and lda, ldb, and ldc are
	* the leading dimensions of the two-dimensional arrays containing A,
	* B, and C.
	*
	* Input
	* -----
	* transa specifies op(A). If transa = 'n' or 'N', op(A) = A. If
	* transa = 't', 'T', 'c', or 'C', op(A) = transpose(A)
	* transb specifies op(B). If transb = 'n' or 'N', op(B) = B. If
	* transb = 't', 'T', 'c', or 'C', op(B) = transpose(B)
	* m number of rows of matrix op(A) and rows of matrix C
	* n number of columns of matrix op(B) and number of columns of C
	* k number of columns of matrix op(A) and number of rows of op(B)
	* alpha single precision scalar multiplier applied to op(A)op(B)
	* A single precision array of dimensions (lda, k) if transa =
	* 'n' or 'N'), and of dimensions (lda, m) otherwise. When transa =
	* 'N' or 'n' then lda must be at least max( 1, m ), otherwise lda
	* must be at least max(1, k).
	* lda leading dimension of two-dimensional array used to store matrix A
	* B single precision array of dimensions (ldb, n) if transb =
	* 'n' or 'N'), and of dimensions (ldb, k) otherwise. When transb =
	* 'N' or 'n' then ldb must be at least max (1, k), otherwise ldb
	* must be at least max (1, n).
	* ldb leading dimension of two-dimensional array used to store matrix B
	* beta single precision scalar multiplier applied to C. If 0, C does
	* not have to be a valid input
	* C single precision array of dimensions (ldc, n). ldc must be at
	* least max (1, m).
	* ldc leading dimension of two-dimensional array used to store matrix C
	*
	* Output
	* ------
	* C updated based on C = alpha * op(A)*op(B) + beta * C
	*
	* Reference: http://www.netlib.org/blas/sgemm.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if any of m, n, or k are < 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasSgemm (char transa, char transb, int m, int n, int k,
	float alpha, const float *A, int lda,		float alpha, const float *A, int lda,
	const float *B, int ldb, float beta, float *C,		const float *B, int ldb, float beta, float *C,
	int ldc);		int ldc)
			{
	/*		cublasHandle_t handle = cublasGetCurrentCtx();
	* void
	* cublasSsymm (char side, char uplo, int m, int n, float alpha,
	* const float *A, int lda, const float *B, int ldb,
	* float beta, float *C, int ldc);
	*
	* performs one of the matrix-matrix operations
	*
	* C = alpha * A * B + beta * C, or
	* C = alpha * B * A + beta * C,
	*
	* where alpha and beta are single precision scalars, A is a symmetric matr
	ix
	* consisting of single precision elements and stored in either lower or up
	per
	* storage mode, and B and C are m x n matrices consisting of single precis
	ion
	* elements.
	*
	* Input
	* -----
	* side specifies whether the symmetric matrix A appears on the left side
	* hand side or right hand side of matrix B, as follows. If side ==
	'L'
	* or 'l', then C = alpha * A * B + beta * C. If side = 'R' or 'r',
	* then C = alpha * B * A + beta * C.
	* uplo specifies whether the symmetric matrix A is stored in upper or lo
	wer
	* storage mode, as follows. If uplo == 'U' or 'u', only the upper
	* triangular part of the symmetric matrix is to be referenced, and
	the
	* elements of the strictly lower triangular part are to be infered
	from
	* those in the upper triangular part. If uplo == 'L' or 'l', only t
	he
	* lower triangular part of the symmetric matrix is to be referenced
	,
	* and the elements of the strictly upper triangular part are to be
	* infered from those in the lower triangular part.
	* m specifies the number of rows of the matrix C, and the number of r
	ows
	* of matrix B. It also specifies the dimensions of symmetric matrix
	A
	* when side == 'L' or 'l'. m must be at least zero.
	* n specifies the number of columns of the matrix C, and the number o
	f
	* columns of matrix B. It also specifies the dimensions of symmetri
	c
	* matrix A when side == 'R' or 'r'. n must be at least zero.
	* alpha single precision scalar multiplier applied to A * B, or B * A
	* A single precision array of dimensions (lda, ka), where ka is m whe
	n
	* side == 'L' or 'l' and is n otherwise. If side == 'L' or 'l' the
	* leading m x m part of array A must contain the symmetric matrix,
	* such that when uplo == 'U' or 'u', the leading m x m part stores
	the
	* upper triangular part of the symmetric matrix, and the strictly l
	ower
	* triangular part of A is not referenced, and when uplo == 'U' or '
	u',
	* the leading m x m part stores the lower triangular part of the
	* symmetric matrix and the strictly upper triangular part is not
	* referenced. If side == 'R' or 'r' the leading n x n part of array
	A
	* must contain the symmetric matrix, such that when uplo == 'U' or
	'u',
	* the leading n x n part stores the upper triangular part of the
	* symmetric matrix and the strictly lower triangular part of A is n
	ot
	* referenced, and when uplo == 'U' or 'u', the leading n x n part
	* stores the lower triangular part of the symmetric matrix and the
	* strictly upper triangular part is not referenced.
	* lda leading dimension of A. When side == 'L' or 'l', it must be at le
	ast
	* max(1, m) and at least max(1, n) otherwise.
	* B single precision array of dimensions (ldb, n). On entry, the lead
	ing
	* m x n part of the array contains the matrix B.
	* ldb leading dimension of B. It must be at least max (1, m).
	* beta single precision scalar multiplier applied to C. If beta is zero,
	C
	* does not have to be a valid input
	* C single precision array of dimensions (ldc, n)
	* ldc leading dimension of C. Must be at least max(1, m)
	*
	* Output
	* ------
	* C updated according to C = alpha * A * B + beta * C, or C = alpha *
	* B * A + beta * C
	*
	* Reference: http://www.netlib.org/blas/ssymm.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if m or n are < 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasSsymm (char side, char uplo, int m, int n, float alpha
	,
	const float *A, int lda, const float *B, int ld
	b,
	float beta, float *C, int ldc);
	/*
	* void
	* cublasSsyrk (char uplo, char trans, int n, int k, float alpha,
	* const float *A, int lda, float beta, float *C, int ldc)
	*
	* performs one of the symmetric rank k operations
	*
	* C = alpha * A * transpose(A) + beta * C, or
	* C = alpha * transpose(A) * A + beta * C.
	*
	* Alpha and beta are single precision scalars. C is an n x n symmetric mat
	rix
	* consisting of single precision elements and stored in either lower or
	* upper storage mode. A is a matrix consisting of single precision element
	s
	* with dimension of n x k in the first case, and k x n in the second case.
	*
	* Input
	* -----
	* uplo specifies whether the symmetric matrix C is stored in upper or lo
	wer
	* storage mode as follows. If uplo == 'U' or 'u', only the upper
	* triangular part of the symmetric matrix is to be referenced, and
	the
	* elements of the strictly lower triangular part are to be infered
	from
	* those in the upper triangular part. If uplo == 'L' or 'l', only t
	he
	* lower triangular part of the symmetric matrix is to be referenced
	,
	* and the elements of the strictly upper triangular part are to be
	* infered from those in the lower triangular part.
	* trans specifies the operation to be performed. If trans == 'N' or 'n',
	C =
	* alpha * transpose(A) + beta * C. If trans == 'T', 't', 'C', or 'c
	',
	* C = transpose(A) * A + beta * C.
	* n specifies the number of rows and the number columns of matrix C.
	If
	* trans == 'N' or 'n', n specifies the number of rows of matrix A.
	If
	* trans == 'T', 't', 'C', or 'c', n specifies the columns of matrix
	A.
	* n must be at least zero.
	* k If trans == 'N' or 'n', k specifies the number of rows of matrix
	A.
	* If trans == 'T', 't', 'C', or 'c', k specifies the number of rows
	of
	* matrix A. k must be at least zero.
	* alpha single precision scalar multiplier applied to A * transpose(A) or
	* transpose(A) * A.
	* A single precision array of dimensions (lda, ka), where ka is k whe
	n
	* trans == 'N' or 'n', and is n otherwise. When trans == 'N' or 'n'
	,
	* the leading n x k part of array A must contain the matrix A,
	* otherwise the leading k x n part of the array must contains the
	* matrix A.
	* lda leading dimension of A. When trans == 'N' or 'n' then lda must be
	at
	* least max(1, n). Otherwise lda must be at least max(1, k).
	* beta single precision scalar multiplier applied to C. If beta izs zero
	, C
	* does not have to be a valid input
	* C single precision array of dimensions (ldc, n). If uplo == 'U' or
	'u',
	* the leading n x n triangular part of the array C must contain the
	* upper triangular part of the symmetric matrix C and the strictly
	* lower triangular part of C is not referenced. On exit, the upper
	* triangular part of C is overwritten by the upper triangular part
	of
	* the updated matrix. If uplo == 'L' or 'l', the leading n x n
	* triangular part of the array C must contain the lower triangular
	part
	* of the symmetric matrix C and the strictly upper triangular part
	of C
	* is not referenced. On exit, the lower triangular part of C is
	* overwritten by the lower triangular part of the updated matrix.
	* ldc leading dimension of C. It must be at least max(1, n).
	*
	* Output
	* ------
	* C updated according to C = alpha * A * transpose(A) + beta * C, or
	C =
	* alpha * transpose(A) * A + beta * C
	*
	* Reference: http://www.netlib.org/blas/ssyrk.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0 or k < 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasSsyrk (char uplo, char trans, int n, int k, float alph
	a,
	const float *A, int lda, float beta, float *C,
	int ldc);
	/*
	* void
	* cublasSsyr2k (char uplo, char trans, int n, int k, float alpha,
	* const float *A, int lda, const float *B, int ldb,
	* float beta, float *C, int ldc)
	*
	* performs one of the symmetric rank 2k operations
	*
	* C = alpha * A * transpose(B) + alpha * B * transpose(A) + beta * C, o
	r
	* C = alpha * transpose(A) * B + alpha * transpose(B) * A + beta * C.
	*
	* Alpha and beta are single precision scalars. C is an n x n symmetric mat
	rix
	* consisting of single precision elements and stored in either lower or up
	per
	* storage mode. A and B are matrices consisting of single precision elemen
	ts
	* with dimension of n x k in the first case, and k x n in the second case.
	*
	* Input
	* -----
	* uplo specifies whether the symmetric matrix C is stored in upper or lo
	wer
	* storage mode, as follows. If uplo == 'U' or 'u', only the upper
	* triangular part of the symmetric matrix is to be referenced, and
	the
	* elements of the strictly lower triangular part are to be infered
	from
	* those in the upper triangular part. If uplo == 'L' or 'l', only t
	he
	* lower triangular part of the symmetric matrix is to be references
	,
	* and the elements of the strictly upper triangular part are to be
	* infered from those in the lower triangular part.
	* trans specifies the operation to be performed. If trans == 'N' or 'n',
	* C = alpha * A * transpose(B) + alpha * B * transpose(A) + beta *
	C,
	* If trans == 'T', 't', 'C', or 'c', C = alpha * transpose(A) * B +
	* alpha * transpose(B) * A + beta * C.
	* n specifies the number of rows and the number columns of matrix C.
	If
	* trans == 'N' or 'n', n specifies the number of rows of matrix A.
	If
	* trans == 'T', 't', 'C', or 'c', n specifies the columns of matrix
	A.
	* n must be at least zero.
	* k If trans == 'N' or 'n', k specifies the number of rows of matrix
	A.
	* If trans == 'T', 't', 'C', or 'c', k specifies the number of rows
	of
	* matrix A. k must be at least zero.
	* alpha single precision scalar multiplier.
	* A single precision array of dimensions (lda, ka), where ka is k whe
	n
	* trans == 'N' or 'n', and is n otherwise. When trans == 'N' or 'n'
	,
	* the leading n x k part of array A must contain the matrix A,
	* otherwise the leading k x n part of the array must contain the ma
	trix
	* A.
	* lda leading dimension of A. When trans == 'N' or 'n' then lda must be
	at
	* least max(1, n). Otherwise lda must be at least max(1,k).
	* B single precision array of dimensions (lda, kb), where kb is k whe
	n
	* trans == 'N' or 'n', and is n otherwise. When trans == 'N' or 'n'
	,
	* the leading n x k part of array B must contain the matrix B,
	* otherwise the leading k x n part of the array must contain the ma
	trix
	* B.
	* ldb leading dimension of N. When trans == 'N' or 'n' then ldb must be
	at
	* least max(1, n). Otherwise ldb must be at least max(1, k).
	* beta single precision scalar multiplier applied to C. If beta is zero,
	C
	* does not have to be a valid input.
	* C single precision array of dimensions (ldc, n). If uplo == 'U' or
	'u',
	* the leading n x n triangular part of the array C must contain the
	* upper triangular part of the symmetric matrix C and the strictly
	* lower triangular part of C is not referenced. On exit, the upper
	* triangular part of C is overwritten by the upper triangular part
	of
	* the updated matrix. If uplo == 'L' or 'l', the leading n x n
	* triangular part of the array C must contain the lower triangular
	part
	* of the symmetric matrix C and the strictly upper triangular part
	of C
	* is not referenced. On exit, the lower triangular part of C is
	* overwritten by the lower triangular part of the updated matrix.
	* ldc leading dimension of C. Must be at least max(1, n).
	*
	* Output
	* ------
	* C updated according to alpha*A*transpose(B) + alpha*B*transpose(A)
	+
	* beta*C or alpha*transpose(A)*B + alpha*transpose(B)*A + beta*C
	*
	* Reference: http://www.netlib.org/blas/ssyr2k.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0 or k < 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasSsyr2k (char uplo, char trans, int n, int k, float alp
	ha,
	const float *A, int lda, const float *B, int l
	db,
	float beta, float *C, int ldc);
	/*
	* void
	* cublasStrmm (char side, char uplo, char transa, char diag, int m, int n,
	* float alpha, const float *A, int lda, const float *B, int l
	db)
	*
	* performs one of the matrix-matrix operations
	*
	* B = alpha * op(A) * B, or B = alpha * B * op(A)
	*
	* where alpha is a single-precision scalar, B is an m x n matrix composed
	* of single precision elements, and A is a unit or non-unit, upper or lowe
	r,
	* triangular matrix composed of single precision elements. op(A) is one of
	*
	* op(A) = A or op(A) = transpose(A)
	*
	* Matrices A and B are stored in column major format, and lda and ldb are
	* the leading dimensions of the two-dimensonials arrays that contain A and
	* B, respectively.
	*
	* Input
	* -----
	* side specifies whether op(A) multiplies B from the left or right.
	* If side = 'L' or 'l', then B = alpha * op(A) * B. If side =
	* 'R' or 'r', then B = alpha * B * op(A).
	* uplo specifies whether the matrix A is an upper or lower triangular
	* matrix. If uplo = 'U' or 'u', A is an upper triangular matrix.
	* If uplo = 'L' or 'l', A is a lower triangular matrix.
	* transa specifies the form of op(A) to be used in the matrix
	* multiplication. If transa = 'N' or 'n', then op(A) = A. If
	* transa = 'T', 't', 'C', or 'c', then op(A) = transpose(A).
	* diag specifies whether or not A is unit triangular. If diag = 'U'
	* or 'u', A is assumed to be unit triangular. If diag = 'N' or
	* 'n', A is not assumed to be unit triangular.
	* m the number of rows of matrix B. m must be at least zero.
	* n the number of columns of matrix B. n must be at least zero.
	* alpha single precision scalar multiplier applied to op(A)*B, or
	* B*op(A), respectively. If alpha is zero no accesses are made
	* to matrix A, and no read accesses are made to matrix B.
	* A single precision array of dimensions (lda, k). k = m if side =
	* 'L' or 'l', k = n if side = 'R' or 'r'. If uplo = 'U' or 'u'
	* the leading k x k upper triangular part of the array A must
	* contain the upper triangular matrix, and the strictly lower
	* triangular part of A is not referenced. If uplo = 'L' or 'l'
	* the leading k x k lower triangular part of the array A must
	* contain the lower triangular matrix, and the strictly upper
	* triangular part of A is not referenced. When diag = 'U' or 'u'
	* the diagonal elements of A are no referenced and are assumed
	* to be unity.
	* lda leading dimension of A. When side = 'L' or 'l', it must be at
	* least max(1,m) and at least max(1,n) otherwise
	* B single precision array of dimensions (ldb, n). On entry, the
	* leading m x n part of the array contains the matrix B. It is
	* overwritten with the transformed matrix on exit.
	* ldb leading dimension of B. It must be at least max (1, m).
	*
	* Output
	* ------
	* B updated according to B = alpha * op(A) * B or B = alpha * B * op
	(A)
	*
	* Reference: http://www.netlib.org/blas/strmm.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if m or n < 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasStrmm (char side, char uplo, char transa, char diag,
	int m, int n, float alpha, const float *A, int
	lda,
	float *B, int ldb);
	/*
	* void
	* cublasStrsm (char side, char uplo, char transa, char diag, int m, int n,
	* float alpha, const float *A, int lda, float *B, int ldb)
	*
	* solves one of the matrix equations
	*
	* op(A) * X = alpha * B, or X * op(A) = alpha * B,
	*
	* where alpha is a single precision scalar, and X and B are m x n matrices
	* that are composed of single precision elements. A is a unit or non-unit,
	* upper or lower triangular matrix, and op(A) is one of
	*
	* op(A) = A or op(A) = transpose(A)
	*
	* The result matrix X overwrites input matrix B; that is, on exit the resu
	lt
	* is stored in B. Matrices A and B are stored in column major format, and
	* lda and ldb are the leading dimensions of the two-dimensonials arrays th
	at
	* contain A and B, respectively.
	*
	* Input
	* -----
	* side specifies whether op(A) appears on the left or right of X as
	* follows: side = 'L' or 'l' indicates solve op(A) * X = alpha * B.
	* side = 'R' or 'r' indicates solve X * op(A) = alpha * B.
	* uplo specifies whether the matrix A is an upper or lower triangular
	* matrix as follows: uplo = 'U' or 'u' indicates A is an upper
	* triangular matrix. uplo = 'L' or 'l' indicates A is a lower
	* triangular matrix.
	* transa specifies the form of op(A) to be used in matrix multiplication
	* as follows: If transa = 'N' or 'N', then op(A) = A. If transa =
	* 'T', 't', 'C', or 'c', then op(A) = transpose(A).
	* diag specifies whether or not A is a unit triangular matrix like so:
	* if diag = 'U' or 'u', A is assumed to be unit triangular. If
	* diag = 'N' or 'n', then A is not assumed to be unit triangular.
	* m specifies the number of rows of B. m must be at least zero.
	* n specifies the number of columns of B. n must be at least zero.
	* alpha is a single precision scalar to be multiplied with B. When alpha
	is
	* zero, then A is not referenced and B need not be set before entry
	.
	* A is a single precision array of dimensions (lda, k), where k is
	* m when side = 'L' or 'l', and is n when side = 'R' or 'r'. If
	* uplo = 'U' or 'u', the leading k x k upper triangular part of
	* the array A must contain the upper triangular matrix and the
	* strictly lower triangular matrix of A is not referenced. When
	* uplo = 'L' or 'l', the leading k x k lower triangular part of
	* the array A must contain the lower triangular matrix and the
	* strictly upper triangular part of A is not referenced. Note that
	* when diag = 'U' or 'u', the diagonal elements of A are not
	* referenced, and are assumed to be unity.
	* lda is the leading dimension of the two dimensional array containing
	A.
	* When side = 'L' or 'l' then lda must be at least max(1, m), when
	* side = 'R' or 'r' then lda must be at least max(1, n).
	* B is a single precision array of dimensions (ldb, n). ldb must be
	* at least max (1,m). The leading m x n part of the array B must
	* contain the right-hand side matrix B. On exit B is overwritten
	* by the solution matrix X.
	* ldb is the leading dimension of the two dimensional array containing
	B.
	* ldb must be at least max(1, m).
	*
	* Output
	* ------
	* B contains the solution matrix X satisfying op(A) * X = alpha * B,
	* or X * op(A) = alpha * B
	*
	* Reference: http://www.netlib.org/blas/strsm.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if m or n < 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasStrsm (char side, char uplo, char transa, char diag,
	int m, int n, float alpha, const float *A, int
	lda,
	float *B, int ldb);
	/* ----------------- CUBLAS single complex BLAS3 functions ----------------
	- */
	/*		cublasStatus_t error = cublasSgemm_v2(handle, convertToOp(transa), conve
	* void cublasCgemm (char transa, char transb, int m, int n, int k,		rtToOp(transb),
	* cuComplex alpha, const cuComplex *A, int lda,		m, n, k, &alpha, A, lda, B, ldb, &beta,
	* const cuComplex *B, int ldb, cuComplex beta,		C, ldc);
	* cuComplex *C, int ldc)		cublasSetError(error);
	*		}
	* performs one of the matrix-matrix operations		static __inline__ void CUBLASAPI cublasDgemm (char transa, char transb, int
	*		m, int n, int k,
	* C = alpha * op(A) * op(B) + beta*C,		double alpha, const double *A, int lda,
	*		const double *B, int ldb, double beta, double *
	* where op(X) is one of		C,
	*		int ldc)
	* op(X) = X or op(X) = transpose or op(X) = conjg(transpose(X))		{
	*		cublasHandle_t handle = cublasGetCurrentCtx();
	* alpha and beta are single-complex scalars, and A, B and C are matrices		cublasStatus_t error = cublasDgemm_v2(handle, convertToOp(transa), conv
	* consisting of single-complex elements, with op(A) an m x k matrix, op(B)		ertToOp(transb),
	* a k x n matrix and C an m x n matrix.		m, n, k, &alpha, A, lda, B, ldb, &beta,
	*		C, ldc);
	* Input		cublasSetError(error);
	* -----		}
	* transa specifies op(A). If transa == 'N' or 'n', op(A) = A. If transa ==		static __inline__ void CUBLASAPI cublasCgemm (char transa, char transb, int
	* 'T' or 't', op(A) = transpose(A). If transa == 'C' or 'c', op(A)		m, int n, int k,
	=
	* conjg(transpose(A)).
	* transb specifies op(B). If transa == 'N' or 'n', op(B) = B. If transb ==
	* 'T' or 't', op(B) = transpose(B). If transb == 'C' or 'c', op(B)
	=
	* conjg(transpose(B)).
	* m number of rows of matrix op(A) and rows of matrix C. It must be a
	t
	* least zero.
	* n number of columns of matrix op(B) and number of columns of C. It
	* must be at least zero.
	* k number of columns of matrix op(A) and number of rows of op(B). It
	* must be at least zero.
	* alpha single-complex scalar multiplier applied to op(A)op(B)
	* A single-complex array of dimensions (lda, k) if transa == 'N' or
	* 'n'), and of dimensions (lda, m) otherwise.
	* lda leading dimension of A. When transa == 'N' or 'n', it must be at
	* least max(1, m) and at least max(1, k) otherwise.
	* B single-complex array of dimensions (ldb, n) if transb == 'N' or '
	n',
	* and of dimensions (ldb, k) otherwise
	* ldb leading dimension of B. When transb == 'N' or 'n', it must be at
	* least max(1, k) and at least max(1, n) otherwise.
	* beta single-complex scalar multiplier applied to C. If beta is zero, C
	* does not have to be a valid input.
	* C single precision array of dimensions (ldc, n)
	* ldc leading dimension of C. Must be at least max(1, m).
	*
	* Output
	* ------
	* C updated according to C = alpha*op(A)*op(B) + beta*C
	*
	* Reference: http://www.netlib.org/blas/cgemm.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if any of m, n, or k are < 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasCgemm (char transa, char transb, int m, int n, int k,
	cuComplex alpha, const cuComplex *A, int lda,
	const cuComplex *B, int ldb, cuComplex beta,
	cuComplex *C, int ldc);
	/*
	* void
	* cublasCsymm (char side, char uplo, int m, int n, cuComplex alpha,
	* const cuComplex *A, int lda, const cuComplex *B, int ldb,
	* cuComplex beta, cuComplex *C, int ldc);
	*
	* performs one of the matrix-matrix operations
	*
	* C = alpha * A * B + beta * C, or
	* C = alpha * B * A + beta * C,
	*
	* where alpha and beta are single precision complex scalars, A is a symmet
	ric matrix
	* consisting of single precision complex elements and stored in either low
	er or upper
	* storage mode, and B and C are m x n matrices consisting of single precis
	ion
	* complex elements.
	*
	* Input
	* -----
	* side specifies whether the symmetric matrix A appears on the left side
	* hand side or right hand side of matrix B, as follows. If side ==
	'L'
	* or 'l', then C = alpha * A * B + beta * C. If side = 'R' or 'r',
	* then C = alpha * B * A + beta * C.
	* uplo specifies whether the symmetric matrix A is stored in upper or lo
	wer
	* storage mode, as follows. If uplo == 'U' or 'u', only the upper
	* triangular part of the symmetric matrix is to be referenced, and
	the
	* elements of the strictly lower triangular part are to be infered
	from
	* those in the upper triangular part. If uplo == 'L' or 'l', only t
	he
	* lower triangular part of the symmetric matrix is to be referenced
	,
	* and the elements of the strictly upper triangular part are to be
	* infered from those in the lower triangular part.
	* m specifies the number of rows of the matrix C, and the number of r
	ows
	* of matrix B. It also specifies the dimensions of symmetric matrix
	A
	* when side == 'L' or 'l'. m must be at least zero.
	* n specifies the number of columns of the matrix C, and the number o
	f
	* columns of matrix B. It also specifies the dimensions of symmetri
	c
	* matrix A when side == 'R' or 'r'. n must be at least zero.
	* alpha single precision scalar multiplier applied to A * B, or B * A
	* A single precision array of dimensions (lda, ka), where ka is m whe
	n
	* side == 'L' or 'l' and is n otherwise. If side == 'L' or 'l' the
	* leading m x m part of array A must contain the symmetric matrix,
	* such that when uplo == 'U' or 'u', the leading m x m part stores
	the
	* upper triangular part of the symmetric matrix, and the strictly l
	ower
	* triangular part of A is not referenced, and when uplo == 'U' or '
	u',
	* the leading m x m part stores the lower triangular part of the
	* symmetric matrix and the strictly upper triangular part is not
	* referenced. If side == 'R' or 'r' the leading n x n part of array
	A
	* must contain the symmetric matrix, such that when uplo == 'U' or
	'u',
	* the leading n x n part stores the upper triangular part of the
	* symmetric matrix and the strictly lower triangular part of A is n
	ot
	* referenced, and when uplo == 'U' or 'u', the leading n x n part
	* stores the lower triangular part of the symmetric matrix and the
	* strictly upper triangular part is not referenced.
	* lda leading dimension of A. When side == 'L' or 'l', it must be at le
	ast
	* max(1, m) and at least max(1, n) otherwise.
	* B single precision array of dimensions (ldb, n). On entry, the lead
	ing
	* m x n part of the array contains the matrix B.
	* ldb leading dimension of B. It must be at least max (1, m).
	* beta single precision scalar multiplier applied to C. If beta is zero,
	C
	* does not have to be a valid input
	* C single precision array of dimensions (ldc, n)
	* ldc leading dimension of C. Must be at least max(1, m)
	*
	* Output
	* ------
	* C updated according to C = alpha * A * B + beta * C, or C = alpha *
	* B * A + beta * C
	*
	* Reference: http://www.netlib.org/blas/csymm.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if m or n are < 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasCsymm (char side, char uplo, int m, int n,
	cuComplex alpha, const cuComplex *A, int lda,
	const cuComplex *B, int ldb, cuComplex beta,
	cuComplex *C, int ldc);
	/*
	* void
	* cublasChemm (char side, char uplo, int m, int n, cuComplex alpha,
	* const cuComplex *A, int lda, const cuComplex *B, int ldb,
	* cuComplex beta, cuComplex *C, int ldc);
	*
	* performs one of the matrix-matrix operations
	*
	* C = alpha * A * B + beta * C, or
	* C = alpha * B * A + beta * C,
	*
	* where alpha and beta are single precision complex scalars, A is a hermit
	ian matrix
	* consisting of single precision complex elements and stored in either low
	er or upper
	* storage mode, and B and C are m x n matrices consisting of single precis
	ion
	* complex elements.
	*
	* Input
	* -----
	* side specifies whether the hermitian matrix A appears on the left side
	* hand side or right hand side of matrix B, as follows. If side ==
	'L'
	* or 'l', then C = alpha * A * B + beta * C. If side = 'R' or 'r',
	* then C = alpha * B * A + beta * C.
	* uplo specifies whether the hermitian matrix A is stored in upper or lo
	wer
	* storage mode, as follows. If uplo == 'U' or 'u', only the upper
	* triangular part of the hermitian matrix is to be referenced, and
	the
	* elements of the strictly lower triangular part are to be infered
	from
	* those in the upper triangular part. If uplo == 'L' or 'l', only t
	he
	* lower triangular part of the hermitian matrix is to be referenced
	,
	* and the elements of the strictly upper triangular part are to be
	* infered from those in the lower triangular part.
	* m specifies the number of rows of the matrix C, and the number of r
	ows
	* of matrix B. It also specifies the dimensions of hermitian matrix
	A
	* when side == 'L' or 'l'. m must be at least zero.
	* n specifies the number of columns of the matrix C, and the number o
	f
	* columns of matrix B. It also specifies the dimensions of hermitia
	n
	* matrix A when side == 'R' or 'r'. n must be at least zero.
	* alpha single precision complex scalar multiplier applied to A * B, or B
	* A
	* A single precision complex array of dimensions (lda, ka), where ka
	is m when
	* side == 'L' or 'l' and is n otherwise. If side == 'L' or 'l' the
	* leading m x m part of array A must contain the hermitian matrix,
	* such that when uplo == 'U' or 'u', the leading m x m part stores
	the
	* upper triangular part of the hermitian matrix, and the strictly l
	ower
	* triangular part of A is not referenced, and when uplo == 'U' or '
	u',
	* the leading m x m part stores the lower triangular part of the
	* hermitian matrix and the strictly upper triangular part is not
	* referenced. If side == 'R' or 'r' the leading n x n part of array
	A
	* must contain the hermitian matrix, such that when uplo == 'U' or
	'u',
	* the leading n x n part stores the upper triangular part of the
	* hermitian matrix and the strictly lower triangular part of A is n
	ot
	* referenced, and when uplo == 'U' or 'u', the leading n x n part
	* stores the lower triangular part of the hermitian matrix and the
	* strictly upper triangular part is not referenced. The imaginary p
	arts
	* of the diagonal elements need not be set, they are assumed to be
	zero.
	* lda leading dimension of A. When side == 'L' or 'l', it must be at le
	ast
	* max(1, m) and at least max(1, n) otherwise.
	* B single precision complex array of dimensions (ldb, n). On entry,
	the leading
	* m x n part of the array contains the matrix B.
	* ldb leading dimension of B. It must be at least max (1, m).
	* beta single precision complex scalar multiplier applied to C. If beta
	is zero, C
	* does not have to be a valid input
	* C single precision complex array of dimensions (ldc, n)
	* ldc leading dimension of C. Must be at least max(1, m)
	*
	* Output
	* ------
	* C updated according to C = alpha * A * B + beta * C, or C = alpha *
	* B * A + beta * C
	*
	* Reference: http://www.netlib.org/blas/chemm.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if m or n are < 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasChemm (char side, char uplo, int m, int n,
	cuComplex alpha, const cuComplex *A, int lda,		cuComplex alpha, const cuComplex *A, int lda,
	const cuComplex *B, int ldb, cuComplex beta,		const cuComplex *B, int ldb, cuComplex beta,
	cuComplex *C, int ldc);		cuComplex *C, int ldc)
	/*		{
	* void		cublasHandle_t handle = cublasGetCurrentCtx();
	* cublasCsyrk (char uplo, char trans, int n, int k, cuComplex alpha,		cublasStatus_t error = cublasCgemm_v2(handle, convertToOp(transa), conv
	* const cuComplex *A, int lda, cuComplex beta, cuComplex *C,		ertToOp(transb),
	int ldc)		m, n, k, &alpha, A, lda, B, ldb, &beta,
	*		C, ldc);
	* performs one of the symmetric rank k operations		cublasSetError(error);
	*
	* C = alpha * A * transpose(A) + beta * C, or
	* C = alpha * transpose(A) * A + beta * C.
	*
	* Alpha and beta are single precision complex scalars. C is an n x n symme
	tric matrix
	* consisting of single precision complex elements and stored in either low
	er or
	* upper storage mode. A is a matrix consisting of single precision complex
	elements
	* with dimension of n x k in the first case, and k x n in the second case.
	*
	* Input
	* -----
	* uplo specifies whether the symmetric matrix C is stored in upper or lo
	wer
	* storage mode as follows. If uplo == 'U' or 'u', only the upper
	* triangular part of the symmetric matrix is to be referenced, and
	the
	* elements of the strictly lower triangular part are to be infered
	from
	* those in the upper triangular part. If uplo == 'L' or 'l', only t
	he
	* lower triangular part of the symmetric matrix is to be referenced
	,
	* and the elements of the strictly upper triangular part are to be
	* infered from those in the lower triangular part.
	* trans specifies the operation to be performed. If trans == 'N' or 'n',
	C =
	* alpha * transpose(A) + beta * C. If trans == 'T', 't', 'C', or 'c
	',
	* C = transpose(A) * A + beta * C.
	* n specifies the number of rows and the number columns of matrix C.
	If
	* trans == 'N' or 'n', n specifies the number of rows of matrix A.
	If
	* trans == 'T', 't', 'C', or 'c', n specifies the columns of matrix
	A.
	* n must be at least zero.
	* k If trans == 'N' or 'n', k specifies the number of rows of matrix
	A.
	* If trans == 'T', 't', 'C', or 'c', k specifies the number of rows
	of
	* matrix A. k must be at least zero.
	* alpha single precision complex scalar multiplier applied to A * transpo
	se(A) or
	* transpose(A) * A.
	* A single precision complex array of dimensions (lda, ka), where ka
	is k when
	* trans == 'N' or 'n', and is n otherwise. When trans == 'N' or 'n'
	,
	* the leading n x k part of array A must contain the matrix A,
	* otherwise the leading k x n part of the array must contains the
	* matrix A.
	* lda leading dimension of A. When trans == 'N' or 'n' then lda must be
	at
	* least max(1, n). Otherwise lda must be at least max(1, k).
	* beta single precision complex scalar multiplier applied to C. If beta
	izs zero, C
	* does not have to be a valid input
	* C single precision complex array of dimensions (ldc, n). If uplo =
	'U' or 'u',
	* the leading n x n triangular part of the array C must contain the
	* upper triangular part of the symmetric matrix C and the strictly
	* lower triangular part of C is not referenced. On exit, the upper
	* triangular part of C is overwritten by the upper triangular part
	of
	* the updated matrix. If uplo = 'L' or 'l', the leading n x n
	* triangular part of the array C must contain the lower triangular
	part
	* of the symmetric matrix C and the strictly upper triangular part
	of C
	* is not referenced. On exit, the lower triangular part of C is
	* overwritten by the lower triangular part of the updated matrix.
	* ldc leading dimension of C. It must be at least max(1, n).
	*
	* Output
	* ------
	* C updated according to C = alpha * A * transpose(A) + beta * C, or
	C =
	* alpha * transpose(A) * A + beta * C
	*
	* Reference: http://www.netlib.org/blas/csyrk.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0 or k < 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasCsyrk (char uplo, char trans, int n, int k,
	cuComplex alpha, const cuComplex *A, int lda,
	cuComplex beta, cuComplex *C, int ldc);
	/*
	* void
	* cublasCherk (char uplo, char trans, int n, int k, float alpha,
	* const cuComplex *A, int lda, float beta, cuComplex *C, int
	ldc)
	*
	* performs one of the hermitian rank k operations
	*
	* C = alpha * A * conjugate(transpose(A)) + beta * C, or
	* C = alpha * conjugate(transpose(A)) * A + beta * C.
	*
	* Alpha and beta are single precision real scalars. C is an n x n hermitia
	n matrix
	* consisting of single precision complex elements and stored in either low
	er or
	* upper storage mode. A is a matrix consisting of single precision complex
	elements
	* with dimension of n x k in the first case, and k x n in the second case.
	*
	* Input
	* -----
	* uplo specifies whether the hermitian matrix C is stored in upper or lo
	wer
	* storage mode as follows. If uplo == 'U' or 'u', only the upper
	* triangular part of the hermitian matrix is to be referenced, and
	the
	* elements of the strictly lower triangular part are to be infered
	from
	* those in the upper triangular part. If uplo == 'L' or 'l', only t
	he
	* lower triangular part of the hermitian matrix is to be referenced
	,
	* and the elements of the strictly upper triangular part are to be
	* infered from those in the lower triangular part.
	* trans specifies the operation to be performed. If trans == 'N' or 'n',
	C =
	* alpha * A * conjugate(transpose(A)) + beta * C. If trans == 'T',
	't', 'C', or 'c',
	* C = alpha * conjugate(transpose(A)) * A + beta * C.
	* n specifies the number of rows and the number columns of matrix C.
	If
	* trans == 'N' or 'n', n specifies the number of rows of matrix A.
	If
	* trans == 'T', 't', 'C', or 'c', n specifies the columns of matrix
	A.
	* n must be at least zero.
	* k If trans == 'N' or 'n', k specifies the number of columns of matr
	ix A.
	* If trans == 'T', 't', 'C', or 'c', k specifies the number of rows
	of
	* matrix A. k must be at least zero.
	* alpha single precision scalar multiplier applied to A * conjugate(trans
	pose(A)) or
	* conjugate(transpose(A)) * A.
	* A single precision complex array of dimensions (lda, ka), where ka
	is k when
	* trans == 'N' or 'n', and is n otherwise. When trans == 'N' or 'n'
	,
	* the leading n x k part of array A must contain the matrix A,
	* otherwise the leading k x n part of the array must contains the
	* matrix A.
	* lda leading dimension of A. When trans == 'N' or 'n' then lda must be
	at
	* least max(1, n). Otherwise lda must be at least max(1, k).
	* beta single precision scalar multiplier applied to C. If beta is zero,
	C
	* does not have to be a valid input.
	* C single precision complex array of dimensions (ldc, n). If uplo =
	'U' or 'u',
	* the leading n x n triangular part of the array C must contain the
	* upper triangular part of the hermitian matrix C and the strictly
	* lower triangular part of C is not referenced. On exit, the upper
	* triangular part of C is overwritten by the upper triangular part
	of
	* the updated matrix. If uplo = 'L' or 'l', the leading n x n
	* triangular part of the array C must contain the lower triangular
	part
	* of the hermitian matrix C and the strictly upper triangular part
	of C
	* is not referenced. On exit, the lower triangular part of C is
	* overwritten by the lower triangular part of the updated matrix.
	* The imaginary parts of the diagonal elements need
	* not be set, they are assumed to be zero, and on exit they
	* are set to zero.
	* ldc leading dimension of C. It must be at least max(1, n).
	*
	* Output
	* ------
	* C updated according to C = alpha * A * conjugate(transpose(A)) + be
	ta * C, or C =
	* alpha * conjugate(transpose(A)) * A + beta * C
	*
	* Reference: http://www.netlib.org/blas/cherk.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0 or k < 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasCherk (char uplo, char trans, int n, int k,
	float alpha, const cuComplex *A, int lda,
	float beta, cuComplex *C, int ldc);
	/*
	* void
	* cublasCsyr2k (char uplo, char trans, int n, int k, cuComplex alpha,
	* const cuComplex *A, int lda, const cuComplex *B, int ldb,
	* cuComplex beta, cuComplex *C, int ldc)
	*
	* performs one of the symmetric rank 2k operations
	*
	* C = alpha * A * transpose(B) + alpha * B * transpose(A) + beta * C, o
	r
	* C = alpha * transpose(A) * B + alpha * transpose(B) * A + beta * C.
	*
	* Alpha and beta are single precision complex scalars. C is an n x n symme
	tric matrix
	* consisting of single precision complex elements and stored in either low
	er or upper
	* storage mode. A and B are matrices consisting of single precision comple
	x elements
	* with dimension of n x k in the first case, and k x n in the second case.
	*
	* Input
	* -----
	* uplo specifies whether the symmetric matrix C is stored in upper or lo
	wer
	* storage mode, as follows. If uplo == 'U' or 'u', only the upper
	* triangular part of the symmetric matrix is to be referenced, and
	the
	* elements of the strictly lower triangular part are to be infered
	from
	* those in the upper triangular part. If uplo == 'L' or 'l', only t
	he
	* lower triangular part of the symmetric matrix is to be references
	,
	* and the elements of the strictly upper triangular part are to be
	* infered from those in the lower triangular part.
	* trans specifies the operation to be performed. If trans == 'N' or 'n',
	* C = alpha * A * transpose(B) + alpha * B * transpose(A) + beta *
	C,
	* If trans == 'T', 't', 'C', or 'c', C = alpha * transpose(A) * B +
	* alpha * transpose(B) * A + beta * C.
	* n specifies the number of rows and the number columns of matrix C.
	If
	* trans == 'N' or 'n', n specifies the number of rows of matrix A.
	If
	* trans == 'T', 't', 'C', or 'c', n specifies the columns of matrix
	A.
	* n must be at least zero.
	* k If trans == 'N' or 'n', k specifies the number of rows of matrix
	A.
	* If trans == 'T', 't', 'C', or 'c', k specifies the number of rows
	of
	* matrix A. k must be at least zero.
	* alpha single precision complex scalar multiplier.
	* A single precision complex array of dimensions (lda, ka), where ka
	is k when
	* trans == 'N' or 'n', and is n otherwise. When trans == 'N' or 'n'
	,
	* the leading n x k part of array A must contain the matrix A,
	* otherwise the leading k x n part of the array must contain the ma
	trix
	* A.
	* lda leading dimension of A. When trans == 'N' or 'n' then lda must be
	at
	* least max(1, n). Otherwise lda must be at least max(1,k).
	* B single precision complex array of dimensions (lda, kb), where kb
	is k when
	* trans == 'N' or 'n', and is n otherwise. When trans == 'N' or 'n'
	,
	* the leading n x k part of array B must contain the matrix B,
	* otherwise the leading k x n part of the array must contain the ma
	trix
	* B.
	* ldb leading dimension of N. When trans == 'N' or 'n' then ldb must be
	at
	* least max(1, n). Otherwise ldb must be at least max(1, k).
	* beta single precision complex scalar multiplier applied to C. If beta
	is zero, C
	* does not have to be a valid input.
	* C single precision complex array of dimensions (ldc, n). If uplo ==
	'U' or 'u',
	* the leading n x n triangular part of the array C must contain the
	* upper triangular part of the symmetric matrix C and the strictly
	* lower triangular part of C is not referenced. On exit, the upper
	* triangular part of C is overwritten by the upper triangular part
	of
	* the updated matrix. If uplo == 'L' or 'l', the leading n x n
	* triangular part of the array C must contain the lower triangular
	part
	* of the symmetric matrix C and the strictly upper triangular part
	of C
	* is not referenced. On exit, the lower triangular part of C is
	* overwritten by the lower triangular part of the updated matrix.
	* ldc leading dimension of C. Must be at least max(1, n).
	*
	* Output
	* ------
	* C updated according to alpha*A*transpose(B) + alpha*B*transpose(A)
	+
	* beta*C or alpha*transpose(A)*B + alpha*transpose(B)*A + beta*C
	*
	* Reference: http://www.netlib.org/blas/csyr2k.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0 or k < 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasCsyr2k (char uplo, char trans, int n, int k,
	cuComplex alpha, const cuComplex *A, int lda,
	const cuComplex *B, int ldb, cuComplex beta,
	cuComplex *C, int ldc);
	/*
	* void
	* cublasCher2k (char uplo, char trans, int n, int k, cuComplex alpha,
	* const cuComplex *A, int lda, const cuComplex *B, int ldb,
	* float beta, cuComplex *C, int ldc)
	*
	* performs one of the hermitian rank 2k operations
	*
	* C = alpha * A * conjugate(transpose(B))
	* + conjugate(alpha) * B * conjugate(transpose(A))
	* + beta * C ,
	* or
	* C = alpha * conjugate(transpose(A)) * B
	* + conjugate(alpha) * conjugate(transpose(B)) * A
	* + beta * C.
	*
	* Alpha is single precision complex scalar whereas Beta is a single preoci
	sion real scalar.
	* C is an n x n hermitian matrix consisting of single precision complex el
	ements
	* and stored in either lower or upper storage mode. A and B are matrices c
	onsisting
	* of single precision complex elements with dimension of n x k in the firs
	t case,
	* and k x n in the second case.
	*
	* Input
	* -----
	* uplo specifies whether the hermitian matrix C is stored in upper or lo
	wer
	* storage mode, as follows. If uplo == 'U' or 'u', only the upper
	* triangular part of the hermitian matrix is to be referenced, and
	the
	* elements of the strictly lower triangular part are to be infered
	from
	* those in the upper triangular part. If uplo == 'L' or 'l', only t
	he
	* lower triangular part of the hermitian matrix is to be references
	,
	* and the elements of the strictly upper triangular part are to be
	* infered from those in the lower triangular part.
	* trans specifies the operation to be performed. If trans == 'N' or 'n',
	* C = alpha * A * conjugate(transpose(B))
	* + conjugate(alpha) * B * conjugate(transpose(A))
	* + beta * C .
	* If trans == 'T', 't', 'C', or 'c',
	* C = alpha * conjugate(transpose(A)) * B
	* + conjugate(alpha) * conjugate(transpose(B)) * A
	* + beta * C.
	* n specifies the number of rows and the number columns of matrix C.
	If
	* trans == 'N' or 'n', n specifies the number of rows of matrix A.
	If
	* trans == 'T', 't', 'C', or 'c', n specifies the columns of matrix
	A.
	* n must be at least zero.
	* k If trans == 'N' or 'n', k specifies the number of rows of matrix
	A.
	* If trans == 'T', 't', 'C', or 'c', k specifies the number of rows
	of
	* matrix A. k must be at least zero.
	* alpha single precision complex scalar multiplier.
	* A single precision complex array of dimensions (lda, ka), where ka
	is k when
	* trans == 'N' or 'n', and is n otherwise. When trans == 'N' or 'n'
	,
	* the leading n x k part of array A must contain the matrix A,
	* otherwise the leading k x n part of the array must contain the ma
	trix
	* A.
	* lda leading dimension of A. When trans == 'N' or 'n' then lda must be
	at
	* least max(1, n). Otherwise lda must be at least max(1,k).
	* B single precision complex array of dimensions (lda, kb), where kb
	is k when
	* trans == 'N' or 'n', and is n otherwise. When trans == 'N' or 'n'
	,
	* the leading n x k part of array B must contain the matrix B,
	* otherwise the leading k x n part of the array must contain the ma
	trix
	* B.
	* ldb leading dimension of N. When trans == 'N' or 'n' then ldb must be
	at
	* least max(1, n). Otherwise ldb must be at least max(1, k).
	* beta single precision scalar multiplier applied to C. If beta is zero,
	C
	* does not have to be a valid input.
	* C single precision complex array of dimensions (ldc, n). If uplo ==
	'U' or 'u',
	* the leading n x n triangular part of the array C must contain the
	* upper triangular part of the hermitian matrix C and the strictly
	* lower triangular part of C is not referenced. On exit, the upper
	* triangular part of C is overwritten by the upper triangular part
	of
	* the updated matrix. If uplo == 'L' or 'l', the leading n x n
	* triangular part of the array C must contain the lower triangular
	part
	* of the hermitian matrix C and the strictly upper triangular part
	of C
	* is not referenced. On exit, the lower triangular part of C is
	* overwritten by the lower triangular part of the updated matrix.
	* The imaginary parts of the diagonal elements need
	* not be set, they are assumed to be zero, and on exit they
	* are set to zero.
	* ldc leading dimension of C. Must be at least max(1, n).
	*
	* Output
	* ------
	* C updated according to alpha*A*conjugate(transpose(B)) +
	* + conjugate(alpha)*B*conjugate(transpose(A)) + beta*C or
	* alpha*conjugate(transpose(A))*B + conjugate(alpha)*conjugate(tran
	spose(B))*A
	* + beta*C.
	*
	* Reference: http://www.netlib.org/blas/cher2k.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0 or k < 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasCher2k (char uplo, char trans, int n, int k,
	cuComplex alpha, const cuComplex *A, int lda,
	const cuComplex *B, int ldb, float beta,
	cuComplex *C, int ldc);
	/*
	* void
	* cublasCtrmm (char side, char uplo, char transa, char diag, int m, int n,
	* cuComplex alpha, const cuComplex *A, int lda, const cuCompl
	ex *B,
	* int ldb)
	*
	* performs one of the matrix-matrix operations
	*
	* B = alpha * op(A) * B, or B = alpha * B * op(A)
	*
	* where alpha is a single-precision complex scalar, B is an m x n matrix c
	omposed
	* of single precision complex elements, and A is a unit or non-unit, upper
	or lower,
	* triangular matrix composed of single precision complex elements. op(A) i
	s one of
	*
	* op(A) = A , op(A) = transpose(A) or op(A) = conjugate(transpose(A))
	*
	* Matrices A and B are stored in column major format, and lda and ldb are
	* the leading dimensions of the two-dimensonials arrays that contain A and
	* B, respectively.
	*
	* Input
	* -----
	* side specifies whether op(A) multiplies B from the left or right.
	* If side = 'L' or 'l', then B = alpha * op(A) * B. If side =
	* 'R' or 'r', then B = alpha * B * op(A).
	* uplo specifies whether the matrix A is an upper or lower triangular
	* matrix. If uplo = 'U' or 'u', A is an upper triangular matrix.
	* If uplo = 'L' or 'l', A is a lower triangular matrix.
	* transa specifies the form of op(A) to be used in the matrix
	* multiplication. If transa = 'N' or 'n', then op(A) = A. If
	* transa = 'T' or 't', then op(A) = transpose(A).
	* If transa = 'C' or 'c', then op(A) = conjugate(transpose(A)).
	* diag specifies whether or not A is unit triangular. If diag = 'U'
	* or 'u', A is assumed to be unit triangular. If diag = 'N' or
	* 'n', A is not assumed to be unit triangular.
	* m the number of rows of matrix B. m must be at least zero.
	* n the number of columns of matrix B. n must be at least zero.
	* alpha single precision complex scalar multiplier applied to op(A)*B, or
	* B*op(A), respectively. If alpha is zero no accesses are made
	* to matrix A, and no read accesses are made to matrix B.
	* A single precision complex array of dimensions (lda, k). k = m if s
	ide =
	* 'L' or 'l', k = n if side = 'R' or 'r'. If uplo = 'U' or 'u'
	* the leading k x k upper triangular part of the array A must
	* contain the upper triangular matrix, and the strictly lower
	* triangular part of A is not referenced. If uplo = 'L' or 'l'
	* the leading k x k lower triangular part of the array A must
	* contain the lower triangular matrix, and the strictly upper
	* triangular part of A is not referenced. When diag = 'U' or 'u'
	* the diagonal elements of A are no referenced and are assumed
	* to be unity.
	* lda leading dimension of A. When side = 'L' or 'l', it must be at
	* least max(1,m) and at least max(1,n) otherwise
	* B single precision complex array of dimensions (ldb, n). On entry,
	the
	* leading m x n part of the array contains the matrix B. It is
	* overwritten with the transformed matrix on exit.
	* ldb leading dimension of B. It must be at least max (1, m).
	*
	* Output
	* ------
	* B updated according to B = alpha * op(A) * B or B = alpha * B * op
	(A)
	*
	* Reference: http://www.netlib.org/blas/ctrmm.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if m or n < 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasCtrmm (char side, char uplo, char transa, char diag,
	int m, int n, cuComplex alpha, const cuComplex
	*A,
	int lda, cuComplex *B, int ldb);
	/*
	* void
	* cublasCtrsm (char side, char uplo, char transa, char diag, int m, int n,
	* cuComplex alpha, const cuComplex *A, int lda,
	* cuComplex *B, int ldb)
	*
	* solves one of the matrix equations
	*
	* op(A) * X = alpha * B, or X * op(A) = alpha * B,
	*
	* where alpha is a single precision complex scalar, and X and B are m x n
	matrices
	* that are composed of single precision complex elements. A is a unit or n
	on-unit,
	* upper or lower triangular matrix, and op(A) is one of
	*
	* op(A) = A or op(A) = transpose(A) or op( A ) = conj( A' ).
	*
	* The result matrix X overwrites input matrix B; that is, on exit the resu
	lt
	* is stored in B. Matrices A and B are stored in column major format, and
	* lda and ldb are the leading dimensions of the two-dimensonials arrays th
	at
	* contain A and B, respectively.
	*
	* Input
	* -----
	* side specifies whether op(A) appears on the left or right of X as
	* follows: side = 'L' or 'l' indicates solve op(A) * X = alpha * B.
	* side = 'R' or 'r' indicates solve X * op(A) = alpha * B.
	* uplo specifies whether the matrix A is an upper or lower triangular
	* matrix as follows: uplo = 'U' or 'u' indicates A is an upper
	* triangular matrix. uplo = 'L' or 'l' indicates A is a lower
	* triangular matrix.
	* transa specifies the form of op(A) to be used in matrix multiplication
	* as follows: If transa = 'N' or 'N', then op(A) = A. If transa =
	* 'T', 't', 'C', or 'c', then op(A) = transpose(A).
	* diag specifies whether or not A is a unit triangular matrix like so:
	* if diag = 'U' or 'u', A is assumed to be unit triangular. If
	* diag = 'N' or 'n', then A is not assumed to be unit triangular.
	* m specifies the number of rows of B. m must be at least zero.
	* n specifies the number of columns of B. n must be at least zero.
	* alpha is a single precision complex scalar to be multiplied with B. Whe
	n alpha is
	* zero, then A is not referenced and B need not be set before entry
	.
	* A is a single precision complex array of dimensions (lda, k), where
	k is
	* m when side = 'L' or 'l', and is n when side = 'R' or 'r'. If
	* uplo = 'U' or 'u', the leading k x k upper triangular part of
	* the array A must contain the upper triangular matrix and the
	* strictly lower triangular matrix of A is not referenced. When
	* uplo = 'L' or 'l', the leading k x k lower triangular part of
	* the array A must contain the lower triangular matrix and the
	* strictly upper triangular part of A is not referenced. Note that
	* when diag = 'U' or 'u', the diagonal elements of A are not
	* referenced, and are assumed to be unity.
	* lda is the leading dimension of the two dimensional array containing
	A.
	* When side = 'L' or 'l' then lda must be at least max(1, m), when
	* side = 'R' or 'r' then lda must be at least max(1, n).
	* B is a single precision complex array of dimensions (ldb, n). ldb m
	ust be
	* at least max (1,m). The leading m x n part of the array B must
	* contain the right-hand side matrix B. On exit B is overwritten
	* by the solution matrix X.
	* ldb is the leading dimension of the two dimensional array containing
	B.
	* ldb must be at least max(1, m).
	*
	* Output
	* ------
	* B contains the solution matrix X satisfying op(A) * X = alpha * B,
	* or X * op(A) = alpha * B
	*
	* Reference: http://www.netlib.org/blas/ctrsm.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if m or n < 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasCtrsm (char side, char uplo, char transa, char diag,
	int m, int n, cuComplex alpha, const cuComplex
	*A,
	int lda, cuComplex *B, int ldb);
	void CUBLASAPI cublasXerbla (const char *srName, int info);
	/* ---------------- CUBLAS double-precision BLAS1 functions ---------------
	- */
	/*
	* double
	* cublasDasum (int n, const double *x, int incx)
	*
	* computes the sum of the absolute values of the elements of double
	* precision vector x; that is, the result is the sum from i = 0 to n - 1 o
	f
	* abs(x[1 + i * incx]).
	*
	* Input
	* -----
	* n number of elements in input vector
	* x double-precision vector with n elements
	* incx storage spacing between elements of x
	*
	* Output
	* ------
	* returns the double-precision sum of absolute values
	* (0 if n <= 0 or incx <= 0, or if an error occurs)
	*
	* Reference: http://www.netlib.org/blas/dasum.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	double CUBLASAPI cublasDasum (int n, const double *x, int incx);
	/*
	* void
	* cublasDaxpy (int n, double alpha, const double *x, int incx, double *y,
	* int incy)
	*
	* multiplies double-precision vector x by double-precision scalar alpha
	* and adds the result to double-precision vector y; that is, it overwrites
	* double-precision y with double-precision alpha * x + y. For i = 0 to n-1
	,
	* it replaces y[ly + i * incy] with alpha * x[lx + i * incx] + y[ly + i*in
	cy],
	* where lx = 1 if incx >= 0, else lx = 1 + (1 - n) * incx; ly is defined i
	n a
	* similar way using incy.
	*
	* Input
	* -----
	* n number of elements in input vectors
	* alpha double-precision scalar multiplier
	* x double-precision vector with n elements
	* incx storage spacing between elements of x
	* y double-precision vector with n elements
	* incy storage spacing between elements of y
	*
	* Output
	* ------
	* y double-precision result (unchanged if n <= 0)
	*
	* Reference: http://www.netlib.org/blas/daxpy.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library was not initialized
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasDaxpy (int n, double alpha, const double *x, int incx,
	double *y, int incy);
	/*
	* void
	* cublasDcopy (int n, const double *x, int incx, double *y, int incy)
	*
	* copies the double-precision vector x to the double-precision vector y. F
	or
	* i = 0 to n-1, copies x[lx + i * incx] to y[ly + i * incy], where lx = 1
	if
	* incx >= 0, else lx = 1 + (1 - n) * incx, and ly is defined in a similar
	* way using incy.
	*
	* Input
	* -----
	* n number of elements in input vectors
	* x double-precision vector with n elements
	* incx storage spacing between elements of x
	* y double-precision vector with n elements
	* incy storage spacing between elements of y
	*
	* Output
	* ------
	* y contains double precision vector x
	*
	* Reference: http://www.netlib.org/blas/dcopy.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasDcopy (int n, const double *x, int incx, double *y,
	int incy);
	/*
	* double
	* cublasDdot (int n, const double *x, int incx, const double *y, int incy)
	*
	* computes the dot product of two double-precision vectors. It returns the
	* dot product of the double precision vectors x and y if successful, and
	* 0.0f otherwise. It computes the sum for i = 0 to n - 1 of x[lx + i *
	* incx] * y[ly + i * incy], where lx = 1 if incx >= 0, else lx = 1 + (1 -
	n)
	* *incx, and ly is defined in a similar way using incy.
	*
	* Input
	* -----
	* n number of elements in input vectors
	* x double-precision vector with n elements
	* incx storage spacing between elements of x
	* y double-precision vector with n elements
	* incy storage spacing between elements of y
	*
	* Output
	* ------
	* returns double-precision dot product (zero if n <= 0)
	*
	* Reference: http://www.netlib.org/blas/ddot.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has nor been initialize
	d
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to execute on GPU
	*/
	double CUBLASAPI cublasDdot (int n, const double *x, int incx, const double
	*y,
	int incy);
	/*
	* double
	* dnrm2 (int n, const double *x, int incx)
	*
	* computes the Euclidean norm of the double-precision n-vector x (with
	* storage increment incx). This code uses a multiphase model of
	* accumulation to avoid intermediate underflow and overflow.
	*
	* Input
	* -----
	* n number of elements in input vector
	* x double-precision vector with n elements
	* incx storage spacing between elements of x
	*
	* Output
	* ------
	* returns Euclidian norm (0 if n <= 0 or incx <= 0, or if an error occurs)
	*
	* Reference: http://www.netlib.org/blas/dnrm2.f
	* Reference: http://www.netlib.org/slatec/lin/dnrm2.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	double CUBLASAPI cublasDnrm2 (int n, const double *x, int incx);
	/*
	* void
	* cublasDrot (int n, double *x, int incx, double *y, int incy, double sc,
	* double ss)
	*
	* multiplies a 2x2 matrix ( sc ss) with the 2xn matrix ( transpose(x) )
	* (-ss sc) ( transpose(y) )
	*
	* The elements of x are in x[lx + i * incx], i = 0 ... n - 1, where lx = 1
	if
	* incx >= 0, else lx = 1 + (1 - n) * incx, and similarly for y using ly an
	d
	* incy.
	*
	* Input
	* -----
	* n number of elements in input vectors
	* x double-precision vector with n elements
	* incx storage spacing between elements of x
	* y double-precision vector with n elements
	* incy storage spacing between elements of y
	* sc element of rotation matrix
	* ss element of rotation matrix
	*
	* Output
	* ------
	* x rotated vector x (unchanged if n <= 0)
	* y rotated vector y (unchanged if n <= 0)
	*
	* Reference http://www.netlib.org/blas/drot.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasDrot (int n, double *x, int incx, double *y, int incy,
	double sc, double ss);
	/*
	* void
	* cublasDrotg (double *host_sa, double *host_sb, double *host_sc, double *
	host_ss)
	*
	* constructs the Givens tranformation
	*
	* ( sc ss )
	* G = ( ) , sc^2 + ss^2 = 1,
	* (-ss sc )
	*
	* which zeros the second entry of the 2-vector transpose(sa, sb).
	*
	* The quantity r = (+/-) sqrt (sa^2 + sb^2) overwrites sa in storage. The
	* value of sb is overwritten by a value z which allows sc and ss to be
	* recovered by the following algorithm:
	*
	* if z=1 set sc = 0.0 and ss = 1.0
	* if abs(z) < 1 set sc = sqrt(1-z^2) and ss = z
	* if abs(z) > 1 set sc = 1/z and ss = sqrt(1-sc^2)
	*
	* The function drot (n, x, incx, y, incy, sc, ss) normally is called next
	* to apply the transformation to a 2 x n matrix.
	* Note that this function is provided for completeness and run exclusively
	* on the Host.
	*
	* Input
	* -----
	* sa double-precision scalar
	* sb double-precision scalar
	*
	* Output
	* ------
	* sa double-precision r
	* sb double-precision z
	* sc double-precision result
	* ss double-precision result
	*
	* Reference: http://www.netlib.org/blas/drotg.f
	*
	* This function does not set any error status.
	*/
	void CUBLASAPI cublasDrotg (double *host_sa, double *host_sb, double *host_
	sc, double *host_ss);
	/*
	* void
	* cublasDrotm (int n, double *x, int incx, double *y, int incy,
	* const double* sparam)
	*
	* applies the modified Givens transformation, h, to the 2 x n matrix
	*
	* ( transpose(x) )
	* ( transpose(y) )
	*
	* The elements of x are in x[lx + i * incx], i = 0 to n-1, where lx = 1 if
	* incx >= 0, else lx = 1 + (1 - n) * incx, and similarly for y using ly an
	d
	* incy. With sparam[0] = sflag, h has one of the following forms:
	*
	* sflag = -1.0 sflag = 0.0 sflag = 1.0 sflag = -2.0
	*
	* (sh00 sh01) (1.0 sh01) (sh00 1.0) (1.0 0.0)
	* h = ( ) ( ) ( ) ( )
	* (sh10 sh11) (sh10 1.0) (-1.0 sh11) (0.0 1.0)
	*
	* Note that this function is provided for completeness and run exclusively
	* on the Host.
	*
	* Input
	* -----
	* n number of elements in input vectors
	* x double-precision vector with n elements
	* incx storage spacing between elements of x
	* y double-precision vector with n elements
	* incy storage spacing between elements of y
	* sparam 5-element vector. sparam[0] is sflag described above. sparam[1]
	* through sparam[4] contain the 2x2 rotation matrix h: sparam[1]
	* contains sh00, sparam[2] contains sh10, sparam[3] contains sh01,
	* and sprams[4] contains sh11.
	*
	* Output
	* ------
	* x rotated vector x (unchanged if n <= 0)
	* y rotated vector y (unchanged if n <= 0)
	*
	* Reference: http://www.netlib.org/blas/drotm.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasDrotm(int n, double *x, int incx, double *y, int incy,
	const double* sparam);
	/*
	* void
	* cublasDrotmg (double *host_sd1, double *host_sd2, double *host_sx1, cons
	t double *host_sy1,
	* double *host_sparam)
	*
	* constructs the modified Givens transformation matrix h which zeros
	* the second component of the 2-vector transpose(sqrt(sd1)*sx1,sqrt(sd2)*s
	y1).
	* With sparam[0] = sflag, h has one of the following forms:
	*
	* sflag = -1.0 sflag = 0.0 sflag = 1.0 sflag = -2.0
	*
	* (sh00 sh01) (1.0 sh01) (sh00 1.0) (1.0 0.0)
	* h = ( ) ( ) ( ) ( )
	* (sh10 sh11) (sh10 1.0) (-1.0 sh11) (0.0 1.0)
	*
	* sparam[1] through sparam[4] contain sh00, sh10, sh01, sh11,
	* respectively. Values of 1.0, -1.0, or 0.0 implied by the value
	* of sflag are not stored in sparam.
	* Note that this function is provided for completeness and run exclusively
	* on the Host.
	*
	* Input
	* -----
	* sd1 single precision scalar
	* sd2 single precision scalar
	* sx1 single precision scalar
	* sy1 single precision scalar
	*
	* Output
	* ------
	* sd1 changed to represent the effect of the transformation
	* sd2 changed to represent the effect of the transformation
	* sx1 changed to represent the effect of the transformation
	* sparam 5-element vector. sparam[0] is sflag described above. sparam[1]
	* through sparam[4] contain the 2x2 rotation matrix h: sparam[1]
	* contains sh00, sparam[2] contains sh10, sparam[3] contains sh01,
	* and sprams[4] contains sh11.
	*
	* Reference: http://www.netlib.org/blas/drotmg.f
	*
	* This functions does not set any error status.
	*/
	void CUBLASAPI cublasDrotmg (double *host_sd1, double *host_sd2, double *ho
	st_sx1,
	const double *host_sy1, double* host_sparam);
	/*
	* void
	* cublasDscal (int n, double alpha, double *x, int incx)
	*
	* replaces double-precision vector x with double-precision alpha * x. For
	* i = 0 to n-1, it replaces x[lx + i * incx] with alpha * x[lx + i * incx]
	,
	* where lx = 1 if incx >= 0, else lx = 1 + (1 - n) * incx.
	*
	* Input
	* -----
	* n number of elements in input vector
	* alpha double-precision scalar multiplier
	* x double-precision vector with n elements
	* incx storage spacing between elements of x
	*
	* Output
	* ------
	* x double-precision result (unchanged if n <= 0 or incx <= 0)
	*
	* Reference: http://www.netlib.org/blas/dscal.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library was not initialized
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasDscal (int n, double alpha, double *x, int incx);
	/*
	* void
	* cublasDswap (int n, double *x, int incx, double *y, int incy)
	*
	* replaces double-precision vector x with double-precision alpha * x. For
	i
	* = 0 to n - 1, it replaces x[ix + i * incx] with alpha * x[ix + i * incx]
	,
	* where ix = 1 if incx >= 0, else ix = 1 + (1 - n) * incx.
	*
	* Input
	* -----
	* n number of elements in input vectors
	* alpha double-precision scalar multiplier
	* x double-precision vector with n elements
	* incx storage spacing between elements of x
	*
	* Output
	* ------
	* x double precision result (unchanged if n <= 0 or incx <= 0)
	*
	* Reference: http://www.netlib.org/blas/dswap.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasDswap (int n, double *x, int incx, double *y, int incy
	);
	/*		}
	* int		static __inline__ void CUBLASAPI cublasZgemm (char transa, char transb, int
	* idamax (int n, const double *x, int incx)		m, int n,
	*		int k, cuDoubleComplex alpha,
	* finds the smallest index of the maximum magnitude element of double-		const cuDoubleComplex *A, int lda,
	* precision vector x; that is, the result is the first i, i = 0 to n - 1,		const cuDoubleComplex *B, int ldb,
	* that maximizes abs(x[1 + i * incx])).		cuDoubleComplex beta, cuDoubleComplex *C,
	*		int ldc)
	* Input		{
	* -----		cublasHandle_t handle = cublasGetCurrentCtx();
	* n number of elements in input vector		cublasStatus_t error = cublasZgemm_v2(handle, convertToOp(transa), conv
	* x double-precision vector with n elements		ertToOp(transb),
	* incx storage spacing between elements of x		m, n, k, &alpha, A, lda, B, ldb, &beta,
	*		C, ldc);
	* Output		cublasSetError(error);
	* ------
	* returns the smallest index (0 if n <= 0 or incx <= 0)
	*
	* Reference: http://www.netlib.org/blas/idamax.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	int CUBLASAPI cublasIdamax (int n, const double *x, int incx);
	/*		}
	* int		/* -------------------------------------------------------*/
	* idamin (int n, const double *x, int incx)		/* SYRK */
	*		static __inline__ void CUBLASAPI cublasSsyrk (char uplo, char trans, int n,
	* finds the smallest index of the minimum magnitude element of double-		int k, float alpha,
	* precision vector x; that is, the result is the first i, i = 0 to n - 1,		const float *A, int lda, float beta, float *C,
	* that minimizes abs(x[1 + i * incx])).		int ldc)
	*		{
	* Input		cublasHandle_t handle = cublasGetCurrentCtx();
	* -----		cublasStatus_t error = cublasSsyrk_v2(handle, convertToFillMode(uplo),
	* n number of elements in input vector		convertToOp(trans),
	* x double-precision vector with n elements		n, k, &alpha, A, lda, &beta, C, ldc);
	* incx storage spacing between elements of x		cublasSetError(error);
	*		}
	* Output		static __inline__ void CUBLASAPI cublasDsyrk (char uplo, char trans, int n,
	* ------		int k,
	* returns the smallest index (0 if n <= 0 or incx <= 0)		double alpha, const double *A, int lda,
	*		double beta, double *C, int ldc)
	* Reference: http://www.netlib.org/scilib/blass.f		{
	*		cublasHandle_t handle = cublasGetCurrentCtx();
	* Error status for this function can be retrieved via cublasGetError().		cublasStatus_t error = cublasDsyrk_v2(handle, convertToFillMode(uplo),
	*		convertToOp(trans),
	* Error Status		n, k, &alpha, A, lda, &beta, C, ldc);
	* ------------		cublasSetError(error);
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize		}
	d
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	int CUBLASAPI cublasIdamin (int n, const double *x, int incx);
	/* ---------------- CUBLAS double precision BLAS2 functions ---------------		static __inline__ void CUBLASAPI cublasCsyrk (char uplo, char trans, int n,
	- */		int k,
			cuComplex alpha, const cuComplex *A, int lda,
			cuComplex beta, cuComplex *C, int ldc)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasCsyrk_v2(handle, convertToFillMode(uplo),
			convertToOp(trans),
			n, k, &alpha, A, lda, &beta, C, ldc);
			cublasSetError(error);
			}
			static __inline__ void CUBLASAPI cublasZsyrk (char uplo, char trans, int n,
			int k,
			cuDoubleComplex alpha,
			const cuDoubleComplex *A, int lda,
			cuDoubleComplex beta,
			cuDoubleComplex *C, int ldc)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasZsyrk_v2(handle, convertToFillMode(uplo),
			convertToOp(trans),
			n, k, &alpha, A, lda, &beta, C, ldc);
			cublasSetError(error);
			}
			/* ------------------------------------------------------- */
			/* HERK */
			static __inline__ void CUBLASAPI cublasCherk (char uplo, char trans, int n,
			int k,
			float alpha, const cuComplex *A, int lda,
			float beta, cuComplex *C, int ldc)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasCherk_v2(handle, convertToFillMode(uplo),
			convertToOp(trans),
			n, k, &alpha, A, lda, &beta, C, ldc);
			cublasSetError(error);
	/*		}
	* cublasDgemv (char trans, int m, int n, double alpha, const double *A,		static __inline__ void CUBLASAPI cublasZherk (char uplo, char trans, int n,
	* int lda, const double *x, int incx, double beta, double *y,		int k,
	* int incy)		double alpha,
	*		const cuDoubleComplex *A, int lda,
	* performs one of the matrix-vector operations		double beta,
	*		cuDoubleComplex *C, int ldc)
	* y = alpha * op(A) * x + beta * y,		{
	*		cublasHandle_t handle = cublasGetCurrentCtx();
	* where op(A) is one of		cublasStatus_t error = cublasZherk_v2(handle, convertToFillMode(uplo),
	*		convertToOp(trans),
	* op(A) = A or op(A) = transpose(A)		n, k, &alpha, A, lda, &beta, C, ldc);
	*		cublasSetError(error);
	* where alpha and beta are double precision scalars, x and y are double
	* precision vectors, and A is an m x n matrix consisting of double precisi
	on
	* elements. Matrix A is stored in column major format, and lda is the lead
	ing
	* dimension of the two-dimensional array in which A is stored.
	*
	* Input
	* -----
	* trans specifies op(A). If transa = 'n' or 'N', op(A) = A. If trans =
	* trans = 't', 'T', 'c', or 'C', op(A) = transpose(A)
	* m specifies the number of rows of the matrix A. m must be at least
	* zero.
	* n specifies the number of columns of the matrix A. n must be at lea
	st
	* zero.
	* alpha double precision scalar multiplier applied to op(A).
	* A double precision array of dimensions (lda, n) if trans = 'n' or
	* 'N'), and of dimensions (lda, m) otherwise. lda must be at least
	* max(1, m) and at least max(1, n) otherwise.
	* lda leading dimension of two-dimensional array used to store matrix A
	* x double precision array of length at least (1 + (n - 1) * abs(incx
	))
	* when trans = 'N' or 'n' and at least (1 + (m - 1) * abs(incx))
	* otherwise.
	* incx specifies the storage spacing between elements of x. incx must no
	t
	* be zero.
	* beta double precision scalar multiplier applied to vector y. If beta
	* is zero, y is not read.
	* y double precision array of length at least (1 + (m - 1) * abs(incy
	))
	* when trans = 'N' or 'n' and at least (1 + (n - 1) * abs(incy))
	* otherwise.
	* incy specifies the storage spacing between elements of x. incx must no
	t
	* be zero.
	*
	* Output
	* ------
	* y updated according to alpha * op(A) * x + beta * y
	*
	* Reference: http://www.netlib.org/blas/dgemv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if m or n are < 0, or if incx or incy ==
	0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasDgemv (char trans, int m, int n, double alpha,
	const double *A, int lda, const double *x,
	int incx, double beta, double *y, int incy);
	/*		}
	* cublasDger (int m, int n, double alpha, const double *x, int incx,		/* ------------------------------------------------------- */
	* const double *y, int incy, double *A, int lda)		/* SYR2K */
	*		static __inline__ void CUBLASAPI cublasSsyr2k (char uplo, char trans, int n
	* performs the symmetric rank 1 operation		, int k, float alpha,
	*		const float *A, int lda, const float *B, int l
	* A = alpha * x * transpose(y) + A,		db,
	*		float beta, float *C, int ldc)
	* where alpha is a double precision scalar, x is an m element double		{
	* precision vector, y is an n element double precision vector, and A		cublasHandle_t handle = cublasGetCurrentCtx();
	* is an m by n matrix consisting of double precision elements. Matrix A		cublasStatus_t error = cublasSsyr2k_v2(handle, convertToFillMode(uplo),
	* is stored in column major format, and lda is the leading dimension of		convertToOp(trans),
	* the two-dimensional array used to store A.		n, k, &alpha, A, lda, B, ldb, &beta, C,
	*		ldc);
	* Input		cublasSetError(error);
	* -----
	* m specifies the number of rows of the matrix A. It must be at least
	* zero.
	* n specifies the number of columns of the matrix A. It must be at
	* least zero.
	* alpha double precision scalar multiplier applied to x * transpose(y)
	* x double precision array of length at least (1 + (m - 1) * abs(incx
	))
	* incx specifies the storage spacing between elements of x. incx must no
	t
	* be zero.
	* y double precision array of length at least (1 + (n - 1) * abs(incy
	))
	* incy specifies the storage spacing between elements of y. incy must no
	t
	* be zero.
	* A double precision array of dimensions (lda, n).
	* lda leading dimension of two-dimensional array used to store matrix A
	*
	* Output
	* ------
	* A updated according to A = alpha * x * transpose(y) + A
	*
	* Reference: http://www.netlib.org/blas/dger.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, incx == 0, incy == 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasDger (int m, int n, double alpha, const double *x,
	int incx, const double *y, int incy,
	double *A, int lda);
	/*		}
	* void
	* cublasDsyr (char uplo, int n, double alpha, const double *x, int incx,
	* double *A, int lda)
	*
	* performs the symmetric rank 1 operation
	*
	* A = alpha * x * transpose(x) + A,
	*
	* where alpha is a double precision scalar, x is an n element double
	* precision vector and A is an n x n symmetric matrix consisting of
	* double precision elements. Matrix A is stored in column major format,
	* and lda is the leading dimension of the two-dimensional array
	* containing A.
	*
	* Input
	* -----
	* uplo specifies whether the matrix data is stored in the upper or
	* the lower triangular part of array A. If uplo = 'U' or 'u',
	* then only the upper triangular part of A may be referenced.
	* If uplo = 'L' or 'l', then only the lower triangular part of
	* A may be referenced.
	* n specifies the number of rows and columns of the matrix A. It
	* must be at least 0.
	* alpha double precision scalar multiplier applied to x * transpose(x)
	* x double precision array of length at least (1 + (n - 1) * abs(incx
	))
	* incx specifies the storage spacing between elements of x. incx must
	* not be zero.
	* A double precision array of dimensions (lda, n). If uplo = 'U' or
	* 'u', then A must contain the upper triangular part of a symmetric
	* matrix, and the strictly lower triangular part is not referenced.
	* If uplo = 'L' or 'l', then A contains the lower triangular part
	* of a symmetric matrix, and the strictly upper triangular part is
	* not referenced.
	* lda leading dimension of the two-dimensional array containing A. lda
	* must be at least max(1, n).
	*
	* Output
	* ------
	* A updated according to A = alpha * x * transpose(x) + A
	*
	* Reference: http://www.netlib.org/blas/dsyr.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, or incx == 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasDsyr (char uplo, int n, double alpha,
	const double *x, int incx, double *A,
	int lda);
	/*		static __inline__ void CUBLASAPI cublasDsyr2k (char uplo, char trans, int n
	* void cublasDsyr2 (char uplo, int n, double alpha, const double *x, int i		, int k,
	ncx,		double alpha, const double *A, int lda,
	* const double *y, int incy, double *A, int lda)		const double *B, int ldb, double beta,
	*		double *C, int ldc)
	* performs the symmetric rank 2 operation		{
	*		cublasHandle_t handle = cublasGetCurrentCtx();
	* A = alpha*x*transpose(y) + alpha*y*transpose(x) + A,		cublasStatus_t error = cublasDsyr2k_v2(handle, convertToFillMode(uplo),
	*		convertToOp(trans),
	* where alpha is a double precision scalar, x and y are n element double		n, k, &alpha, A, lda, B, ldb, &beta, C,
	* precision vector and A is an n by n symmetric matrix consisting of doubl		ldc);
	e		cublasSetError(error);
	* precision elements.
	*
	* Input
	* -----
	* uplo specifies whether the matrix data is stored in the upper or the l
	ower
	* triangular part of array A. If uplo == 'U' or 'u', then only the
	* upper triangular part of A may be referenced and the lower triang
	ular
	* part of A is inferred. If uplo == 'L' or 'l', then only the lower
	* triangular part of A may be referenced and the upper triangular p
	art
	* of A is inferred.
	* n specifies the number of rows and columns of the matrix A. It must
	be
	* at least zero.
	* alpha double precision scalar multiplier applied to x * transpose(y) +
	* y * transpose(x).
	* x double precision array of length at least (1 + (n - 1) * abs (inc
	x)).
	* incx storage spacing between elements of x. incx must not be zero.
	* y double precision array of length at least (1 + (n - 1) * abs (inc
	y)).
	* incy storage spacing between elements of y. incy must not be zero.
	* A double precision array of dimensions (lda, n). If uplo == 'U' or
	'u',
	* then A must contains the upper triangular part of a symmetric mat
	rix,
	* and the strictly lower triangular parts is not referenced. If upl
	o ==
	* 'L' or 'l', then A contains the lower triangular part of a symmet
	ric
	* matrix, and the strictly upper triangular part is not referenced.
	* lda leading dimension of A. It must be at least max(1, n).
	*
	* Output
	* ------
	* A updated according to A = alpha*x*transpose(y)+alpha*y*transpose(x
	)+A
	*
	* Reference: http://www.netlib.org/blas/dsyr2.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, incx == 0, incy == 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasDsyr2 (char uplo, int n, double alpha,
	const double *x, int incx, const double *y,
	int incy, double *A, int lda);
	/*		}
	* void		static __inline__ void CUBLASAPI cublasCsyr2k (char uplo, char trans, int n
	* cublasDspr (char uplo, int n, double alpha, const double *x, int incx,		, int k,
	* double *AP)		cuComplex alpha, const cuComplex *A, int lda,
	*		const cuComplex *B, int ldb, cuComplex beta,
	* performs the symmetric rank 1 operation		cuComplex *C, int ldc)
	*		{
	* A = alpha * x * transpose(x) + A,		cublasHandle_t handle = cublasGetCurrentCtx();
	*		cublasStatus_t error = cublasCsyr2k_v2(handle, convertToFillMode(uplo),
	* where alpha is a double precision scalar and x is an n element double		convertToOp(trans),
	* precision vector. A is a symmetric n x n matrix consisting of double		n, k, &alpha, A, lda, B, ldb, &beta, C,
	* precision elements that is supplied in packed form.		ldc);
	*		cublasSetError(error);
	* Input
	* -----
	* uplo specifies whether the matrix data is stored in the upper or the l
	ower
	* triangular part of array AP. If uplo == 'U' or 'u', then the uppe
	r
	* triangular part of A is supplied in AP. If uplo == 'L' or 'l', th
	en
	* the lower triangular part of A is supplied in AP.
	* n specifies the number of rows and columns of the matrix A. It must
	be
	* at least zero.
	* alpha double precision scalar multiplier applied to x * transpose(x).
	* x double precision array of length at least (1 + (n - 1) * abs(incx
	)).
	* incx storage spacing between elements of x. incx must not be zero.
	* AP double precision array with at least ((n * (n + 1)) / 2) elements
	. If
	* uplo == 'U' or 'u', the array AP contains the upper triangular pa
	rt
	* of the symmetric matrix A, packed sequentially, column by column;
	* that is, if i <= j, then A[i,j] is stored is AP[i+(j*(j+1)/2)]. I
	f
	* uplo == 'L' or 'L', the array AP contains the lower triangular pa
	rt
	* of the symmetric matrix A, packed sequentially, column by column;
	* that is, if i >= j, then A[i,j] is stored in AP[i+((2*n-j+1)*j)/2
	].
	*
	* Output
	* ------
	* A updated according to A = alpha * x * transpose(x) + A
	*
	* Reference: http://www.netlib.org/blas/dspr.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, or incx == 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasDspr (char uplo, int n, double alpha,
	const double *x, int incx, double *AP);
	/*
	* void
	* cublasDspr2 (char uplo, int n, double alpha, const double *x, int incx,
	* const double *y, int incy, double *AP)
	*
	* performs the symmetric rank 2 operation
	*
	* A = alpha*x*transpose(y) + alpha*y*transpose(x) + A,
	*
	* where alpha is a double precision scalar, and x and y are n element doub
	le
	* precision vectors. A is a symmetric n x n matrix consisting of double
	* precision elements that is supplied in packed form.
	*
	* Input
	* -----
	* uplo specifies whether the matrix data is stored in the upper or the l
	ower
	* triangular part of array A. If uplo == 'U' or 'u', then only the
	* upper triangular part of A may be referenced and the lower triang
	ular
	* part of A is inferred. If uplo == 'L' or 'l', then only the lower
	* triangular part of A may be referenced and the upper triangular p
	art
	* of A is inferred.
	* n specifies the number of rows and columns of the matrix A. It must
	be
	* at least zero.
	* alpha double precision scalar multiplier applied to x * transpose(y) +
	* y * transpose(x).
	* x double precision array of length at least (1 + (n - 1) * abs (inc
	x)).
	* incx storage spacing between elements of x. incx must not be zero.
	* y double precision array of length at least (1 + (n - 1) * abs (inc
	y)).
	* incy storage spacing between elements of y. incy must not be zero.
	* AP double precision array with at least ((n * (n + 1)) / 2) elements
	. If
	* uplo == 'U' or 'u', the array AP contains the upper triangular pa
	rt
	* of the symmetric matrix A, packed sequentially, column by column;
	* that is, if i <= j, then A[i,j] is stored is AP[i+(j*(j+1)/2)]. I
	f
	* uplo == 'L' or 'L', the array AP contains the lower triangular pa
	rt
	* of the symmetric matrix A, packed sequentially, column by column;
	* that is, if i >= j, then A[i,j] is stored in AP[i+((2*n-j+1)*j)/2
	].
	*
	* Output
	* ------
	* A updated according to A = alpha*x*transpose(y)+alpha*y*transpose(x
	)+A
	*
	* Reference: http://www.netlib.org/blas/dspr2.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, incx == 0, incy == 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasDspr2 (char uplo, int n, double alpha,
	const double *x, int incx, const double *y,
	int incy, double *AP);
	/*		}
	* void
	* cublasDtrsv (char uplo, char trans, char diag, int n, const double *A,
	* int lda, double *x, int incx)
	*
	* solves a system of equations op(A) * x = b, where op(A) is either A or
	* transpose(A). b and x are double precision vectors consisting of n
	* elements, and A is an n x n matrix composed of a unit or non-unit, upper
	* or lower triangular matrix. Matrix A is stored in column major format,
	* and lda is the leading dimension of the two-dimensional array containing
	* A.
	*
	* No test for singularity or near-singularity is included in this function
	.
	* Such tests must be performed before calling this function.
	*
	* Input
	* -----
	* uplo specifies whether the matrix data is stored in the upper or the
	* lower triangular part of array A. If uplo = 'U' or 'u', then only
	* the upper triangular part of A may be referenced. If uplo = 'L' o
	r
	* 'l', then only the lower triangular part of A may be referenced.
	* trans specifies op(A). If transa = 'n' or 'N', op(A) = A. If transa = '
	t',
	* 'T', 'c', or 'C', op(A) = transpose(A)
	* diag specifies whether or not A is a unit triangular matrix like so:
	* if diag = 'U' or 'u', A is assumed to be unit triangular. If
	* diag = 'N' or 'n', then A is not assumed to be unit triangular.
	* n specifies the number of rows and columns of the matrix A. It
	* must be at least 0.
	* A is a double precision array of dimensions (lda, n). If uplo = 'U'
	* or 'u', then A must contains the upper triangular part of a symme
	tric
	* matrix, and the strictly lower triangular parts is not referenced
	.
	* If uplo = 'L' or 'l', then A contains the lower triangular part o
	f
	* a symmetric matrix, and the strictly upper triangular part is not
	* referenced.
	* lda is the leading dimension of the two-dimensional array containing
	A.
	* lda must be at least max(1, n).
	* x double precision array of length at least (1 + (n - 1) * abs(incx
	)).
	* On entry, x contains the n element right-hand side vector b. On e
	xit,
	* it is overwritten with the solution vector x.
	* incx specifies the storage spacing between elements of x. incx must no
	t
	* be zero.
	*
	* Output
	* ------
	* x updated to contain the solution vector x that solves op(A) * x =
	b.
	*
	* Reference: http://www.netlib.org/blas/dtrsv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if incx == 0 or if n < 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasDtrsv (char uplo, char trans, char diag, int n,
	const double *A, int lda, double *x,
	int incx);
	/*		static __inline__ void CUBLASAPI cublasZsyr2k (char uplo, char trans, int n
	* void		, int k,
	* cublasDtrmv (char uplo, char trans, char diag, int n, const double *A,		cuDoubleComplex alpha, const cuDoubleComplex *
	* int lda, double *x, int incx);		A, int lda,
	*		const cuDoubleComplex *B, int ldb, cuDoubleCom
	* performs one of the matrix-vector operations x = op(A) * x, where op(A)		plex beta,
	=		cuDoubleComplex *C, int ldc)
	= A, or op(A) = transpose(A). x is an n-element single precision vector, a		{
	nd		cublasHandle_t handle = cublasGetCurrentCtx();
	* A is an n x n, unit or non-unit, upper or lower, triangular matrix compo		cublasStatus_t error = cublasZsyr2k_v2(handle, convertToFillMode(uplo),
	sed		convertToOp(trans),
	* of single precision elements.		n, k, &alpha, A, lda, B, ldb, &beta, C,
	*		ldc);
	* Input		cublasSetError(error);
	* -----
	* uplo specifies whether the matrix A is an upper or lower triangular
	* matrix. If uplo = 'U' or 'u', then A is an upper triangular matri
	x.
	* If uplo = 'L' or 'l', then A is a lower triangular matrix.
	* trans specifies op(A). If transa = 'N' or 'n', op(A) = A. If trans = 'T
	',
	* 't', 'C', or 'c', op(A) = transpose(A)
	* diag specifies whether or not matrix A is unit triangular. If diag = '
	U'
	* or 'u', A is assumed to be unit triangular. If diag = 'N' or 'n',
	A
	* is not assumed to be unit triangular.
	* n specifies the number of rows and columns of the matrix A. n must
	be
	* at least zero.
	* A single precision array of dimension (lda, n). If uplo = 'U' or 'u
	',
	* the leading n x n upper triangular part of the array A must conta
	in
	* the upper triangular matrix and the strictly lower triangular par
	t
	* of A is not referenced. If uplo = 'L' or 'l', the leading n x n l
	ower
	* triangular part of the array A must contain the lower triangular
	* matrix and the strictly upper triangular part of A is not referen
	ced.
	* When diag = 'U' or 'u', the diagonal elements of A are not refere
	nced
	* either, but are are assumed to be unity.
	* lda is the leading dimension of A. It must be at least max (1, n).
	* x single precision array of length at least (1 + (n - 1) * abs(incx
	) ).
	* On entry, x contains the source vector. On exit, x is overwritten
	* with the result vector.
	* incx specifies the storage spacing for elements of x. incx must not be
	* zero.
	*
	* Output
	* ------
	* x updated according to x = op(A) * x,
	*
	* Reference: http://www.netlib.org/blas/dtrmv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if incx == 0 or if n < 0
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasDtrmv (char uplo, char trans, char diag, int n,
	const double *A, int lda, double *x, int incx);
	/*		}
	* void		/* ------------------------------------------------------- */
	* cublasDgbmv (char trans, int m, int n, int kl, int ku, double alpha,		/* HER2K */
	* const double *A, int lda, const double *x, int incx, double		static __inline__ void CUBLASAPI cublasCher2k (char uplo, char trans, int n
	beta,		, int k,
	* double *y, int incy);		cuComplex alpha, const cuComplex *A, int lda,
	*		const cuComplex *B, int ldb, float beta,
	* performs one of the matrix-vector operations		cuComplex *C, int ldc)
	*		{
	* y = alpha*op(A)*x + beta*y, op(A)=A or op(A) = transpose(A)		cublasHandle_t handle = cublasGetCurrentCtx();
	*		cublasStatus_t error = cublasCher2k_v2(handle, convertToFillMode(uplo),
	* alpha and beta are double precision scalars. x and y are double precisio		convertToOp(trans),
	n		n, k, &alpha, A, lda, B, ldb, &beta, C,
	* vectors. A is an m by n band matrix consisting of double precision eleme		ldc);
	nts		cublasSetError(error);
	* with kl sub-diagonals and ku super-diagonals.
	*
	* Input
	* -----
	* trans specifies op(A). If trans == 'N' or 'n', op(A) = A. If trans == '
	T',
	* 't', 'C', or 'c', op(A) = transpose(A)
	* m specifies the number of rows of the matrix A. m must be at least
	* zero.
	* n specifies the number of columns of the matrix A. n must be at lea
	st
	* zero.
	* kl specifies the number of sub-diagonals of matrix A. It must be at
	* least zero.
	* ku specifies the number of super-diagonals of matrix A. It must be a
	t
	* least zero.
	* alpha double precision scalar multiplier applied to op(A).
	* A double precision array of dimensions (lda, n). The leading
	* (kl + ku + 1) x n part of the array A must contain the band matri
	x A,
	* supplied column by column, with the leading diagonal of the matri
	x
	* in row (ku + 1) of the array, the first super-diagonal starting a
	t
	* position 2 in row ku, the first sub-diagonal starting at position
	1
	* in row (ku + 2), and so on. Elements in the array A that do not
	* correspond to elements in the band matrix (such as the top left
	* ku x ku triangle) are not referenced.
	* lda leading dimension of A. lda must be at least (kl + ku + 1).
	* x double precision array of length at least (1+(n-1)*abs(incx)) whe
	n
	* trans == 'N' or 'n' and at least (1+(m-1)*abs(incx)) otherwise.
	* incx specifies the increment for the elements of x. incx must not be z
	ero.
	* beta double precision scalar multiplier applied to vector y. If beta i
	s
	* zero, y is not read.
	* y double precision array of length at least (1+(m-1)*abs(incy)) whe
	n
	* trans == 'N' or 'n' and at least (1+(n-1)*abs(incy)) otherwise. I
	f
	* beta is zero, y is not read.
	* incy On entry, incy specifies the increment for the elements of y. inc
	y
	* must not be zero.
	*
	* Output
	* ------
	* y updated according to y = alpha*op(A)*x + beta*y
	*
	* Reference: http://www.netlib.org/blas/dgbmv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, or if incx or incy == 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasDgbmv (char trans, int m, int n, int kl, int ku,
	double alpha, const double *A, int lda,
	const double *x, int incx, double beta,
	double *y, int incy);
	/*		}
	* void
	* cublasDtbmv (char uplo, char trans, char diag, int n, int k, const doubl
	e *A,
	* int lda, double *x, int incx)
	*
	* performs one of the matrix-vector operations x = op(A) * x, where op(A)
	= A,
	* or op(A) = transpose(A). x is an n-element double precision vector, and
	A is
	* an n x n, unit or non-unit, upper or lower triangular band matrix compos
	ed
	* of double precision elements.
	*
	* Input
	* -----
	* uplo specifies whether the matrix A is an upper or lower triangular ba
	nd
	* matrix. If uplo == 'U' or 'u', A is an upper triangular band matr
	ix.
	* If uplo == 'L' or 'l', A is a lower triangular band matrix.
	* trans specifies op(A). If transa == 'N' or 'n', op(A) = A. If trans ==
	'T',
	* 't', 'C', or 'c', op(A) = transpose(A)
	* diag specifies whether or not matrix A is unit triangular. If diag ==
	'U'
	* or 'u', A is assumed to be unit triangular. If diag == 'N' or 'n'
	, A
	* is not assumed to be unit triangular.
	* n specifies the number of rows and columns of the matrix A. n must
	be
	* at least zero.
	* k specifies the number of super- or sub-diagonals. If uplo == 'U' o
	r
	* 'u', k specifies the number of super-diagonals. If uplo == 'L' or
	* 'l', k specifies the number of sub-diagonals. k must at least be
	* zero.
	* A double precision array of dimension (lda, n). If uplo == 'U' or '
	u',
	* the leading (k + 1) x n part of the array A must contain the uppe
	r
	* triangular band matrix, supplied column by column, with the leadi
	ng
	* diagonal of the matrix in row (k + 1) of the array, the first
	* super-diagonal starting at position 2 in row k, and so on. The to
	p
	* left k x k triangle of the array A is not referenced. If uplo ==
	'L'
	* or 'l', the leading (k + 1) x n part of the array A must constain
	the
	* lower triangular band matrix, supplied column by column, with the
	* leading diagonal of the matrix in row 1 of the array, the first
	* sub-diagonal startingat position 1 in row 2, and so on. The botto
	m
	* right k x k triangle of the array is not referenced.
	* lda is the leading dimension of A. It must be at least (k + 1).
	* x double precision array of length at least (1 + (n - 1) * abs(incx
	)).
	* On entry, x contains the source vector. On exit, x is overwritten
	* with the result vector.
	* incx specifies the storage spacing for elements of x. incx must not be
	* zero.
	*
	* Output
	* ------
	* x updated according to x = op(A) * x
	*
	* Reference: http://www.netlib.org/blas/dtbmv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n or k < 0, or if incx == 0
	* CUBLAS_STATUS_ALLOC_FAILED if function cannot allocate enough intern
	al scratch vector memory
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasDtbmv (char uplo, char trans, char diag, int n,
	int k, const double *A, int lda, double *x,
	int incx);
	/*		static __inline__ void CUBLASAPI cublasZher2k (char uplo, char trans, int n
	* void		, int k,
	* cublasDtpmv (char uplo, char trans, char diag, int n, const double *AP,		cuDoubleComplex alpha, const cuDoubleComplex *
	* double *x, int incx);		A, int lda,
	*		const cuDoubleComplex *B, int ldb, double beta
	* performs one of the matrix-vector operations x = op(A) * x, where op(A)		,
	= A,		cuDoubleComplex *C, int ldc)
	* or op(A) = transpose(A). x is an n element double precision vector, and		{
	A		cublasHandle_t handle = cublasGetCurrentCtx();
	* is an n x n, unit or non-unit, upper or lower triangular matrix composed		cublasStatus_t error = cublasZher2k_v2(handle, convertToFillMode(uplo),
	* of double precision elements.		convertToOp(trans),
	*		n, k, &alpha, A, lda, B, ldb, &beta, C,
	* Input		ldc);
	* -----		cublasSetError(error);
	* uplo specifies whether the matrix A is an upper or lower triangular		}
	* matrix. If uplo == 'U' or 'u', then A is an upper triangular matr
	ix.
	* If uplo == 'L' or 'l', then A is a lower triangular matrix.
	* trans specifies op(A). If transa == 'N' or 'n', op(A) = A. If trans ==
	'T',
	* 't', 'C', or 'c', op(A) = transpose(A)
	* diag specifies whether or not matrix A is unit triangular. If diag ==
	'U'
	* or 'u', A is assumed to be unit triangular. If diag == 'N' or 'n'
	, A
	* is not assumed to be unit triangular.
	* n specifies the number of rows and columns of the matrix A. n must
	be
	* at least zero. In the current implementation n must not exceed 40
	70.
	* AP double precision array with at least ((n * (n + 1)) / 2) elements
	. If
	* uplo == 'U' or 'u', the array AP contains the upper triangular pa
	rt
	* of the symmetric matrix A, packed sequentially, column by column;
	* that is, if i <= j, then A[i,j] is stored in AP[i+(j*(j+1)/2)]. I
	f
	* uplo == 'L' or 'L', the array AP contains the lower triangular pa
	rt
	* of the symmetric matrix A, packed sequentially, column by column;
	* that is, if i >= j, then A[i,j] is stored in AP[i+((2*n-j+1)*j)/2
	].
	* x double precision array of length at least (1 + (n - 1) * abs(incx
	)).
	* On entry, x contains the source vector. On exit, x is overwritten
	* with the result vector.
	* incx specifies the storage spacing for elements of x. incx must not be
	* zero.
	*
	* Output
	* ------
	* x updated according to x = op(A) * x,
	*
	* Reference: http://www.netlib.org/blas/dtpmv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if incx == 0 or n < 0
	* CUBLAS_STATUS_ALLOC_FAILED if function cannot allocate enough intern
	al scratch vector memory
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasDtpmv (char uplo, char trans, char diag, int n,
	const double *AP, double *x, int incx);
	/*		/*------------------------------------------------------------------------*
	* void		/
	* cublasDtpsv (char uplo, char trans, char diag, int n, const double *AP,		/* SYMM*/
	* double *X, int incx)		static __inline__ void CUBLASAPI cublasSsymm (char side, char uplo, int m,
	*		int n, float alpha,
	* solves one of the systems of equations op(A)*x = b, where op(A) is eithe		const float *A, int lda, const float *B, int ld
	r		b,
	* op(A) = A or op(A) = transpose(A). b and x are n element vectors, and A		float beta, float *C, int ldc)
	is		{
	* an n x n unit or non-unit, upper or lower triangular matrix. No test for		cublasHandle_t handle = cublasGetCurrentCtx();
	* singularity or near-singularity is included in this routine. Such tests		cublasStatus_t error = cublasSsymm_v2(handle, convertToSideMode(side),c
	* must be performed before calling this routine.		onvertToFillMode(uplo),
	*		m, n, &alpha, A, lda, B, ldb, &beta, C,
	* Input		ldc );
	* -----		cublasSetError(error);
	* uplo specifies whether the matrix is an upper or lower triangular matr		}
	ix		static __inline__ void CUBLASAPI cublasDsymm (char side, char uplo, int m,
	* as follows: If uplo == 'U' or 'u', A is an upper triangluar matri		int n, double alpha,
	x.		const double *A, int lda, const double *B, int
	* If uplo == 'L' or 'l', A is a lower triangular matrix.		ldb,
	* trans specifies op(A). If trans == 'N' or 'n', op(A) = A. If trans == '		double beta, double *C, int ldc)
	T',		{
	* 't', 'C', or 'c', op(A) = transpose(A).		cublasHandle_t handle = cublasGetCurrentCtx();
	* diag specifies whether A is unit triangular. If diag == 'U' or 'u', A		cublasStatus_t error = cublasDsymm_v2(handle, convertToSideMode(side),c
	is		onvertToFillMode(uplo),
	* assumed to be unit triangular; thas is, diagonal elements are not		m, n, &alpha, A, lda, B, ldb, &beta, C,
	* read and are assumed to be unity. If diag == 'N' or 'n', A is not		ldc );
	* assumed to be unit triangular.		cublasSetError(error);
	* n specifies the number of rows and columns of the matrix A. n must		}
	be		static __inline__ void CUBLASAPI cublasCsymm (char side, char uplo, int m,
	* at least zero.		int n, cuComplex alpha,
	* AP double precision array with at least ((n*(n+1))/2) elements. If u		const cuComplex *A, int lda, const cuComplex *B
	plo		, int ldb,
	* == 'U' or 'u', the array AP contains the upper triangular matrix		cuComplex beta, cuComplex *C, int ldc)
	A,		{
	* packed sequentially, column by column; that is, if i <= j, then		cublasHandle_t handle = cublasGetCurrentCtx();
	* A[i,j] is stored is AP[i+(j*(j+1)/2)]. If uplo == 'L' or 'L', the		cublasStatus_t error = cublasCsymm_v2(handle, convertToSideMode(side),c
	* array AP contains the lower triangular matrix A, packed sequentia		onvertToFillMode(uplo),
	lly,		m, n, &alpha, A, lda, B, ldb, &beta, C,
	* column by column; that is, if i >= j, then A[i,j] is stored in		ldc );
	* AP[i+((2*n-j+1)*j)/2]. When diag = 'U' or 'u', the diagonal eleme		cublasSetError(error);
	nts		}
	* of A are not referenced and are assumed to be unity.		static __inline__ void CUBLASAPI cublasZsymm (char side, char uplo, int m,
	* x double precision array of length at least (1+(n-1)*abs(incx)).		int n, cuDoubleComplex alpha,
	* incx storage spacing between elements of x. It must not be zero.		const cuDoubleComplex *A, int lda, const cuDoub
	*		leComplex *B, int ldb,
	* Output		cuDoubleComplex beta, cuDoubleComplex *C, int l
	* ------		dc)
	* x updated to contain the solution vector x that solves op(A) * x =		{
	b.		cublasHandle_t handle = cublasGetCurrentCtx();
	*		cublasStatus_t error = cublasZsymm_v2(handle, convertToSideMode(side),c
	* Reference: http://www.netlib.org/blas/dtpsv.f		onvertToFillMode(uplo),
	*		m, n, &alpha, A, lda, B, ldb, &beta, C,
	* Error status for this function can be retrieved via cublasGetError().		ldc );
	*		cublasSetError(error);
	* Error Status		}
	* ------------		/*------------------------------------------------------------------------*
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize		/
	d		/* HEMM*/
	* CUBLAS_STATUS_INVALID_VALUE if incx == 0 or if n < 0 or n > 2035		static __inline__ void CUBLASAPI cublasChemm (char side, char uplo, int m,
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support		int n,
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU		cuComplex alpha, const cuComplex *A, int lda,
	*/		const cuComplex *B, int ldb, cuComplex beta,
	void CUBLASAPI cublasDtpsv (char uplo, char trans, char diag, int n,		cuComplex *C, int ldc)
	const double *AP, double *x, int incx);		{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasChemm_v2(handle, convertToSideMode(side),c
			onvertToFillMode(uplo),
			m, n, &alpha, A, lda, B, ldb, &beta, C,
			ldc );
			cublasSetError(error);
	/*		}
	* void cublasDtbsv (char uplo, char trans, char diag, int n, int k,		static __inline__ void CUBLASAPI cublasZhemm (char side, char uplo, int m,
	* const double *A, int lda, double *X, int incx)		int n,
	*		cuDoubleComplex alpha, const cuDoubleComplex *A
	* solves one of the systems of equations op(A)*x = b, where op(A) is eithe		, int lda,
	r		const cuDoubleComplex *B, int ldb, cuDoubleComp
	* op(A) = A or op(A) = transpose(A). b and x are n element vectors, and A		lex beta,
	is		cuDoubleComplex *C, int ldc)
	* an n x n unit or non-unit, upper or lower triangular band matrix with k		{
	+ 1		cublasHandle_t handle = cublasGetCurrentCtx();
	* diagonals. No test for singularity or near-singularity is included in th		cublasStatus_t error = cublasZhemm_v2(handle, convertToSideMode(side),c
	is		onvertToFillMode(uplo),
	* function. Such tests must be performed before calling this function.		m, n, &alpha, A, lda, B, ldb, &beta, C,
	*		ldc );
	* Input		cublasSetError(error);
	* -----		}
	* uplo specifies whether the matrix is an upper or lower triangular band
	* matrix as follows: If uplo == 'U' or 'u', A is an upper triangula
	r
	* band matrix. If uplo == 'L' or 'l', A is a lower triangular band
	* matrix.
	* trans specifies op(A). If trans == 'N' or 'n', op(A) = A. If trans == '
	T',
	* 't', 'C', or 'c', op(A) = transpose(A).
	* diag specifies whether A is unit triangular. If diag == 'U' or 'u', A
	is
	* assumed to be unit triangular; thas is, diagonal elements are not
	* read and are assumed to be unity. If diag == 'N' or 'n', A is not
	* assumed to be unit triangular.
	* n specifies the number of rows and columns of the matrix A. n must
	be
	* at least zero.
	* k specifies the number of super- or sub-diagonals. If uplo == 'U' o
	r
	* 'u', k specifies the number of super-diagonals. If uplo == 'L' or
	* 'l', k specifies the number of sub-diagonals. k must at least be
	* zero.
	* A double precision array of dimension (lda, n). If uplo == 'U' or '
	u',
	* the leading (k + 1) x n part of the array A must contain the uppe
	r
	* triangular band matrix, supplied column by column, with the leadi
	ng
	* diagonal of the matrix in row (k + 1) of the array, the first sup
	er-
	* diagonal starting at position 2 in row k, and so on. The top left
	* k x k triangle of the array A is not referenced. If uplo == 'L' o
	r
	* 'l', the leading (k + 1) x n part of the array A must constain th
	e
	* lower triangular band matrix, supplied column by column, with the
	* leading diagonal of the matrix in row 1 of the array, the first
	* sub-diagonal starting at position 1 in row 2, and so on. The bott
	om
	* right k x k triangle of the array is not referenced.
	* x double precision array of length at least (1+(n-1)*abs(incx)).
	* incx storage spacing between elements of x. It must not be zero.
	*
	* Output
	* ------
	* x updated to contain the solution vector x that solves op(A) * x =
	b.
	*
	* Reference: http://www.netlib.org/blas/dtbsv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if incx == 0, n < 0 or n > 2035
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasDtbsv (char uplo, char trans, char diag, int n,
	int k, const double *A, int lda, double *x,
	int incx);
	/*		/*------------------------------------------------------------------------*
	* void		/
	* cublasDsymv (char uplo, int n, double alpha, const double *A, int lda,		/* TRSM*/
	* const double *x, int incx, double beta, double *y, int incy		static __inline__ void CUBLASAPI cublasStrsm (char side, char uplo, char tr
	)		ansa, char diag,
	*		int m, int n, float alpha, const float *A, int
	* performs the matrix-vector operation		lda,
	*		float *B, int ldb)
	* y = alpha*A*x + beta*y		{
	*		cublasHandle_t handle = cublasGetCurrentCtx();
	* Alpha and beta are double precision scalars, and x and y are double		cublasStatus_t error = cublasStrsm_v2(handle, convertToSideMode(side),c
	* precision vectors, each with n elements. A is a symmetric n x n matrix		onvertToFillMode(uplo),
	* consisting of double precision elements that is stored in either upper o		convertToOp(transa), convertToDiagType(d
	r		iag), m, n, &alpha, A, lda, B, ldb );
	* lower storage mode.		cublasSetError(error);
	*
	* Input
	* -----
	* uplo specifies whether the upper or lower triangular part of the array
	A
	* is to be referenced. If uplo == 'U' or 'u', the symmetric matrix
	A
	* is stored in upper storage mode, i.e. only the upper triangular p
	art
	* of A is to be referenced while the lower triangular part of A is
	to
	* be inferred. If uplo == 'L' or 'l', the symmetric matrix A is sto
	red
	* in lower storage mode, i.e. only the lower triangular part of A i
	s
	* to be referenced while the upper triangular part of A is to be
	* inferred.
	* n specifies the number of rows and the number of columns of the
	* symmetric matrix A. n must be at least zero.
	* alpha double precision scalar multiplier applied to A*x.
	* A double precision array of dimensions (lda, n). If uplo == 'U' or
	'u',
	* the leading n x n upper triangular part of the array A must conta
	in
	* the upper triangular part of the symmetric matrix and the strictl
	y
	* lower triangular part of A is not referenced. If uplo == 'L' or '
	l',
	* the leading n x n lower triangular part of the array A must conta
	in
	* the lower triangular part of the symmetric matrix and the strictl
	y
	* upper triangular part of A is not referenced.
	* lda leading dimension of A. It must be at least max (1, n).
	* x double precision array of length at least (1 + (n - 1) * abs(incx
	)).
	* incx storage spacing between elements of x. incx must not be zero.
	* beta double precision scalar multiplier applied to vector y.
	* y double precision array of length at least (1 + (n - 1) * abs(incy
	)).
	* If beta is zero, y is not read.
	* incy storage spacing between elements of y. incy must not be zero.
	*
	* Output
	* ------
	* y updated according to y = alpha*A*x + beta*y
	*
	* Reference: http://www.netlib.org/blas/dsymv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, or if incx or incy == 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasDsymv (char uplo, int n, double alpha,
	const double *A, int lda, const double *x,
	int incx, double beta, double *y, int incy);
	/*		}
	* void
	* cublasDsbmv (char uplo, int n, int k, double alpha, const double *A, int
	lda,
	* const double *x, int incx, double beta, double *y, int incy
	)
	*
	* performs the matrix-vector operation
	*
	* y := alpha*A*x + beta*y
	*
	* alpha and beta are double precision scalars. x and y are double precisio
	n
	* vectors with n elements. A is an n by n symmetric band matrix consisting
	* of double precision elements, with k super-diagonals and the same number
	* of subdiagonals.
	*
	* Input
	* -----
	* uplo specifies whether the upper or lower triangular part of the symme
	tric
	* band matrix A is being supplied. If uplo == 'U' or 'u', the upper
	* triangular part is being supplied. If uplo == 'L' or 'l', the low
	er
	* triangular part is being supplied.
	* n specifies the number of rows and the number of columns of the
	* symmetric matrix A. n must be at least zero.
	* k specifies the number of super-diagonals of matrix A. Since the ma
	trix
	* is symmetric, this is also the number of sub-diagonals. k must be
	at
	* least zero.
	* alpha double precision scalar multiplier applied to A*x.
	* A double precision array of dimensions (lda, n). When uplo == 'U' o
	r
	* 'u', the leading (k + 1) x n part of array A must contain the upp
	er
	* triangular band of the symmetric matrix, supplied column by colum
	n,
	* with the leading diagonal of the matrix in row (k+1) of the array
	,
	* the first super-diagonal starting at position 2 in row k, and so
	on.
	* The top left k x k triangle of the array A is not referenced. Whe
	n
	* uplo == 'L' or 'l', the leading (k + 1) x n part of the array A m
	ust
	* contain the lower triangular band part of the symmetric matrix,
	* supplied column by column, with the leading diagonal of the matri
	x in
	* row 1 of the array, the first sub-diagonal starting at position 1
	in
	* row 2, and so on. The bottom right k x k triangle of the array A
	is
	* not referenced.
	* lda leading dimension of A. lda must be at least (k + 1).
	* x double precision array of length at least (1 + (n - 1) * abs(incx
	)).
	* incx storage spacing between elements of x. incx must not be zero.
	* beta double precision scalar multiplier applied to vector y. If beta i
	s
	* zero, y is not read.
	* y double precision array of length at least (1 + (n - 1) * abs(incy
	)).
	* If beta is zero, y is not read.
	* incy storage spacing between elements of y. incy must not be zero.
	*
	* Output
	* ------
	* y updated according to alpha*A*x + beta*y
	*
	* Reference: http://www.netlib.org/blas/dsbmv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if k or n < 0, or if incx or incy == 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasDsbmv (char uplo, int n, int k, double alpha,
	const double *A, int lda, const double *x,
	int incx, double beta, double *y, int incy);
	/*		static __inline__ void CUBLASAPI cublasDtrsm (char side, char uplo, char tr
	* void		ansa,
	* cublasDspmv (char uplo, int n, double alpha, const double *AP, const dou		char diag, int m, int n, double alpha,
	ble *x,		const double *A, int lda, double *B,
	* int incx, double beta, double *y, int incy)		int ldb)
	*		{
	* performs the matrix-vector operation		cublasHandle_t handle = cublasGetCurrentCtx();
	*		cublasStatus_t error = cublasDtrsm_v2(handle, convertToSideMode(side),c
	* y = alpha * A * x + beta * y		onvertToFillMode(uplo),
	*		convertToOp(transa), convertToDiagType(d
	* Alpha and beta are double precision scalars, and x and y are double		iag), m, n, &alpha, A, lda, B, ldb );
	* precision vectors with n elements. A is a symmetric n x n matrix		cublasSetError(error);
	* consisting of double precision elements that is supplied in packed form.
	*
	* Input
	* -----
	* uplo specifies whether the matrix data is stored in the upper or the l
	ower
	* triangular part of array AP. If uplo == 'U' or 'u', then the uppe
	r
	* triangular part of A is supplied in AP. If uplo == 'L' or 'l', th
	en
	* the lower triangular part of A is supplied in AP.
	* n specifies the number of rows and columns of the matrix A. It must
	be
	* at least zero.
	* alpha double precision scalar multiplier applied to A*x.
	* AP double precision array with at least ((n * (n + 1)) / 2) elements
	. If
	* uplo == 'U' or 'u', the array AP contains the upper triangular pa
	rt
	* of the symmetric matrix A, packed sequentially, column by column;
	* that is, if i <= j, then A[i,j] is stored is AP[i+(j*(j+1)/2)]. I
	f
	* uplo == 'L' or 'L', the array AP contains the lower triangular pa
	rt
	* of the symmetric matrix A, packed sequentially, column by column;
	* that is, if i >= j, then A[i,j] is stored in AP[i+((2*n-j+1)*j)/2
	].
	* x double precision array of length at least (1 + (n - 1) * abs(incx
	)).
	* incx storage spacing between elements of x. incx must not be zero.
	* beta double precision scalar multiplier applied to vector y;
	* y double precision array of length at least (1 + (n - 1) * abs(incy
	)).
	* If beta is zero, y is not read.
	* incy storage spacing between elements of y. incy must not be zero.
	*
	* Output
	* ------
	* y updated according to y = alpha*A*x + beta*y
	*
	* Reference: http://www.netlib.org/blas/dspmv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, or if incx or incy == 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasDspmv (char uplo, int n, double alpha,
	const double *AP, const double *x,
	int incx, double beta, double *y, int incy);
	/* ---------------- CUBLAS double precision BLAS3 functions --------------- - */		}
	/*		static __inline__ void CUBLASAPI cublasCtrsm (char side, char uplo, char tr
	* void		ansa, char diag,
	* cublasDgemm (char transa, char transb, int m, int n, int k, double alpha		int m, int n, cuComplex alpha, const cuComplex
	,		*A,
	* const double *A, int lda, const double *B, int ldb,		int lda, cuComplex *B, int ldb)
	* double beta, double *C, int ldc)		{
	*		cublasHandle_t handle = cublasGetCurrentCtx();
	* computes the product of matrix A and matrix B, multiplies the result		cublasStatus_t error = cublasCtrsm_v2(handle, convertToSideMode(side),c
	* by scalar alpha, and adds the sum to the product of matrix C and		onvertToFillMode(uplo),
	* scalar beta. It performs one of the matrix-matrix operations:		convertToOp(transa), convertToDiagType(d
	*		iag), m, n, &alpha, A, lda, B, ldb );
	* C = alpha * op(A) * op(B) + beta * C,		cublasSetError(error);
	* where op(X) = X or op(X) = transpose(X),
	*
	* and alpha and beta are double-precision scalars. A, B and C are matrices
	* consisting of double-precision elements, with op(A) an m x k matrix,
	* op(B) a k x n matrix, and C an m x n matrix. Matrices A, B, and C are
	* stored in column-major format, and lda, ldb, and ldc are the leading
	* dimensions of the two-dimensional arrays containing A, B, and C.
	*
	* Input
	* -----
	* transa specifies op(A). If transa == 'N' or 'n', op(A) = A.
	* If transa == 'T', 't', 'C', or 'c', op(A) = transpose(A).
	* transb specifies op(B). If transb == 'N' or 'n', op(B) = B.
	* If transb == 'T', 't', 'C', or 'c', op(B) = transpose(B).
	* m number of rows of matrix op(A) and rows of matrix C; m must be at
	* least zero.
	* n number of columns of matrix op(B) and number of columns of C;
	* n must be at least zero.
	* k number of columns of matrix op(A) and number of rows of op(B);
	* k must be at least zero.
	* alpha double-precision scalar multiplier applied to op(A) * op(B).
	* A double-precision array of dimensions (lda, k) if transa == 'N' or
	* 'n', and of dimensions (lda, m) otherwise. If transa == 'N' or
	* 'n' lda must be at least max(1, m), otherwise lda must be at
	* least max(1, k).
	* lda leading dimension of two-dimensional array used to store matrix A
	.
	* B double-precision array of dimensions (ldb, n) if transb == 'N' or
	* 'n', and of dimensions (ldb, k) otherwise. If transb == 'N' or
	* 'n' ldb must be at least max (1, k), otherwise ldb must be at
	* least max(1, n).
	* ldb leading dimension of two-dimensional array used to store matrix B
	.
	* beta double-precision scalar multiplier applied to C. If zero, C does
	not
	* have to be a valid input
	* C double-precision array of dimensions (ldc, n); ldc must be at lea
	st
	* max(1, m).
	* ldc leading dimension of two-dimensional array used to store matrix C
	.
	*
	* Output
	* ------
	* C updated based on C = alpha * op(A)*op(B) + beta * C.
	*
	* Reference: http://www.netlib.org/blas/sgemm.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS was not initialized
	* CUBLAS_STATUS_INVALID_VALUE if m < 0, n < 0, or k < 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasDgemm (char transa, char transb, int m, int n, int k,
	double alpha, const double *A, int lda,
	const double *B, int ldb, double beta, double *
	C,
	int ldc);
	/*		}
	* void
	* cublasDtrsm (char side, char uplo, char transa, char diag, int m, int n,
	* double alpha, const double *A, int lda, double *B, int ldb)
	*
	* solves one of the matrix equations
	*
	* op(A) * X = alpha * B, or X * op(A) = alpha * B,
	*
	* where alpha is a double precision scalar, and X and B are m x n matrices
	* that are composed of double precision elements. A is a unit or non-unit,
	* upper or lower triangular matrix, and op(A) is one of
	*
	* op(A) = A or op(A) = transpose(A)
	*
	* The result matrix X overwrites input matrix B; that is, on exit the resu
	lt
	* is stored in B. Matrices A and B are stored in column major format, and
	* lda and ldb are the leading dimensions of the two-dimensonials arrays th
	at
	* contain A and B, respectively.
	*
	* Input
	* -----
	* side specifies whether op(A) appears on the left or right of X as
	* follows: side = 'L' or 'l' indicates solve op(A) * X = alpha * B.
	* side = 'R' or 'r' indicates solve X * op(A) = alpha * B.
	* uplo specifies whether the matrix A is an upper or lower triangular
	* matrix as follows: uplo = 'U' or 'u' indicates A is an upper
	* triangular matrix. uplo = 'L' or 'l' indicates A is a lower
	* triangular matrix.
	* transa specifies the form of op(A) to be used in matrix multiplication
	* as follows: If transa = 'N' or 'N', then op(A) = A. If transa =
	* 'T', 't', 'C', or 'c', then op(A) = transpose(A).
	* diag specifies whether or not A is a unit triangular matrix like so:
	* if diag = 'U' or 'u', A is assumed to be unit triangular. If
	* diag = 'N' or 'n', then A is not assumed to be unit triangular.
	* m specifies the number of rows of B. m must be at least zero.
	* n specifies the number of columns of B. n must be at least zero.
	* alpha is a double precision scalar to be multiplied with B. When alpha
	is
	* zero, then A is not referenced and B need not be set before entry
	.
	* A is a double precision array of dimensions (lda, k), where k is
	* m when side = 'L' or 'l', and is n when side = 'R' or 'r'. If
	* uplo = 'U' or 'u', the leading k x k upper triangular part of
	* the array A must contain the upper triangular matrix and the
	* strictly lower triangular matrix of A is not referenced. When
	* uplo = 'L' or 'l', the leading k x k lower triangular part of
	* the array A must contain the lower triangular matrix and the
	* strictly upper triangular part of A is not referenced. Note that
	* when diag = 'U' or 'u', the diagonal elements of A are not
	* referenced, and are assumed to be unity.
	* lda is the leading dimension of the two dimensional array containing
	A.
	* When side = 'L' or 'l' then lda must be at least max(1, m), when
	* side = 'R' or 'r' then lda must be at least max(1, n).
	* B is a double precision array of dimensions (ldb, n). ldb must be
	* at least max (1,m). The leading m x n part of the array B must
	* contain the right-hand side matrix B. On exit B is overwritten
	* by the solution matrix X.
	* ldb is the leading dimension of the two dimensional array containing
	B.
	* ldb must be at least max(1, m).
	*
	* Output
	* ------
	* B contains the solution matrix X satisfying op(A) * X = alpha * B,
	* or X * op(A) = alpha * B
	*
	* Reference: http://www.netlib.org/blas/dtrsm.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if m or n < 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasDtrsm (char side, char uplo, char transa,
	char diag, int m, int n, double alpha,
	const double *A, int lda, double *B,
	int ldb);
	/*		static __inline__ void CUBLASAPI cublasZtrsm (char side, char uplo, char tr
	* void		ansa,
	* cublasZtrsm (char side, char uplo, char transa, char diag, int m, int n,
	* cuDoubleComplex alpha, const cuDoubleComplex *A, int lda,
	* cuDoubleComplex *B, int ldb)
	*
	* solves one of the matrix equations
	*
	* op(A) * X = alpha * B, or X * op(A) = alpha * B,
	*
	* where alpha is a double precision complex scalar, and X and B are m x n
	matrices
	* that are composed of double precision complex elements. A is a unit or n
	on-unit,
	* upper or lower triangular matrix, and op(A) is one of
	*
	* op(A) = A or op(A) = transpose(A) or op( A ) = conj( A' ).
	*
	* The result matrix X overwrites input matrix B; that is, on exit the resu
	lt
	* is stored in B. Matrices A and B are stored in column major format, and
	* lda and ldb are the leading dimensions of the two-dimensonials arrays th
	at
	* contain A and B, respectively.
	*
	* Input
	* -----
	* side specifies whether op(A) appears on the left or right of X as
	* follows: side = 'L' or 'l' indicates solve op(A) * X = alpha * B.
	* side = 'R' or 'r' indicates solve X * op(A) = alpha * B.
	* uplo specifies whether the matrix A is an upper or lower triangular
	* matrix as follows: uplo = 'U' or 'u' indicates A is an upper
	* triangular matrix. uplo = 'L' or 'l' indicates A is a lower
	* triangular matrix.
	* transa specifies the form of op(A) to be used in matrix multiplication
	* as follows: If transa = 'N' or 'N', then op(A) = A. If transa =
	* 'T', 't', 'C', or 'c', then op(A) = transpose(A).
	* diag specifies whether or not A is a unit triangular matrix like so:
	* if diag = 'U' or 'u', A is assumed to be unit triangular. If
	* diag = 'N' or 'n', then A is not assumed to be unit triangular.
	* m specifies the number of rows of B. m must be at least zero.
	* n specifies the number of columns of B. n must be at least zero.
	* alpha is a double precision complex scalar to be multiplied with B. Whe
	n alpha is
	* zero, then A is not referenced and B need not be set before entry
	.
	* A is a double precision complex array of dimensions (lda, k), where
	k is
	* m when side = 'L' or 'l', and is n when side = 'R' or 'r'. If
	* uplo = 'U' or 'u', the leading k x k upper triangular part of
	* the array A must contain the upper triangular matrix and the
	* strictly lower triangular matrix of A is not referenced. When
	* uplo = 'L' or 'l', the leading k x k lower triangular part of
	* the array A must contain the lower triangular matrix and the
	* strictly upper triangular part of A is not referenced. Note that
	* when diag = 'U' or 'u', the diagonal elements of A are not
	* referenced, and are assumed to be unity.
	* lda is the leading dimension of the two dimensional array containing
	A.
	* When side = 'L' or 'l' then lda must be at least max(1, m), when
	* side = 'R' or 'r' then lda must be at least max(1, n).
	* B is a double precision complex array of dimensions (ldb, n). ldb m
	ust be
	* at least max (1,m). The leading m x n part of the array B must
	* contain the right-hand side matrix B. On exit B is overwritten
	* by the solution matrix X.
	* ldb is the leading dimension of the two dimensional array containing
	B.
	* ldb must be at least max(1, m).
	*
	* Output
	* ------
	* B contains the solution matrix X satisfying op(A) * X = alpha * B,
	* or X * op(A) = alpha * B
	*
	* Reference: http://www.netlib.org/blas/ztrsm.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if m or n < 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZtrsm (char side, char uplo, char transa,
	char diag, int m, int n, cuDoubleComplex alpha,		char diag, int m, int n, cuDoubleComplex alpha,
	const cuDoubleComplex *A, int lda,		const cuDoubleComplex *A, int lda,
	cuDoubleComplex *B, int ldb);		cuDoubleComplex *B, int ldb)
			{
			cublasHandle_t handle = cublasGetCurrentCtx();
			cublasStatus_t error = cublasZtrsm_v2(handle, convertToSideMode(side),c
			onvertToFillMode(uplo),
			convertToOp(transa), convertToDiagType(d
			iag), m, n, &alpha, A, lda, B, ldb );
			cublasSetError(error);
	/*		}
	* void		/*------------------------------------------------------------------------*
	* cublasDtrmm (char side, char uplo, char transa, char diag, int m, int n,		/
	* double alpha, const double *A, int lda, const double *B, in		/* TRMM*/
	t ldb)		static __inline__ void CUBLASAPI cublasStrmm (char side, char uplo, char tr
	*		ansa, char diag,
	* performs one of the matrix-matrix operations		int m, int n, float alpha, const float *A, int
	*		lda,
	* B = alpha * op(A) * B, or B = alpha * B * op(A)		float *B, int ldb)
	*		{
	* where alpha is a double-precision scalar, B is an m x n matrix composed		cublasHandle_t handle = cublasGetCurrentCtx();
	* of double precision elements, and A is a unit or non-unit, upper or lowe		cublasStatus_t error = cublasStrmm_v2(handle, convertToSideMode(side),c
	r,		onvertToFillMode(uplo),
	* triangular matrix composed of double precision elements. op(A) is one of		convertToOp(transa), convertToDiagType(d
	*		iag), m, n, &alpha, A, lda, B, ldb, B, ldb);
	* op(A) = A or op(A) = transpose(A)		cublasSetError(error);
	*		}
	* Matrices A and B are stored in column major format, and lda and ldb are		static __inline__ void CUBLASAPI cublasDtrmm (char side, char uplo, char tr
	* the leading dimensions of the two-dimensonials arrays that contain A and		ansa,
	* B, respectively.
	*
	* Input
	* -----
	* side specifies whether op(A) multiplies B from the left or right.
	* If side = 'L' or 'l', then B = alpha * op(A) * B. If side =
	* 'R' or 'r', then B = alpha * B * op(A).
	* uplo specifies whether the matrix A is an upper or lower triangular
	* matrix. If uplo = 'U' or 'u', A is an upper triangular matrix.
	* If uplo = 'L' or 'l', A is a lower triangular matrix.
	* transa specifies the form of op(A) to be used in the matrix
	* multiplication. If transa = 'N' or 'n', then op(A) = A. If
	* transa = 'T', 't', 'C', or 'c', then op(A) = transpose(A).
	* diag specifies whether or not A is unit triangular. If diag = 'U'
	* or 'u', A is assumed to be unit triangular. If diag = 'N' or
	* 'n', A is not assumed to be unit triangular.
	* m the number of rows of matrix B. m must be at least zero.
	* n the number of columns of matrix B. n must be at least zero.
	* alpha double precision scalar multiplier applied to op(A)*B, or
	* B*op(A), respectively. If alpha is zero no accesses are made
	* to matrix A, and no read accesses are made to matrix B.
	* A double precision array of dimensions (lda, k). k = m if side =
	* 'L' or 'l', k = n if side = 'R' or 'r'. If uplo = 'U' or 'u'
	* the leading k x k upper triangular part of the array A must
	* contain the upper triangular matrix, and the strictly lower
	* triangular part of A is not referenced. If uplo = 'L' or 'l'
	* the leading k x k lower triangular part of the array A must
	* contain the lower triangular matrix, and the strictly upper
	* triangular part of A is not referenced. When diag = 'U' or 'u'
	* the diagonal elements of A are no referenced and are assumed
	* to be unity.
	* lda leading dimension of A. When side = 'L' or 'l', it must be at
	* least max(1,m) and at least max(1,n) otherwise
	* B double precision array of dimensions (ldb, n). On entry, the
	* leading m x n part of the array contains the matrix B. It is
	* overwritten with the transformed matrix on exit.
	* ldb leading dimension of B. It must be at least max (1, m).
	*
	* Output
	* ------
	* B updated according to B = alpha * op(A) * B or B = alpha * B * op
	(A)
	*
	* Reference: http://www.netlib.org/blas/dtrmm.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if m or n < 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasDtrmm (char side, char uplo, char transa,
	char diag, int m, int n, double alpha,		char diag, int m, int n, double alpha,
	const double *A, int lda, double *B,		const double *A, int lda, double *B,
	int ldb);		int ldb)
			{
	/*		cublasHandle_t handle = cublasGetCurrentCtx();
	* void		cublasStatus_t error = cublasDtrmm_v2(handle, convertToSideMode(side),c
	* cublasDsymm (char side, char uplo, int m, int n, double alpha,		onvertToFillMode(uplo),
	* const double *A, int lda, const double *B, int ldb,		convertToOp(transa), convertToDiagType(d
	* double beta, double *C, int ldc);		iag), m, n, &alpha, A, lda, B, ldb, B, ldb );
	*		cublasSetError(error);
	* performs one of the matrix-matrix operations		}
	*		static __inline__ void CUBLASAPI cublasCtrmm (char side, char uplo, char tr
	* C = alpha * A * B + beta * C, or		ansa, char diag,
	* C = alpha * B * A + beta * C,		int m, int n, cuComplex alpha, const cuComplex
	*		*A,
	* where alpha and beta are double precision scalars, A is a symmetric matr		int lda, cuComplex *B, int ldb)
	ix		{
	* consisting of double precision elements and stored in either lower or up		cublasHandle_t handle = cublasGetCurrentCtx();
	per		cublasStatus_t error = cublasCtrmm_v2(handle, convertToSideMode(side),c
	* storage mode, and B and C are m x n matrices consisting of double precis		onvertToFillMode(uplo),
	ion		convertToOp(transa), convertToDiagType(d
	* elements.		iag), m, n, &alpha, A, lda, B, ldb, B, ldb );
	*		cublasSetError(error);
	* Input
	* -----
	* side specifies whether the symmetric matrix A appears on the left side
	* hand side or right hand side of matrix B, as follows. If side ==
	'L'
	* or 'l', then C = alpha * A * B + beta * C. If side = 'R' or 'r',
	* then C = alpha * B * A + beta * C.
	* uplo specifies whether the symmetric matrix A is stored in upper or lo
	wer
	* storage mode, as follows. If uplo == 'U' or 'u', only the upper
	* triangular part of the symmetric matrix is to be referenced, and
	the
	* elements of the strictly lower triangular part are to be infered
	from
	* those in the upper triangular part. If uplo == 'L' or 'l', only t
	he
	* lower triangular part of the symmetric matrix is to be referenced
	,
	* and the elements of the strictly upper triangular part are to be
	* infered from those in the lower triangular part.
	* m specifies the number of rows of the matrix C, and the number of r
	ows
	* of matrix B. It also specifies the dimensions of symmetric matrix
	A
	* when side == 'L' or 'l'. m must be at least zero.
	* n specifies the number of columns of the matrix C, and the number o
	f
	* columns of matrix B. It also specifies the dimensions of symmetri
	c
	* matrix A when side == 'R' or 'r'. n must be at least zero.
	* alpha double precision scalar multiplier applied to A * B, or B * A
	* A double precision array of dimensions (lda, ka), where ka is m whe
	n
	* side == 'L' or 'l' and is n otherwise. If side == 'L' or 'l' the
	* leading m x m part of array A must contain the symmetric matrix,
	* such that when uplo == 'U' or 'u', the leading m x m part stores
	the
	* upper triangular part of the symmetric matrix, and the strictly l
	ower
	* triangular part of A is not referenced, and when uplo == 'U' or '
	u',
	* the leading m x m part stores the lower triangular part of the
	* symmetric matrix and the strictly upper triangular part is not
	* referenced. If side == 'R' or 'r' the leading n x n part of array
	A
	* must contain the symmetric matrix, such that when uplo == 'U' or
	'u',
	* the leading n x n part stores the upper triangular part of the
	* symmetric matrix and the strictly lower triangular part of A is n
	ot
	* referenced, and when uplo == 'U' or 'u', the leading n x n part
	* stores the lower triangular part of the symmetric matrix and the
	* strictly upper triangular part is not referenced.
	* lda leading dimension of A. When side == 'L' or 'l', it must be at le
	ast
	* max(1, m) and at least max(1, n) otherwise.
	* B double precision array of dimensions (ldb, n). On entry, the lead
	ing
	* m x n part of the array contains the matrix B.
	* ldb leading dimension of B. It must be at least max (1, m).
	* beta double precision scalar multiplier applied to C. If beta is zero,
	C
	* does not have to be a valid input
	* C double precision array of dimensions (ldc, n)
	* ldc leading dimension of C. Must be at least max(1, m)
	*
	* Output
	* ------
	* C updated according to C = alpha * A * B + beta * C, or C = alpha *
	* B * A + beta * C
	*
	* Reference: http://www.netlib.org/blas/dsymm.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if m or n are < 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasDsymm (char side, char uplo, int m, int n,
	double alpha, const double *A, int lda,
	const double *B, int ldb, double beta,
	double *C, int ldc);
	/*
	* void
	* cublasZsymm (char side, char uplo, int m, int n, cuDoubleComplex alpha,
	* const cuDoubleComplex *A, int lda, const cuDoubleComplex *B
	, int ldb,
	* cuDoubleComplex beta, cuDoubleComplex *C, int ldc);
	*
	* performs one of the matrix-matrix operations
	*
	* C = alpha * A * B + beta * C, or
	* C = alpha * B * A + beta * C,
	*
	* where alpha and beta are double precision complex scalars, A is a symmet
	ric matrix
	* consisting of double precision complex elements and stored in either low
	er or upper
	* storage mode, and B and C are m x n matrices consisting of double precis
	ion
	* complex elements.
	*
	* Input
	* -----
	* side specifies whether the symmetric matrix A appears on the left side
	* hand side or right hand side of matrix B, as follows. If side ==
	'L'
	* or 'l', then C = alpha * A * B + beta * C. If side = 'R' or 'r',
	* then C = alpha * B * A + beta * C.
	* uplo specifies whether the symmetric matrix A is stored in upper or lo
	wer
	* storage mode, as follows. If uplo == 'U' or 'u', only the upper
	* triangular part of the symmetric matrix is to be referenced, and
	the
	* elements of the strictly lower triangular part are to be infered
	from
	* those in the upper triangular part. If uplo == 'L' or 'l', only t
	he
	* lower triangular part of the symmetric matrix is to be referenced
	,
	* and the elements of the strictly upper triangular part are to be
	* infered from those in the lower triangular part.
	* m specifies the number of rows of the matrix C, and the number of r
	ows
	* of matrix B. It also specifies the dimensions of symmetric matrix
	A
	* when side == 'L' or 'l'. m must be at least zero.
	* n specifies the number of columns of the matrix C, and the number o
	f
	* columns of matrix B. It also specifies the dimensions of symmetri
	c
	* matrix A when side == 'R' or 'r'. n must be at least zero.
	* alpha double precision scalar multiplier applied to A * B, or B * A
	* A double precision array of dimensions (lda, ka), where ka is m whe
	n
	* side == 'L' or 'l' and is n otherwise. If side == 'L' or 'l' the
	* leading m x m part of array A must contain the symmetric matrix,
	* such that when uplo == 'U' or 'u', the leading m x m part stores
	the
	* upper triangular part of the symmetric matrix, and the strictly l
	ower
	* triangular part of A is not referenced, and when uplo == 'U' or '
	u',
	* the leading m x m part stores the lower triangular part of the
	* symmetric matrix and the strictly upper triangular part is not
	* referenced. If side == 'R' or 'r' the leading n x n part of array
	A
	* must contain the symmetric matrix, such that when uplo == 'U' or
	'u',
	* the leading n x n part stores the upper triangular part of the
	* symmetric matrix and the strictly lower triangular part of A is n
	ot
	* referenced, and when uplo == 'U' or 'u', the leading n x n part
	* stores the lower triangular part of the symmetric matrix and the
	* strictly upper triangular part is not referenced.
	* lda leading dimension of A. When side == 'L' or 'l', it must be at le
	ast
	* max(1, m) and at least max(1, n) otherwise.
	* B double precision array of dimensions (ldb, n). On entry, the lead
	ing
	* m x n part of the array contains the matrix B.
	* ldb leading dimension of B. It must be at least max (1, m).
	* beta double precision scalar multiplier applied to C. If beta is zero,
	C
	* does not have to be a valid input
	* C double precision array of dimensions (ldc, n)
	* ldc leading dimension of C. Must be at least max(1, m)
	*
	* Output
	* ------
	* C updated according to C = alpha * A * B + beta * C, or C = alpha *
	* B * A + beta * C
	*
	* Reference: http://www.netlib.org/blas/zsymm.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if m or n are < 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZsymm (char side, char uplo, int m, int n,
	cuDoubleComplex alpha, const cuDoubleComplex *A
	, int lda,
	const cuDoubleComplex *B, int ldb, cuDoubleComp
	lex beta,
	cuDoubleComplex *C, int ldc);
	/*
	* void
	* cublasDsyrk (char uplo, char trans, int n, int k, double alpha,
	* const double *A, int lda, double beta, double *C, int ldc)
	*
	* performs one of the symmetric rank k operations
	*
	* C = alpha * A * transpose(A) + beta * C, or
	* C = alpha * transpose(A) * A + beta * C.
	*
	* Alpha and beta are double precision scalars. C is an n x n symmetric mat
	rix
	* consisting of double precision elements and stored in either lower or
	* upper storage mode. A is a matrix consisting of double precision element
	s
	* with dimension of n x k in the first case, and k x n in the second case.
	*
	* Input
	* -----
	* uplo specifies whether the symmetric matrix C is stored in upper or lo
	wer
	* storage mode as follows. If uplo == 'U' or 'u', only the upper
	* triangular part of the symmetric matrix is to be referenced, and
	the
	* elements of the strictly lower triangular part are to be infered
	from
	* those in the upper triangular part. If uplo == 'L' or 'l', only t
	he
	* lower triangular part of the symmetric matrix is to be referenced
	,
	* and the elements of the strictly upper triangular part are to be
	* infered from those in the lower triangular part.
	* trans specifies the operation to be performed. If trans == 'N' or 'n',
	C =
	* alpha * transpose(A) + beta * C. If trans == 'T', 't', 'C', or 'c
	',
	* C = transpose(A) * A + beta * C.
	* n specifies the number of rows and the number columns of matrix C.
	If
	* trans == 'N' or 'n', n specifies the number of rows of matrix A.
	If
	* trans == 'T', 't', 'C', or 'c', n specifies the columns of matrix
	A.
	* n must be at least zero.
	* k If trans == 'N' or 'n', k specifies the number of rows of matrix
	A.
	* If trans == 'T', 't', 'C', or 'c', k specifies the number of rows
	of
	* matrix A. k must be at least zero.
	* alpha double precision scalar multiplier applied to A * transpose(A) or
	* transpose(A) * A.
	* A double precision array of dimensions (lda, ka), where ka is k whe
	n
	* trans == 'N' or 'n', and is n otherwise. When trans == 'N' or 'n'
	,
	* the leading n x k part of array A must contain the matrix A,
	* otherwise the leading k x n part of the array must contains the
	* matrix A.
	* lda leading dimension of A. When trans == 'N' or 'n' then lda must be
	at
	* least max(1, n). Otherwise lda must be at least max(1, k).
	* beta double precision scalar multiplier applied to C. If beta izs zero
	, C
	* does not have to be a valid input
	* C double precision array of dimensions (ldc, n). If uplo = 'U' or '
	u',
	* the leading n x n triangular part of the array C must contain the
	* upper triangular part of the symmetric matrix C and the strictly
	* lower triangular part of C is not referenced. On exit, the upper
	* triangular part of C is overwritten by the upper triangular part
	of
	* the updated matrix. If uplo = 'L' or 'l', the leading n x n
	* triangular part of the array C must contain the lower triangular
	part
	* of the symmetric matrix C and the strictly upper triangular part
	of C
	* is not referenced. On exit, the lower triangular part of C is
	* overwritten by the lower triangular part of the updated matrix.
	* ldc leading dimension of C. It must be at least max(1, n).
	*
	* Output
	* ------
	* C updated according to C = alpha * A * transpose(A) + beta * C, or
	C =
	* alpha * transpose(A) * A + beta * C
	*
	* Reference: http://www.netlib.org/blas/dsyrk.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0 or k < 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasDsyrk (char uplo, char trans, int n, int k,
	double alpha, const double *A, int lda,
	double beta, double *C, int ldc);
	/*
	* void
	* cublasZsyrk (char uplo, char trans, int n, int k, cuDoubleComplex alpha,
	* const cuDoubleComplex *A, int lda, cuDoubleComplex beta, cu
	DoubleComplex *C, int ldc)
	*
	* performs one of the symmetric rank k operations
	*
	* C = alpha * A * transpose(A) + beta * C, or
	* C = alpha * transpose(A) * A + beta * C.
	*
	* Alpha and beta are double precision complex scalars. C is an n x n symme
	tric matrix
	* consisting of double precision complex elements and stored in either low
	er or
	* upper storage mode. A is a matrix consisting of double precision complex
	elements
	* with dimension of n x k in the first case, and k x n in the second case.
	*
	* Input
	* -----
	* uplo specifies whether the symmetric matrix C is stored in upper or lo
	wer
	* storage mode as follows. If uplo == 'U' or 'u', only the upper
	* triangular part of the symmetric matrix is to be referenced, and
	the
	* elements of the strictly lower triangular part are to be infered
	from
	* those in the upper triangular part. If uplo == 'L' or 'l', only t
	he
	* lower triangular part of the symmetric matrix is to be referenced
	,
	* and the elements of the strictly upper triangular part are to be
	* infered from those in the lower triangular part.
	* trans specifies the operation to be performed. If trans == 'N' or 'n',
	C =
	* alpha * transpose(A) + beta * C. If trans == 'T', 't', 'C', or 'c
	',
	* C = transpose(A) * A + beta * C.
	* n specifies the number of rows and the number columns of matrix C.
	If
	* trans == 'N' or 'n', n specifies the number of rows of matrix A.
	If
	* trans == 'T', 't', 'C', or 'c', n specifies the columns of matrix
	A.
	* n must be at least zero.
	* k If trans == 'N' or 'n', k specifies the number of rows of matrix
	A.
	* If trans == 'T', 't', 'C', or 'c', k specifies the number of rows
	of
	* matrix A. k must be at least zero.
	* alpha double precision complex scalar multiplier applied to A * transpo
	se(A) or
	* transpose(A) * A.
	* A double precision complex array of dimensions (lda, ka), where ka
	is k when
	* trans == 'N' or 'n', and is n otherwise. When trans == 'N' or 'n'
	,
	* the leading n x k part of array A must contain the matrix A,
	* otherwise the leading k x n part of the array must contains the
	* matrix A.
	* lda leading dimension of A. When trans == 'N' or 'n' then lda must be
	at
	* least max(1, n). Otherwise lda must be at least max(1, k).
	* beta double precision complex scalar multiplier applied to C. If beta
	izs zero, C
	* does not have to be a valid input
	* C double precision complex array of dimensions (ldc, n). If uplo =
	'U' or 'u',
	* the leading n x n triangular part of the array C must contain the
	* upper triangular part of the symmetric matrix C and the strictly
	* lower triangular part of C is not referenced. On exit, the upper
	* triangular part of C is overwritten by the upper triangular part
	of
	* the updated matrix. If uplo = 'L' or 'l', the leading n x n
	* triangular part of the array C must contain the lower triangular
	part
	* of the symmetric matrix C and the strictly upper triangular part
	of C
	* is not referenced. On exit, the lower triangular part of C is
	* overwritten by the lower triangular part of the updated matrix.
	* ldc leading dimension of C. It must be at least max(1, n).
	*
	* Output
	* ------
	* C updated according to C = alpha * A * transpose(A) + beta * C, or
	C =
	* alpha * transpose(A) * A + beta * C
	*
	* Reference: http://www.netlib.org/blas/zsyrk.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0 or k < 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZsyrk (char uplo, char trans, int n, int k,
	cuDoubleComplex alpha,
	const cuDoubleComplex *A, int lda,
	cuDoubleComplex beta,
	cuDoubleComplex *C, int ldc);
	/*
	* void
	* cublasZsyr2k (char uplo, char trans, int n, int k, cuDoubleComplex alpha
	,
	* const cuDoubleComplex *A, int lda, const cuDoubleComplex *
	B, int ldb,
	* cuDoubleComplex beta, cuDoubleComplex *C, int ldc)
	*
	* performs one of the symmetric rank 2k operations
	*
	* C = alpha * A * transpose(B) + alpha * B * transpose(A) + beta * C, o
	r
	* C = alpha * transpose(A) * B + alpha * transpose(B) * A + beta * C.
	*
	* Alpha and beta are double precision complex scalars. C is an n x n symme
	tric matrix
	* consisting of double precision complex elements and stored in either low
	er or upper
	* storage mode. A and B are matrices consisting of double precision comple
	x elements
	* with dimension of n x k in the first case, and k x n in the second case.
	*
	* Input
	* -----
	* uplo specifies whether the symmetric matrix C is stored in upper or lo
	wer
	* storage mode, as follows. If uplo == 'U' or 'u', only the upper
	* triangular part of the symmetric matrix is to be referenced, and
	the
	* elements of the strictly lower triangular part are to be infered
	from
	* those in the upper triangular part. If uplo == 'L' or 'l', only t
	he
	* lower triangular part of the symmetric matrix is to be references
	,
	* and the elements of the strictly upper triangular part are to be
	* infered from those in the lower triangular part.
	* trans specifies the operation to be performed. If trans == 'N' or 'n',
	* C = alpha * A * transpose(B) + alpha * B * transpose(A) + beta *
	C,
	* If trans == 'T', 't', 'C', or 'c', C = alpha * transpose(A) * B +
	* alpha * transpose(B) * A + beta * C.
	* n specifies the number of rows and the number columns of matrix C.
	If
	* trans == 'N' or 'n', n specifies the number of rows of matrix A.
	If
	* trans == 'T', 't', 'C', or 'c', n specifies the columns of matrix
	A.
	* n must be at least zero.
	* k If trans == 'N' or 'n', k specifies the number of rows of matrix
	A.
	* If trans == 'T', 't', 'C', or 'c', k specifies the number of rows
	of
	* matrix A. k must be at least zero.
	* alpha double precision scalar multiplier.
	* A double precision array of dimensions (lda, ka), where ka is k whe
	n
	* trans == 'N' or 'n', and is n otherwise. When trans == 'N' or 'n'
	,
	* the leading n x k part of array A must contain the matrix A,
	* otherwise the leading k x n part of the array must contain the ma
	trix
	* A.
	* lda leading dimension of A. When trans == 'N' or 'n' then lda must be
	at
	* least max(1, n). Otherwise lda must be at least max(1,k).
	* B double precision array of dimensions (lda, kb), where kb is k whe
	n
	* trans == 'N' or 'n', and is n otherwise. When trans == 'N' or 'n'
	,
	* the leading n x k part of array B must contain the matrix B,
	* otherwise the leading k x n part of the array must contain the ma
	trix
	* B.
	* ldb leading dimension of N. When trans == 'N' or 'n' then ldb must be
	at
	* least max(1, n). Otherwise ldb must be at least max(1, k).
	* beta double precision scalar multiplier applied to C. If beta is zero,
	C
	* does not have to be a valid input.
	* C double precision array of dimensions (ldc, n). If uplo == 'U' or
	'u',
	* the leading n x n triangular part of the array C must contain the
	* upper triangular part of the symmetric matrix C and the strictly
	* lower triangular part of C is not referenced. On exit, the upper
	* triangular part of C is overwritten by the upper triangular part
	of
	* the updated matrix. If uplo == 'L' or 'l', the leading n x n
	* triangular part of the array C must contain the lower triangular
	part
	* of the symmetric matrix C and the strictly upper triangular part
	of C
	* is not referenced. On exit, the lower triangular part of C is
	* overwritten by the lower triangular part of the updated matrix.
	* ldc leading dimension of C. Must be at least max(1, n).
	*
	* Output
	* ------
	* C updated according to alpha*A*transpose(B) + alpha*B*transpose(A)
	+
	* beta*C or alpha*transpose(A)*B + alpha*transpose(B)*A + beta*C
	*
	* Reference: http://www.netlib.org/blas/zsyr2k.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0 or k < 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZsyr2k (char uplo, char trans, int n, int k,
	cuDoubleComplex alpha, const cuDoubleComplex *
	A, int lda,
	const cuDoubleComplex *B, int ldb, cuDoubleCom
	plex beta,
	cuDoubleComplex *C, int ldc);
	/*
	* void
	* cublasZher2k (char uplo, char trans, int n, int k, cuDoubleComplex alpha
	,
	* const cuDoubleComplex *A, int lda, const cuDoubleComplex *
	B, int ldb,
	* double beta, cuDoubleComplex *C, int ldc)
	*
	* performs one of the hermitian rank 2k operations
	*
	* C = alpha * A * conjugate(transpose(B))
	* + conjugate(alpha) * B * conjugate(transpose(A))
	* + beta * C ,
	* or
	* C = alpha * conjugate(transpose(A)) * B
	* + conjugate(alpha) * conjugate(transpose(B)) * A
	* + beta * C.
	*
	* Alpha is double precision complex scalar whereas Beta is a double precis
	ion real scalar.
	* C is an n x n hermitian matrix consisting of double precision complex el
	ements and
	* stored in either lower or upper storage mode. A and B are matrices consi
	sting of
	* double precision complex elements with dimension of n x k in the first c
	ase,
	* and k x n in the second case.
	*
	* Input
	* -----
	* uplo specifies whether the hermitian matrix C is stored in upper or lo
	wer
	* storage mode, as follows. If uplo == 'U' or 'u', only the upper
	* triangular part of the hermitian matrix is to be referenced, and
	the
	* elements of the strictly lower triangular part are to be infered
	from
	* those in the upper triangular part. If uplo == 'L' or 'l', only t
	he
	* lower triangular part of the hermitian matrix is to be references
	,
	* and the elements of the strictly upper triangular part are to be
	* infered from those in the lower triangular part.
	* trans specifies the operation to be performed. If trans == 'N' or 'n',
	* C = alpha * A * conjugate(transpose(B))
	* + conjugate(alpha) * B * conjugate(transpose(A))
	* + beta * C .
	* If trans == 'T', 't', 'C', or 'c',
	* C = alpha * conjugate(transpose(A)) * B
	* + conjugate(alpha) * conjugate(transpose(B)) * A
	* + beta * C.
	* n specifies the number of rows and the number columns of matrix C.
	If
	* trans == 'N' or 'n', n specifies the number of rows of matrix A.
	If
	* trans == 'T', 't', 'C', or 'c', n specifies the columns of matrix
	A.
	* n must be at least zero.
	* k If trans == 'N' or 'n', k specifies the number of rows of matrix
	A.
	* If trans == 'T', 't', 'C', or 'c', k specifies the number of rows
	of
	* matrix A. k must be at least zero.
	* alpha double precision scalar multiplier.
	* A double precision array of dimensions (lda, ka), where ka is k whe
	n
	* trans == 'N' or 'n', and is n otherwise. When trans == 'N' or 'n'
	,
	* the leading n x k part of array A must contain the matrix A,
	* otherwise the leading k x n part of the array must contain the ma
	trix
	* A.
	* lda leading dimension of A. When trans == 'N' or 'n' then lda must be
	at
	* least max(1, n). Otherwise lda must be at least max(1,k).
	* B double precision array of dimensions (lda, kb), where kb is k whe
	n
	* trans == 'N' or 'n', and is n otherwise. When trans == 'N' or 'n'
	,
	* the leading n x k part of array B must contain the matrix B,
	* otherwise the leading k x n part of the array must contain the ma
	trix
	* B.
	* ldb leading dimension of N. When trans == 'N' or 'n' then ldb must be
	at
	* least max(1, n). Otherwise ldb must be at least max(1, k).
	* beta double precision scalar multiplier applied to C. If beta is zero,
	C
	* does not have to be a valid input.
	* C double precision array of dimensions (ldc, n). If uplo == 'U' or
	'u',
	* the leading n x n triangular part of the array C must contain the
	* upper triangular part of the hermitian matrix C and the strictly
	* lower triangular part of C is not referenced. On exit, the upper
	* triangular part of C is overwritten by the upper triangular part
	of
	* the updated matrix. If uplo == 'L' or 'l', the leading n x n
	* triangular part of the array C must contain the lower triangular
	part
	* of the hermitian matrix C and the strictly upper triangular part
	of C
	* is not referenced. On exit, the lower triangular part of C is
	* overwritten by the lower triangular part of the updated matrix.
	* The imaginary parts of the diagonal elements need
	* not be set, they are assumed to be zero, and on exit they
	* are set to zero.
	* ldc leading dimension of C. Must be at least max(1, n).
	*
	* Output
	* ------
	* C updated according to alpha*A*conjugate(transpose(B)) +
	* + conjugate(alpha)*B*conjugate(transpose(A)) + beta*C or
	* alpha*conjugate(transpose(A))*B + conjugate(alpha)*conjugate(tran
	spose(B))*A
	* + beta*C.
	*
	* Reference: http://www.netlib.org/blas/zher2k.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0 or k < 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZher2k (char uplo, char trans, int n, int k,
	cuDoubleComplex alpha, const cuDoubleComplex *
	A, int lda,
	const cuDoubleComplex *B, int ldb, double beta
	,
	cuDoubleComplex *C, int ldc);
	/*
	* void
	* cublasZher (char uplo, int n, double alpha, const cuDoubleComplex *x, in
	t incx,
	* cuDoubleComplex *A, int lda)
	*
	* performs the hermitian rank 1 operation
	*
	* A = alpha * x * conjugate(transpose(x)) + A,
	*
	* where alpha is a double precision real scalar, x is an n element double
	* precision complex vector and A is an n x n hermitian matrix consisting o
	f
	* double precision complex elements. Matrix A is stored in column major fo
	rmat,
	* and lda is the leading dimension of the two-dimensional array
	* containing A.
	*
	* Input
	* -----
	* uplo specifies whether the matrix data is stored in the upper or
	* the lower triangular part of array A. If uplo = 'U' or 'u',
	* then only the upper triangular part of A may be referenced.
	* If uplo = 'L' or 'l', then only the lower triangular part of
	* A may be referenced.
	* n specifies the number of rows and columns of the matrix A. It
	* must be at least 0.
	* alpha double precision real scalar multiplier applied to
	* x * conjugate(transpose(x))
	* x double precision complex array of length at least (1 + (n - 1) *
	abs(incx))
	* incx specifies the storage spacing between elements of x. incx must
	* not be zero.
	* A double precision complex array of dimensions (lda, n). If uplo =
	'U' or
	* 'u', then A must contain the upper triangular part of a hermitian
	* matrix, and the strictly lower triangular part is not referenced.
	* If uplo = 'L' or 'l', then A contains the lower triangular part
	* of a hermitian matrix, and the strictly upper triangular part is
	* not referenced. The imaginary parts of the diagonal elements need
	* not be set, they are assumed to be zero, and on exit they
	* are set to zero.
	* lda leading dimension of the two-dimensional array containing A. lda
	* must be at least max(1, n).
	*
	* Output
	* ------
	* A updated according to A = alpha * x * conjugate(transpose(x)) + A
	*
	* Reference: http://www.netlib.org/blas/zher.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, or incx == 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZher (char uplo, int n, double alpha,
	const cuDoubleComplex *x, int incx, cuDoubleComp
	lex *A,
	int lda);
	/*
	* void
	* cublasZher (char uplo, int n, double alpha, const cuDoubleComplex *x, in
	t incx,
	* cuDoubleComplex *A, int lda)
	*
	* performs the hermitian rank 1 operation
	*
	* A = alpha * x * conjugate(transpose(x) + A,
	*
	* where alpha is a double precision real scalar, x is an n element double
	* precision complex vector and A is an n x n hermitian matrix consisting o
	f
	* double precision complex elements. Matrix A is stored in column major fo
	rmat,
	* and lda is the leading dimension of the two-dimensional array
	* containing A.
	*
	* Input
	* -----
	* uplo specifies whether the matrix data is stored in the upper or
	* the lower triangular part of array A. If uplo = 'U' or 'u',
	* then only the upper triangular part of A may be referenced.
	* If uplo = 'L' or 'l', then only the lower triangular part of
	* A may be referenced.
	* n specifies the number of rows and columns of the matrix A. It
	* must be at least 0.
	* alpha double precision real scalar multiplier applied to
	* x * conjugate(transpose(x))
	* x double precision complex array of length at least (1 + (n - 1) *
	abs(incx))
	* incx specifies the storage spacing between elements of x. incx must
	* not be zero.
	* A double precision complex array of dimensions (lda, n). If uplo =
	'U' or
	* 'u', then A must contain the upper triangular part of a hermitian
	* matrix, and the strictly lower triangular part is not referenced.
	* If uplo = 'L' or 'l', then A contains the lower triangular part
	* of a hermitian matrix, and the strictly upper triangular part is
	* not referenced. The imaginary parts of the diagonal elements need
	* not be set, they are assumed to be zero, and on exit they
	* are set to zero.
	* lda leading dimension of the two-dimensional array containing A. lda
	* must be at least max(1, n).
	*
	* Output
	* ------
	* A updated according to A = alpha * x * conjugate(transpose(x) + A
	*
	* Reference: http://www.netlib.org/blas/zher.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, or incx == 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZher (char uplo, int n, double alpha, const cuDoubleCo
	mplex *x,
	int incx, cuDoubleComplex *A, int lda);
	/*
	* void
	* cublasZhpr (char uplo, int n, double alpha, const cuDoubleComplex *x, in
	t incx,
	* cuDoubleComplex *AP)
	*
	* performs the hermitian rank 1 operation
	*
	* A = alpha * x * conjugate(transpose(x)) + A,
	*
	* where alpha is a double precision real scalar and x is an n element doub
	le
	* precision complex vector. A is a hermitian n x n matrix consisting of do
	uble
	* precision complex elements that is supplied in packed form.
	*
	* Input
	* -----
	* uplo specifies whether the matrix data is stored in the upper or the l
	ower
	* triangular part of array AP. If uplo == 'U' or 'u', then the uppe
	r
	* triangular part of A is supplied in AP. If uplo == 'L' or 'l', th
	en
	* the lower triangular part of A is supplied in AP.
	* n specifies the number of rows and columns of the matrix A. It must
	be
	* at least zero.
	* alpha double precision real scalar multiplier applied to x * conjugate(
	transpose(x)).
	* x double precision array of length at least (1 + (n - 1) * abs(incx
	)).
	* incx storage spacing between elements of x. incx must not be zero.
	* AP double precision complex array with at least ((n * (n + 1)) / 2)
	elements. If
	* uplo == 'U' or 'u', the array AP contains the upper triangular pa
	rt
	* of the hermitian matrix A, packed sequentially, column by column;
	* that is, if i <= j, then A[i,j] is stored is AP[i+(j*(j+1)/2)]. I
	f
	* uplo == 'L' or 'L', the array AP contains the lower triangular pa
	rt
	* of the hermitian matrix A, packed sequentially, column by column;
	* that is, if i >= j, then A[i,j] is stored in AP[i+((2*n-j+1)*j)/2
	].
	* The imaginary parts of the diagonal elements need not be set, the
	y
	* are assumed to be zero, and on exit they are set to zero.
	*
	* Output
	* ------
	* A updated according to A = alpha * x * conjugate(transpose(x)) + A
	*
	* Reference: http://www.netlib.org/blas/zhpr.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, or incx == 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZhpr (char uplo, int n, double alpha,
	const cuDoubleComplex *x, int incx, cuDoubleComp
	lex *AP);
	/*
	* void
	* cublasZhpr2 (char uplo, int n, cuDoubleComplex alpha, const cuDoubleComp
	lex *x, int incx,
	* const cuDoubleComplex *y, int incy, cuDoubleComplex *AP)
	*
	* performs the hermitian rank 2 operation
	*
	* A = alpha*x*conjugate(transpose(y)) + conjugate(alpha)*y*conjugate(tr
	anspose(x)) + A,
	*
	* where alpha is a double precision complex scalar, and x and y are n elem
	ent double
	* precision complex vectors. A is a hermitian n x n matrix consisting of d
	ouble
	* precision complex elements that is supplied in packed form.
	*
	* Input
	* -----
	* uplo specifies whether the matrix data is stored in the upper or the l
	ower
	* triangular part of array A. If uplo == 'U' or 'u', then only the
	* upper triangular part of A may be referenced and the lower triang
	ular
	* part of A is inferred. If uplo == 'L' or 'l', then only the lower
	* triangular part of A may be referenced and the upper triangular p
	art
	* of A is inferred.
	* n specifies the number of rows and columns of the matrix A. It must
	be
	* at least zero.
	* alpha double precision complex scalar multiplier applied to x * conjuga
	te(transpose(y)) +
	* y * conjugate(transpose(x)).
	* x double precision complex array of length at least (1 + (n - 1) *
	abs (incx)).
	* incx storage spacing between elements of x. incx must not be zero.
	* y double precision complex array of length at least (1 + (n - 1) *
	abs (incy)).
	* incy storage spacing between elements of y. incy must not be zero.
	* AP double precision complex array with at least ((n * (n + 1)) / 2)
	elements. If
	* uplo == 'U' or 'u', the array AP contains the upper triangular pa
	rt
	* of the hermitian matrix A, packed sequentially, column by column;
	* that is, if i <= j, then A[i,j] is stored is AP[i+(j*(j+1)/2)]. I
	f
	* uplo == 'L' or 'L', the array AP contains the lower triangular pa
	rt
	* of the hermitian matrix A, packed sequentially, column by column;
	* that is, if i >= j, then A[i,j] is stored in AP[i+((2*n-j+1)*j)/2
	].
	* The imaginary parts of the diagonal elements need not be set, the
	y
	* are assumed to be zero, and on exit they are set to zero.
	*
	* Output
	* ------
	* A updated according to A = alpha*x*conjugate(transpose(y))
	* + conjugate(alpha)*y*conjugate(transpose(x
	))+A
	*
	* Reference: http://www.netlib.org/blas/zhpr2.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, incx == 0, incy == 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZhpr2 (char uplo, int n, cuDoubleComplex alpha,
	const cuDoubleComplex *x, int incx, const cuDou
	bleComplex *y,
	int incy, cuDoubleComplex *AP);
	/*
	* void cublasZher2 (char uplo, int n, cuDoubleComplex alpha, const cuDoubl
	eComplex *x, int incx,
	* const cuDoubleComplex *y, int incy, cuDoubleComplex *A
	, int lda)
	*
	* performs the hermitian rank 2 operation
	*
	* A = alpha*x*conjugate(transpose(y)) + conjugate(alpha)*y*conjugate(tr
	anspose(x)) + A,
	*
	* where alpha is a double precision complex scalar, x and y are n element
	double
	* precision complex vector and A is an n by n hermitian matrix consisting
	of double
	* precision complex elements.
	*
	* Input
	* -----
	* uplo specifies whether the matrix data is stored in the upper or the l
	ower
	* triangular part of array A. If uplo == 'U' or 'u', then only the
	* upper triangular part of A may be referenced and the lower triang
	ular
	* part of A is inferred. If uplo == 'L' or 'l', then only the lower
	* triangular part of A may be referenced and the upper triangular p
	art
	* of A is inferred.
	* n specifies the number of rows and columns of the matrix A. It must
	be
	* at least zero.
	* alpha double precision complex scalar multiplier applied to x * conjuga
	te(transpose(y)) +
	* y * conjugate(transpose(x)).
	* x double precision array of length at least (1 + (n - 1) * abs (inc
	x)).
	* incx storage spacing between elements of x. incx must not be zero.
	* y double precision array of length at least (1 + (n - 1) * abs (inc
	y)).
	* incy storage spacing between elements of y. incy must not be zero.
	* A double precision complex array of dimensions (lda, n). If uplo ==
	'U' or 'u',
	* then A must contains the upper triangular part of a hermitian mat
	rix,
	* and the strictly lower triangular parts is not referenced. If upl
	o ==
	* 'L' or 'l', then A contains the lower triangular part of a hermit
	ian
	* matrix, and the strictly upper triangular part is not referenced.
	* The imaginary parts of the diagonal elements need not be set,
	* they are assumed to be zero, and on exit they are set to zero.
	*
	* lda leading dimension of A. It must be at least max(1, n).
	*
	* Output
	* ------
	* A updated according to A = alpha*x*conjugate(transpose(y))
	* + conjugate(alpha)*y*conjugate(transpose(x
	))+A
	*
	* Reference: http://www.netlib.org/blas/zher2.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0, incx == 0, incy == 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZher2 (char uplo, int n, cuDoubleComplex alpha,
	const cuDoubleComplex *x, int incx, const cuDo
	ubleComplex *y,
	int incy, cuDoubleComplex *A, int lda);
	/*
	* void
	* cublasDsyr2k (char uplo, char trans, int n, int k, double alpha,
	* const double *A, int lda, const double *B, int ldb,
	* double beta, double *C, int ldc)
	*
	* performs one of the symmetric rank 2k operations
	*
	* C = alpha * A * transpose(B) + alpha * B * transpose(A) + beta * C, o
	r
	* C = alpha * transpose(A) * B + alpha * transpose(B) * A + beta * C.
	*
	* Alpha and beta are double precision scalars. C is an n x n symmetric mat
	rix
	* consisting of double precision elements and stored in either lower or up
	per
	* storage mode. A and B are matrices consisting of double precision elemen
	ts
	* with dimension of n x k in the first case, and k x n in the second case.
	*
	* Input
	* -----
	* uplo specifies whether the symmetric matrix C is stored in upper or lo
	wer
	* storage mode, as follows. If uplo == 'U' or 'u', only the upper
	* triangular part of the symmetric matrix is to be referenced, and
	the
	* elements of the strictly lower triangular part are to be infered
	from
	* those in the upper triangular part. If uplo == 'L' or 'l', only t
	he
	* lower triangular part of the symmetric matrix is to be references
	,
	* and the elements of the strictly upper triangular part are to be
	* infered from those in the lower triangular part.
	* trans specifies the operation to be performed. If trans == 'N' or 'n',
	* C = alpha * A * transpose(B) + alpha * B * transpose(A) + beta *
	C,
	* If trans == 'T', 't', 'C', or 'c', C = alpha * transpose(A) * B +
	* alpha * transpose(B) * A + beta * C.
	* n specifies the number of rows and the number columns of matrix C.
	If
	* trans == 'N' or 'n', n specifies the number of rows of matrix A.
	If
	* trans == 'T', 't', 'C', or 'c', n specifies the columns of matrix
	A.
	* n must be at least zero.
	* k If trans == 'N' or 'n', k specifies the number of rows of matrix
	A.
	* If trans == 'T', 't', 'C', or 'c', k specifies the number of rows
	of
	* matrix A. k must be at least zero.
	* alpha double precision scalar multiplier.
	* A double precision array of dimensions (lda, ka), where ka is k whe
	n
	* trans == 'N' or 'n', and is n otherwise. When trans == 'N' or 'n'
	,
	* the leading n x k part of array A must contain the matrix A,
	* otherwise the leading k x n part of the array must contain the ma
	trix
	* A.
	* lda leading dimension of A. When trans == 'N' or 'n' then lda must be
	at
	* least max(1, n). Otherwise lda must be at least max(1,k).
	* B double precision array of dimensions (lda, kb), where kb is k whe
	n
	* trans == 'N' or 'n', and is n otherwise. When trans == 'N' or 'n'
	,
	* the leading n x k part of array B must contain the matrix B,
	* otherwise the leading k x n part of the array must contain the ma
	trix
	* B.
	* ldb leading dimension of N. When trans == 'N' or 'n' then ldb must be
	at
	* least max(1, n). Otherwise ldb must be at least max(1, k).
	* beta double precision scalar multiplier applied to C. If beta is zero,
	C
	* does not have to be a valid input.
	* C double precision array of dimensions (ldc, n). If uplo == 'U' or
	'u',
	* the leading n x n triangular part of the array C must contain the
	* upper triangular part of the symmetric matrix C and the strictly
	* lower triangular part of C is not referenced. On exit, the upper
	* triangular part of C is overwritten by the upper triangular part
	of
	* the updated matrix. If uplo == 'L' or 'l', the leading n x n
	* triangular part of the array C must contain the lower triangular
	part
	* of the symmetric matrix C and the strictly upper triangular part
	of C
	* is not referenced. On exit, the lower triangular part of C is
	* overwritten by the lower triangular part of the updated matrix.
	* ldc leading dimension of C. Must be at least max(1, n).
	*
	* Output
	* ------
	* C updated according to alpha*A*transpose(B) + alpha*B*transpose(A)
	+
	* beta*C or alpha*transpose(A)*B + alpha*transpose(B)*A + beta*C
	*
	* Reference: http://www.netlib.org/blas/dsyr2k.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0 or k < 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasDsyr2k (char uplo, char trans, int n, int k,
	double alpha, const double *A, int lda,
	const double *B, int ldb, double beta,
	double *C, int ldc);
	/*
	* void cublasZgemm (char transa, char transb, int m, int n, int k,
	* cuDoubleComplex alpha, const cuDoubleComplex *A, int l
	da,
	* const cuDoubleComplex *B, int ldb, cuDoubleComplex bet
	a,
	* cuDoubleComplex *C, int ldc)
	*
	* zgemm performs one of the matrix-matrix operations
	*
	* C = alpha * op(A) * op(B) + beta*C,
	*
	* where op(X) is one of
	*
	* op(X) = X or op(X) = transpose or op(X) = conjg(transpose(X))
	*
	* alpha and beta are double-complex scalars, and A, B and C are matrices
	* consisting of double-complex elements, with op(A) an m x k matrix, op(B)
	* a k x n matrix and C an m x n matrix.
	*
	* Input
	* -----
	* transa specifies op(A). If transa == 'N' or 'n', op(A) = A. If transa ==
	* 'T' or 't', op(A) = transpose(A). If transa == 'C' or 'c', op(A)
	=
	* conjg(transpose(A)).
	* transb specifies op(B). If transa == 'N' or 'n', op(B) = B. If transb ==
	* 'T' or 't', op(B) = transpose(B). If transb == 'C' or 'c', op(B)
	=
	* conjg(transpose(B)).
	* m number of rows of matrix op(A) and rows of matrix C. It must be a
	t
	* least zero.
	* n number of columns of matrix op(B) and number of columns of C. It
	* must be at least zero.
	* k number of columns of matrix op(A) and number of rows of op(B). It
	* must be at least zero.
	* alpha double-complex scalar multiplier applied to op(A)op(B)
	* A double-complex array of dimensions (lda, k) if transa == 'N' or
	* 'n'), and of dimensions (lda, m) otherwise.
	* lda leading dimension of A. When transa == 'N' or 'n', it must be at
	* least max(1, m) and at least max(1, k) otherwise.
	* B double-complex array of dimensions (ldb, n) if transb == 'N' or '
	n',
	* and of dimensions (ldb, k) otherwise
	* ldb leading dimension of B. When transb == 'N' or 'n', it must be at
	* least max(1, k) and at least max(1, n) otherwise.
	* beta double-complex scalar multiplier applied to C. If beta is zero, C
	* does not have to be a valid input.
	* C double precision array of dimensions (ldc, n)
	* ldc leading dimension of C. Must be at least max(1, m).
	*
	* Output
	* ------
	* C updated according to C = alpha*op(A)*op(B) + beta*C
	*
	* Reference: http://www.netlib.org/blas/zgemm.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if any of m, n, or k are < 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZgemm (char transa, char transb, int m, int n,
	int k, cuDoubleComplex alpha,
	const cuDoubleComplex *A, int lda,
	const cuDoubleComplex *B, int ldb,
	cuDoubleComplex beta, cuDoubleComplex *C,
	int ldc);
	/*		}
	* void		static __inline__ void CUBLASAPI cublasZtrmm (char side, char uplo, char tr
	* cublasZtrmm (char side, char uplo, char transa, char diag, int m, int n,		ansa,
	* cuDoubleComplex alpha, const cuDoubleComplex *A, int lda, c
	onst cuDoubleComplex *B,
	* int ldb)
	*
	* performs one of the matrix-matrix operations
	*
	* B = alpha * op(A) * B, or B = alpha * B * op(A)
	*
	* where alpha is a double-precision complex scalar, B is an m x n matrix c
	omposed
	* of double precision complex elements, and A is a unit or non-unit, upper
	or lower,
	* triangular matrix composed of double precision complex elements. op(A) i
	s one of
	*
	* op(A) = A , op(A) = transpose(A) or op(A) = conjugate(transpose(A))
	*
	* Matrices A and B are stored in column major format, and lda and ldb are
	* the leading dimensions of the two-dimensonials arrays that contain A and
	* B, respectively.
	*
	* Input
	* -----
	* side specifies whether op(A) multiplies B from the left or right.
	* If side = 'L' or 'l', then B = alpha * op(A) * B. If side =
	* 'R' or 'r', then B = alpha * B * op(A).
	* uplo specifies whether the matrix A is an upper or lower triangular
	* matrix. If uplo = 'U' or 'u', A is an upper triangular matrix.
	* If uplo = 'L' or 'l', A is a lower triangular matrix.
	* transa specifies the form of op(A) to be used in the matrix
	* multiplication. If transa = 'N' or 'n', then op(A) = A. If
	* transa = 'T' or 't', then op(A) = transpose(A).
	* If transa = 'C' or 'c', then op(A) = conjugate(transpose(A)).
	* diag specifies whether or not A is unit triangular. If diag = 'U'
	* or 'u', A is assumed to be unit triangular. If diag = 'N' or
	* 'n', A is not assumed to be unit triangular.
	* m the number of rows of matrix B. m must be at least zero.
	* n the number of columns of matrix B. n must be at least zero.
	* alpha double precision complex scalar multiplier applied to op(A)*B, or
	* B*op(A), respectively. If alpha is zero no accesses are made
	* to matrix A, and no read accesses are made to matrix B.
	* A double precision complex array of dimensions (lda, k). k = m if s
	ide =
	* 'L' or 'l', k = n if side = 'R' or 'r'. If uplo = 'U' or 'u'
	* the leading k x k upper triangular part of the array A must
	* contain the upper triangular matrix, and the strictly lower
	* triangular part of A is not referenced. If uplo = 'L' or 'l'
	* the leading k x k lower triangular part of the array A must
	* contain the lower triangular matrix, and the strictly upper
	* triangular part of A is not referenced. When diag = 'U' or 'u'
	* the diagonal elements of A are no referenced and are assumed
	* to be unity.
	* lda leading dimension of A. When side = 'L' or 'l', it must be at
	* least max(1,m) and at least max(1,n) otherwise
	* B double precision complex array of dimensions (ldb, n). On entry,
	the
	* leading m x n part of the array contains the matrix B. It is
	* overwritten with the transformed matrix on exit.
	* ldb leading dimension of B. It must be at least max (1, m).
	*
	* Output
	* ------
	* B updated according to B = alpha * op(A) * B or B = alpha * B * op
	(A)
	*
	* Reference: http://www.netlib.org/blas/ztrmm.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if m or n < 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZtrmm (char side, char uplo, char transa,
	char diag, int m, int n, cuDoubleComplex alpha,		char diag, int m, int n, cuDoubleComplex alpha,
	const cuDoubleComplex *A, int lda, cuDoubleComp lex *B,		const cuDoubleComplex *A, int lda, cuDoubleComp lex *B,
	int ldb);		int ldb)
			{
	/*		cublasHandle_t handle = cublasGetCurrentCtx();
	* cublasZgeru (int m, int n, cuDoubleComplex alpha, const cuDoubleComplex		cublasStatus_t error = cublasZtrmm_v2(handle, convertToSideMode(side),c
	*x, int incx,		onvertToFillMode(uplo),
	* const cuDoubleComplex *y, int incy, cuDoubleComplex *A, int		convertToOp(transa), convertToDiagType(d
	lda)		iag), m, n, &alpha, A, lda, B, ldb, B, ldb );
	*		cublasSetError(error);
	* performs the symmetric rank 1 operation
	*
	* A = alpha * x * transpose(y) + A,
	*
	* where alpha is a double precision complex scalar, x is an m element doub
	le
	* precision complex vector, y is an n element double precision complex vec
	tor, and A
	* is an m by n matrix consisting of double precision complex elements. Mat
	rix A
	* is stored in column major format, and lda is the leading dimension of
	* the two-dimensional array used to store A.
	*
	* Input
	* -----
	* m specifies the number of rows of the matrix A. It must be at least
	* zero.
	* n specifies the number of columns of the matrix A. It must be at
	* least zero.
	* alpha double precision complex scalar multiplier applied to x * transpo
	se(y)
	* x double precision complex array of length at least (1 + (m - 1) *
	abs(incx))
	* incx specifies the storage spacing between elements of x. incx must no
	t
	* be zero.
	* y double precision complex array of length at least (1 + (n - 1) *
	abs(incy))
	* incy specifies the storage spacing between elements of y. incy must no
	t
	* be zero.
	* A double precision complex array of dimensions (lda, n).
	* lda leading dimension of two-dimensional array used to store matrix A
	*
	* Output
	* ------
	* A updated according to A = alpha * x * transpose(y) + A
	*
	* Reference: http://www.netlib.org/blas/zgeru.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if m < 0, n < 0, incx == 0, incy == 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZgeru (int m, int n, cuDoubleComplex alpha,
	const cuDoubleComplex *x, int incx, const cuDou
	bleComplex *y,
	int incy, cuDoubleComplex *A, int lda);
	/*
	* cublasZgerc (int m, int n, cuDoubleComplex alpha, const cuDoubleComplex
	*x, int incx,
	* const cuDoubleComplex *y, int incy, cuDoubleComplex *A, int
	lda)
	*
	* performs the symmetric rank 1 operation
	*
	* A = alpha * x * conjugate(transpose(y)) + A,
	*
	* where alpha is a double precision complex scalar, x is an m element doub
	le
	* precision complex vector, y is an n element double precision complex vec
	tor, and A
	* is an m by n matrix consisting of double precision complex elements. Mat
	rix A
	* is stored in column major format, and lda is the leading dimension of
	* the two-dimensional array used to store A.
	*
	* Input
	* -----
	* m specifies the number of rows of the matrix A. It must be at least
	* zero.
	* n specifies the number of columns of the matrix A. It must be at
	* least zero.
	* alpha double precision complex scalar multiplier applied to x * conjuga
	te(transpose(y))
	* x double precision array of length at least (1 + (m - 1) * abs(incx
	))
	* incx specifies the storage spacing between elements of x. incx must no
	t
	* be zero.
	* y double precision complex array of length at least (1 + (n - 1) *
	abs(incy))
	* incy specifies the storage spacing between elements of y. incy must no
	t
	* be zero.
	* A double precision complex array of dimensions (lda, n).
	* lda leading dimension of two-dimensional array used to store matrix A
	*
	* Output
	* ------
	* A updated according to A = alpha * x * conjugate(transpose(y)) + A
	*
	* Reference: http://www.netlib.org/blas/zgerc.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if m < 0, n < 0, incx == 0, incy == 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZgerc (int m, int n, cuDoubleComplex alpha,
	const cuDoubleComplex *x, int incx, const cuDou
	bleComplex *y,
	int incy, cuDoubleComplex *A, int lda);
	/*
	* void
	* cublasZherk (char uplo, char trans, int n, int k, double alpha,
	* const cuDoubleComplex *A, int lda, double beta, cuDoubleCom
	plex *C, int ldc)
	*
	* performs one of the hermitian rank k operations
	*
	* C = alpha * A * conjugate(transpose(A)) + beta * C, or
	* C = alpha * conjugate(transpose(A)) * A + beta * C.
	*
	* Alpha and beta are double precision scalars. C is an n x n hermitian mat
	rix
	* consisting of double precision complex elements and stored in either low
	er or
	* upper storage mode. A is a matrix consisting of double precision complex
	elements
	* with dimension of n x k in the first case, and k x n in the second case.
	*
	* Input
	* -----
	* uplo specifies whether the hermitian matrix C is stored in upper or lo
	wer
	* storage mode as follows. If uplo == 'U' or 'u', only the upper
	* triangular part of the hermitian matrix is to be referenced, and
	the
	* elements of the strictly lower triangular part are to be infered
	from
	* those in the upper triangular part. If uplo == 'L' or 'l', only t
	he
	* lower triangular part of the hermitian matrix is to be referenced
	,
	* and the elements of the strictly upper triangular part are to be
	* infered from those in the lower triangular part.
	* trans specifies the operation to be performed. If trans == 'N' or 'n',
	C =
	* alpha * A * conjugate(transpose(A)) + beta * C. If trans == 'T',
	't', 'C', or 'c',
	* C = alpha * conjugate(transpose(A)) * A + beta * C.
	* n specifies the number of rows and the number columns of matrix C.
	If
	* trans == 'N' or 'n', n specifies the number of rows of matrix A.
	If
	* trans == 'T', 't', 'C', or 'c', n specifies the columns of matrix
	A.
	* n must be at least zero.
	* k If trans == 'N' or 'n', k specifies the number of columns of matr
	ix A.
	* If trans == 'T', 't', 'C', or 'c', k specifies the number of rows
	of
	* matrix A. k must be at least zero.
	* alpha double precision scalar multiplier applied to A * conjugate(trans
	pose(A)) or
	* conjugate(transpose(A)) * A.
	* A double precision complex array of dimensions (lda, ka), where ka
	is k when
	* trans == 'N' or 'n', and is n otherwise. When trans == 'N' or 'n'
	,
	* the leading n x k part of array A must contain the matrix A,
	* otherwise the leading k x n part of the array must contains the
	* matrix A.
	* lda leading dimension of A. When trans == 'N' or 'n' then lda must be
	at
	* least max(1, n). Otherwise lda must be at least max(1, k).
	* beta double precision scalar multiplier applied to C. If beta is zero,
	C
	* does not have to be a valid input
	* C double precision complex array of dimensions (ldc, n). If uplo =
	'U' or 'u',
	* the leading n x n triangular part of the array C must contain the
	* upper triangular part of the hermitian matrix C and the strictly
	* lower triangular part of C is not referenced. On exit, the upper
	* triangular part of C is overwritten by the upper triangular part
	of
	* the updated matrix. If uplo = 'L' or 'l', the leading n x n
	* triangular part of the array C must contain the lower triangular
	part
	* of the hermitian matrix C and the strictly upper triangular part
	of C
	* is not referenced. On exit, the lower triangular part of C is
	* overwritten by the lower triangular part of the updated matrix.
	* The imaginary parts of the diagonal elements need
	* not be set, they are assumed to be zero, and on exit they
	* are set to zero.
	* ldc leading dimension of C. It must be at least max(1, n).
	*
	* Output
	* ------
	* C updated according to C = alpha * A * conjugate(transpose(A)) + be
	ta * C, or C =
	* alpha * conjugate(transpose(A)) * A + beta * C
	*
	* Reference: http://www.netlib.org/blas/zherk.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if n < 0 or k < 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZherk (char uplo, char trans, int n, int k,
	double alpha,
	const cuDoubleComplex *A, int lda,
	double beta,
	cuDoubleComplex *C, int ldc);
	/*
	* void
	* cublasZhemm (char side, char uplo, int m, int n, cuDoubleComplex alpha,
	* const cuDoubleComplex *A, int lda, const cuDoubleComplex *B
	, int ldb,
	* cuDoubleComplex beta, cuDoubleComplex *C, int ldc);
	*
	* performs one of the matrix-matrix operations
	*
	* C = alpha * A * B + beta * C, or
	* C = alpha * B * A + beta * C,
	*
	* where alpha and beta are double precision complex scalars, A is a hermit
	ian matrix
	* consisting of double precision complex elements and stored in either low
	er or upper
	* storage mode, and B and C are m x n matrices consisting of double precis
	ion
	* complex elements.
	*
	* Input
	* -----
	* side specifies whether the hermitian matrix A appears on the left side
	* hand side or right hand side of matrix B, as follows. If side ==
	'L'
	* or 'l', then C = alpha * A * B + beta * C. If side = 'R' or 'r',
	* then C = alpha * B * A + beta * C.
	* uplo specifies whether the hermitian matrix A is stored in upper or lo
	wer
	* storage mode, as follows. If uplo == 'U' or 'u', only the upper
	* triangular part of the hermitian matrix is to be referenced, and
	the
	* elements of the strictly lower triangular part are to be infered
	from
	* those in the upper triangular part. If uplo == 'L' or 'l', only t
	he
	* lower triangular part of the hermitian matrix is to be referenced
	,
	* and the elements of the strictly upper triangular part are to be
	* infered from those in the lower triangular part.
	* m specifies the number of rows of the matrix C, and the number of r
	ows
	* of matrix B. It also specifies the dimensions of hermitian matrix
	A
	* when side == 'L' or 'l'. m must be at least zero.
	* n specifies the number of columns of the matrix C, and the number o
	f
	* columns of matrix B. It also specifies the dimensions of hermitia
	n
	* matrix A when side == 'R' or 'r'. n must be at least zero.
	* alpha double precision scalar multiplier applied to A * B, or B * A
	* A double precision complex array of dimensions (lda, ka), where ka
	is m when
	* side == 'L' or 'l' and is n otherwise. If side == 'L' or 'l' the
	* leading m x m part of array A must contain the hermitian matrix,
	* such that when uplo == 'U' or 'u', the leading m x m part stores
	the
	* upper triangular part of the hermitian matrix, and the strictly l
	ower
	* triangular part of A is not referenced, and when uplo == 'U' or '
	u',
	* the leading m x m part stores the lower triangular part of the
	* hermitian matrix and the strictly upper triangular part is not
	* referenced. If side == 'R' or 'r' the leading n x n part of array
	A
	* must contain the hermitian matrix, such that when uplo == 'U' or
	'u',
	* the leading n x n part stores the upper triangular part of the
	* hermitian matrix and the strictly lower triangular part of A is n
	ot
	* referenced, and when uplo == 'U' or 'u', the leading n x n part
	* stores the lower triangular part of the hermitian matrix and the
	* strictly upper triangular part is not referenced. The imaginary p
	arts
	* of the diagonal elements need not be set, they are assumed to be
	zero.
	*
	* lda leading dimension of A. When side == 'L' or 'l', it must be at le
	ast
	* max(1, m) and at least max(1, n) otherwise.
	* B double precision complex array of dimensions (ldb, n). On entry,
	the leading
	* m x n part of the array contains the matrix B.
	* ldb leading dimension of B. It must be at least max (1, m).
	* beta double precision complex scalar multiplier applied to C. If beta
	is zero, C
	* does not have to be a valid input
	* C double precision complex array of dimensions (ldc, n)
	* ldc leading dimension of C. Must be at least max(1, m)
	*
	* Output
	* ------
	* C updated according to C = alpha * A * B + beta * C, or C = alpha *
	* B * A + beta * C
	*
	* Reference: http://www.netlib.org/blas/zhemm.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if m or n are < 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZhemm (char side, char uplo, int m, int n,
	cuDoubleComplex alpha, const cuDoubleComplex *A
	, int lda,
	const cuDoubleComplex *B, int ldb, cuDoubleComp
	lex beta,
	cuDoubleComplex *C, int ldc);
	/*		}
	* void
	* cublasZtrsv (char uplo, char trans, char diag, int n, const cuDoubleComp
	lex *A,
	* int lda, cuDoubleComplex *x, int incx)
	*
	* solves a system of equations op(A) * x = b, where op(A) is either A,
	* transpose(A) or conjugate(transpose(A)). b and x are double precision
	* complex vectors consisting of n elements, and A is an n x n matrix
	* composed of a unit or non-unit, upper or lower triangular matrix.
	* Matrix A is stored in column major format, and lda is the leading
	* dimension of the two-dimensional array containing A.
	*
	* No test for singularity or near-singularity is included in this function
	.
	* Such tests must be performed before calling this function.
	*
	* Input
	* -----
	* uplo specifies whether the matrix data is stored in the upper or the
	* lower triangular part of array A. If uplo = 'U' or 'u', then only
	* the upper triangular part of A may be referenced. If uplo = 'L' o
	r
	* 'l', then only the lower triangular part of A may be referenced.
	* trans specifies op(A). If transa = 'n' or 'N', op(A) = A. If transa = '
	t',
	* 'T', 'c', or 'C', op(A) = transpose(A)
	* diag specifies whether or not A is a unit triangular matrix like so:
	* if diag = 'U' or 'u', A is assumed to be unit triangular. If
	* diag = 'N' or 'n', then A is not assumed to be unit triangular.
	* n specifies the number of rows and columns of the matrix A. It
	* must be at least 0.
	* A is a double precision complex array of dimensions (lda, n). If up
	lo = 'U'
	* or 'u', then A must contains the upper triangular part of a symme
	tric
	* matrix, and the strictly lower triangular parts is not referenced
	.
	* If uplo = 'L' or 'l', then A contains the lower triangular part o
	f
	* a symmetric matrix, and the strictly upper triangular part is not
	* referenced.
	* lda is the leading dimension of the two-dimensional array containing
	A.
	* lda must be at least max(1, n).
	* x double precision complex array of length at least (1 + (n - 1) *
	abs(incx)).
	* On entry, x contains the n element right-hand side vector b. On e
	xit,
	* it is overwritten with the solution vector x.
	* incx specifies the storage spacing between elements of x. incx must no
	t
	* be zero.
	*
	* Output
	* ------
	* x updated to contain the solution vector x that solves op(A) * x =
	b.
	*
	* Reference: http://www.netlib.org/blas/ztrsv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if incx == 0 or if n < 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZtrsv (char uplo, char trans, char diag, int n,
	const cuDoubleComplex *A, int lda, cuDoubleComp
	lex *x,
	int incx);
	/*
	* void
	* cublasZhbmv (char uplo, int n, int k, cuDoubleComplex alpha, const cuDou
	bleComplex *A, int lda,
	* const cuDoubleComplex *x, int incx, cuDoubleComplex beta, c
	uDoubleComplex *y, int incy)
	*
	* performs the matrix-vector operation
	*
	* y := alpha*A*x + beta*y
	*
	* alpha and beta are double precision complex scalars. x and y are double
	precision
	* complex vectors with n elements. A is an n by n hermitian band matrix co
	nsisting
	* of double precision complex elements, with k super-diagonals and the sam
	e number
	* of subdiagonals.
	*
	* Input
	* -----
	* uplo specifies whether the upper or lower triangular part of the hermi
	tian
	* band matrix A is being supplied. If uplo == 'U' or 'u', the upper
	* triangular part is being supplied. If uplo == 'L' or 'l', the low
	er
	* triangular part is being supplied.
	* n specifies the number of rows and the number of columns of the
	* hermitian matrix A. n must be at least zero.
	* k specifies the number of super-diagonals of matrix A. Since the ma
	trix
	* is hermitian, this is also the number of sub-diagonals. k must be
	at
	* least zero.
	* alpha double precision complex scalar multiplier applied to A*x.
	* A double precision complex array of dimensions (lda, n). When uplo
	== 'U' or
	* 'u', the leading (k + 1) x n part of array A must contain the upp
	er
	* triangular band of the hermitian matrix, supplied column by colum
	n,
	* with the leading diagonal of the matrix in row (k+1) of the array
	,
	* the first super-diagonal starting at position 2 in row k, and so
	on.
	* The top left k x k triangle of the array A is not referenced. Whe
	n
	* uplo == 'L' or 'l', the leading (k + 1) x n part of the array A m
	ust
	* contain the lower triangular band part of the hermitian matrix,
	* supplied column by column, with the leading diagonal of the matri
	x in
	* row 1 of the array, the first sub-diagonal starting at position 1
	in
	* row 2, and so on. The bottom right k x k triangle of the array A
	is
	* not referenced. The imaginary parts of the diagonal elements need
	* not be set, they are assumed to be zero.
	* lda leading dimension of A. lda must be at least (k + 1).
	* x double precision complex array of length at least (1 + (n - 1) *
	abs(incx)).
	* incx storage spacing between elements of x. incx must not be zero.
	* beta double precision complex scalar multiplier applied to vector y. I
	f beta is
	* zero, y is not read.
	* y double precision complex array of length at least (1 + (n - 1) *
	abs(incy)).
	* If beta is zero, y is not read.
	* incy storage spacing between elements of y. incy must not be zero.
	*
	* Output
	* ------
	* y updated according to alpha*A*x + beta*y
	*
	* Reference: http://www.netlib.org/blas/zhbmv.f
	*
	* Error status for this function can be retrieved via cublasGetError().
	*
	* Error Status
	* ------------
	* CUBLAS_STATUS_NOT_INITIALIZED if CUBLAS library has not been initialize
	d
	* CUBLAS_STATUS_INVALID_VALUE if k or n < 0, or if incx or incy == 0
	* CUBLAS_STATUS_ARCH_MISMATCH if invoked on device without DP support
	* CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	*/
	void CUBLASAPI cublasZhbmv (char uplo, int n, int k, cuDoubleComplex alpha,
	const cuDoubleComplex *A, int lda, const cuDoub
	leComplex *x,
	int incx, cuDoubleComplex beta, cuDoubleComplex
	*y, int incy);
	#if defined(__cplusplus)		#if defined(__cplusplus)
	}		}
	#endif /* __cplusplus */		#endif /* __cplusplus */
	#endif /* !defined(CUBLAS_H_) */		#endif /* !defined(CUBLAS_H_) */
End of changes. 95 change blocks.
	10240 lines changed or deleted		1844 lines changed or added

	cuda.h		cuda.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#ifndef __cuda_cuda_h__		#ifndef __cuda_cuda_h__
	#define __cuda_cuda_h__		#define __cuda_cuda_h__
	#include <stdlib.h>		#include <stdlib.h>
	/**		/**
	* CUDA API versioning support		* CUDA API versioning support
	*/		*/
	#if defined(CUDA_FORCE_API_VERSION)		#if defined(CUDA_FORCE_API_VERSION)
	#if (CUDA_FORCE_API_VERSION == 3010)		#if (CUDA_FORCE_API_VERSION == 3010)
	#define __CUDA_API_VERSION 3010		#define __CUDA_API_VERSION 3010
	#else		#else
	#error "Unsupported value of CUDA_FORCE_API_VERSION"		#error "Unsupported value of CUDA_FORCE_API_VERSION"
	#endif		#endif
	#else		#else
	#define __CUDA_API_VERSION 3020		#define __CUDA_API_VERSION 4000
	#endif /* CUDA_FORCE_API_VERSION */		#endif /* CUDA_FORCE_API_VERSION */
	#if defined(__CUDA_API_VERSION_INTERNAL) || __CUDA_API_VERSION >= 3020		#if defined(__CUDA_API_VERSION_INTERNAL) || __CUDA_API_VERSION >= 3020
	#define cuDeviceTotalMem cuDeviceTotalMem_v2		#define cuDeviceTotalMem cuDeviceTotalMem_v2
	#define cuCtxCreate cuCtxCreate_v2		#define cuCtxCreate cuCtxCreate_v2
	#define cuModuleGetGlobal cuModuleGetGlobal_v2		#define cuModuleGetGlobal cuModuleGetGlobal_v2
	#define cuMemGetInfo cuMemGetInfo_v2		#define cuMemGetInfo cuMemGetInfo_v2
	#define cuMemAlloc cuMemAlloc_v2		#define cuMemAlloc cuMemAlloc_v2
	#define cuMemAllocPitch cuMemAllocPitch_v2		#define cuMemAllocPitch cuMemAllocPitch_v2
	#define cuMemFree cuMemFree_v2		#define cuMemFree cuMemFree_v2
	skipping to change at line 98		skipping to change at line 112
	#define cuMemsetD2D32 cuMemsetD2D32_v2		#define cuMemsetD2D32 cuMemsetD2D32_v2
	#define cuArrayCreate cuArrayCreate_v2		#define cuArrayCreate cuArrayCreate_v2
	#define cuArrayGetDescriptor cuArrayGetDescriptor_v2		#define cuArrayGetDescriptor cuArrayGetDescriptor_v2
	#define cuArray3DCreate cuArray3DCreate_v2		#define cuArray3DCreate cuArray3DCreate_v2
	#define cuArray3DGetDescriptor cuArray3DGetDescriptor_v2		#define cuArray3DGetDescriptor cuArray3DGetDescriptor_v2
	#define cuTexRefSetAddress cuTexRefSetAddress_v2		#define cuTexRefSetAddress cuTexRefSetAddress_v2
	#define cuTexRefSetAddress2D cuTexRefSetAddress2D_v2		#define cuTexRefSetAddress2D cuTexRefSetAddress2D_v2
	#define cuTexRefGetAddress cuTexRefGetAddress_v2		#define cuTexRefGetAddress cuTexRefGetAddress_v2
	#define cuGraphicsResourceGetMappedPointer cuGraphicsResourceGetMapped Pointer_v2		#define cuGraphicsResourceGetMappedPointer cuGraphicsResourceGetMapped Pointer_v2
	#endif /* __CUDA_API_VERSION_INTERNAL || __CUDA_API_VERSION >= 3020 */		#endif /* __CUDA_API_VERSION_INTERNAL || __CUDA_API_VERSION >= 3020 */
			#if defined(__CUDA_API_VERSION_INTERNAL) || __CUDA_API_VERSION >= 4000
			#define cuCtxDestroy cuCtxDestroy_v2
			#define cuCtxPopCurrent cuCtxPopCurrent_v2
			#define cuCtxPushCurrent cuCtxPushCurrent_v2
			#define cuStreamDestroy cuStreamDestroy_v2
			#define cuEventDestroy cuEventDestroy_v2
			#endif /* __CUDA_API_VERSION_INTERNAL || __CUDA_API_VERSION >= 4000 */
	/**		/**
	* \defgroup CUDA_DRIVER CUDA Driver API		* \defgroup CUDA_DRIVER CUDA Driver API
	*		*
	* This section describes the low-level CUDA driver application programming		* This section describes the low-level CUDA driver application programming
	* interface.		* interface.
	*		*
	* @{		* @{
	*/		*/
	/**		/**
	* \defgroup CUDA_TYPES Data types used by CUDA driver		* \defgroup CUDA_TYPES Data types used by CUDA driver
	* @{		* @{
	*/		*/
	/**		/**
	* CUDA API version number		* CUDA API version number
	*/		*/
	#define CUDA_VERSION 3020 /* 3.2 */		#define CUDA_VERSION 4000 /* 4.0 */
	#ifdef __cplusplus		#ifdef __cplusplus
	extern "C" {		extern "C" {
	#endif		#endif
	/**		/**
	* CUDA device pointer		* CUDA device pointer
	*/		*/
	#if __CUDA_API_VERSION >= 3020		#if __CUDA_API_VERSION >= 3020
	skipping to change at line 154		skipping to change at line 175
	typedef struct CUgraphicsResource_st *CUgraphicsResource; /**< CUDA graphic s interop resource */		typedef struct CUgraphicsResource_st *CUgraphicsResource; /**< CUDA graphic s interop resource */
	typedef struct CUuuid_st { /**< CUDA definit ion of UUID */		typedef struct CUuuid_st { /**< CUDA definit ion of UUID */
	char bytes[16];		char bytes[16];
	} CUuuid;		} CUuuid;
	/**		/**
	* Context creation flags		* Context creation flags
	*/		*/
	typedef enum CUctx_flags_enum {		typedef enum CUctx_flags_enum {
	CU_CTX_SCHED_AUTO = 0, /**< Automatic scheduling */		CU_CTX_SCHED_AUTO = 0x00, /**< Automatic scheduling */
	CU_CTX_SCHED_SPIN = 1, /**< Set spin as default scheduling */		CU_CTX_SCHED_SPIN = 0x01, /**< Set spin as default scheduling
	CU_CTX_SCHED_YIELD = 2, /**< Set yield as default scheduling */		*/
	CU_CTX_SCHED_MASK = 0x3,		CU_CTX_SCHED_YIELD = 0x02, /**< Set yield as default scheduling
	CU_CTX_BLOCKING_SYNC = 4, /**< Use blocking synchronization */		*/
	CU_CTX_MAP_HOST = 8, /**< Support mapped pinned allocations		CU_CTX_SCHED_BLOCKING_SYNC = 0x04, /**< Set blocking synchronization as
	*/		default scheduling */
	CU_CTX_LMEM_RESIZE_TO_MAX = 16, /**< Keep local memory allocation after		CU_CTX_BLOCKING_SYNC = 0x04, /**< Set blocking synchronization as
	launch */		default scheduling \deprecated */
	CU_CTX_FLAGS_MASK = 0x1f		CU_CTX_SCHED_MASK = 0x07,
			CU_CTX_MAP_HOST = 0x08, /**< Support mapped pinned allocatio
			ns */
			CU_CTX_LMEM_RESIZE_TO_MAX = 0x10, /**< Keep local memory allocation af
			ter launch */
			CU_CTX_FLAGS_MASK = 0x1f
	} CUctx_flags;		} CUctx_flags;
	/**		/**
	* Event creation flags		* Event creation flags
	*/		*/
	typedef enum CUevent_flags_enum {		typedef enum CUevent_flags_enum {
	CU_EVENT_DEFAULT = 0, /**< Default event flag */		CU_EVENT_DEFAULT = 0, /**< Default event flag */
	CU_EVENT_BLOCKING_SYNC = 1, /**< Event uses blocking synchronization * /		CU_EVENT_BLOCKING_SYNC = 1, /**< Event uses blocking synchronization * /
	CU_EVENT_DISABLE_TIMING = 2 /**< Event will not record timing data */		CU_EVENT_DISABLE_TIMING = 2 /**< Event will not record timing data */
	} CUevent_flags;		} CUevent_flags;
	skipping to change at line 225		skipping to change at line 247
	CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z = 7, /**< Maximu m grid dimension Z */		CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z = 7, /**< Maximu m grid dimension Z */
	CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK = 8, /**< Maximu m shared memory available per block in bytes */		CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK = 8, /**< Maximu m shared memory available per block in bytes */
	CU_DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK = 8, /**< Deprec ated, use CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK */		CU_DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK = 8, /**< Deprec ated, use CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK */
	CU_DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY = 9, /**< Memory available on device for __constant__ variables in a CUDA C kernel in bytes */		CU_DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY = 9, /**< Memory available on device for __constant__ variables in a CUDA C kernel in bytes */
	CU_DEVICE_ATTRIBUTE_WARP_SIZE = 10, /**< Warp s ize in threads */		CU_DEVICE_ATTRIBUTE_WARP_SIZE = 10, /**< Warp s ize in threads */
	CU_DEVICE_ATTRIBUTE_MAX_PITCH = 11, /**< Maximu m pitch in bytes allowed by memory copies */		CU_DEVICE_ATTRIBUTE_MAX_PITCH = 11, /**< Maximu m pitch in bytes allowed by memory copies */
	CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK = 12, /**< Maximu m number of 32-bit registers available per block */		CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK = 12, /**< Maximu m number of 32-bit registers available per block */
	CU_DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK = 12, /**< Deprec ated, use CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK */		CU_DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK = 12, /**< Deprec ated, use CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK */
	CU_DEVICE_ATTRIBUTE_CLOCK_RATE = 13, /**< Peak c lock frequency in kilohertz */		CU_DEVICE_ATTRIBUTE_CLOCK_RATE = 13, /**< Peak c lock frequency in kilohertz */
	CU_DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT = 14, /**< Alignm ent requirement for textures */		CU_DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT = 14, /**< Alignm ent requirement for textures */
	CU_DEVICE_ATTRIBUTE_GPU_OVERLAP = 15, /**< Device can possibly copy memory and execute a kernel concurrently */		CU_DEVICE_ATTRIBUTE_GPU_OVERLAP = 15, /**< Device can possibly copy memory and execute a kernel concurrently. Deprecated. Us e instead CU_DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT. */
	CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT = 16, /**< Number of multiprocessors on device */		CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT = 16, /**< Number of multiprocessors on device */
	CU_DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT = 17, /**< Specif ies whether there is a run time limit on kernels */		CU_DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT = 17, /**< Specif ies whether there is a run time limit on kernels */
	CU_DEVICE_ATTRIBUTE_INTEGRATED = 18, /**< Device is integrated with host memory */		CU_DEVICE_ATTRIBUTE_INTEGRATED = 18, /**< Device is integrated with host memory */
	CU_DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY = 19, /**< Device can map host memory into CUDA address space */		CU_DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY = 19, /**< Device can map host memory into CUDA address space */
	CU_DEVICE_ATTRIBUTE_COMPUTE_MODE = 20, /**< Comput e mode (See ::CUcomputemode for details) */		CU_DEVICE_ATTRIBUTE_COMPUTE_MODE = 20, /**< Comput e mode (See ::CUcomputemode for details) */
	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH = 21, /**< Maximu m 1D texture width */		CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH = 21, /**< Maximu m 1D texture width */
	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH = 22, /**< Maximu m 2D texture width */		CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH = 22, /**< Maximu m 2D texture width */
	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT = 23, /**< Maximu m 2D texture height */		CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT = 23, /**< Maximu m 2D texture height */
	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH = 24, /**< Maximu m 3D texture width */		CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH = 24, /**< Maximu m 3D texture width */
	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT = 25, /**< Maximu m 3D texture height */		CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT = 25, /**< Maximu m 3D texture height */
	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH = 26, /**< Maximu m 3D texture depth */		CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH = 26, /**< Maximu m 3D texture depth */
	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH = 27, /**< Maximu		CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH = 27, /**< Maximu
	m texture array width */		m 2D layered texture width */
	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT = 28, /**< Maximu		CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT = 28, /**< Maximu
	m texture array height */		m 2D layered texture height */
	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES = 29, /**< Maximu		CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS = 29, /**< Maximu
	m slices in a texture array */		m layers in a 2D layered texture */
			CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH = 27, /**< Deprec
			ated, use CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH */
			CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT = 28, /**< Deprec
			ated, use CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT */
			CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES = 29, /**< Deprec
			ated, use CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS */
	CU_DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT = 30, /**< Alignm ent requirement for surfaces */		CU_DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT = 30, /**< Alignm ent requirement for surfaces */
	CU_DEVICE_ATTRIBUTE_CONCURRENT_KERNELS = 31, /**< Device can possibly execute multiple kernels concurrently */		CU_DEVICE_ATTRIBUTE_CONCURRENT_KERNELS = 31, /**< Device can possibly execute multiple kernels concurrently */
	CU_DEVICE_ATTRIBUTE_ECC_ENABLED = 32, /**< Device has ECC support enabled */		CU_DEVICE_ATTRIBUTE_ECC_ENABLED = 32, /**< Device has ECC support enabled */
	CU_DEVICE_ATTRIBUTE_PCI_BUS_ID = 33, /**< PCI bu s ID of the device */		CU_DEVICE_ATTRIBUTE_PCI_BUS_ID = 33, /**< PCI bu s ID of the device */
	CU_DEVICE_ATTRIBUTE_PCI_DEVICE_ID = 34, /**< PCI de vice ID of the device */		CU_DEVICE_ATTRIBUTE_PCI_DEVICE_ID = 34, /**< PCI de vice ID of the device */
	CU_DEVICE_ATTRIBUTE_TCC_DRIVER = 35 /**< Device		CU_DEVICE_ATTRIBUTE_TCC_DRIVER = 35, /**< Device
	is using TCC driver model */		is using TCC driver model */
			CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE = 36, /**< Peak m
			emory clock frequency in kilohertz */
			CU_DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH = 37, /**< Global
			memory bus width in bits */
			CU_DEVICE_ATTRIBUTE_L2_CACHE_SIZE = 38, /**< Size o
			f L2 cache in bytes */
			CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR = 39, /**< Maximu
			m resident threads per multiprocessor */
			CU_DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT = 40, /**< Number
			of asynchronous engines */
			CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING = 41, /**< Device
			shares a unified address space with the host */
			CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH = 42, /**< Maximu
			m 1D layered texture width */
			CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS = 43, /**< Maximu
			m layers in a 1D layered texture */
			CU_DEVICE_ATTRIBUTE_PCI_DOMAIN_ID = 50 /**< PCI do
			main ID of the device */
	} CUdevice_attribute;		} CUdevice_attribute;
	/**		/**
	* Legacy device properties		* Legacy device properties
	*/		*/
	typedef struct CUdevprop_st {		typedef struct CUdevprop_st {
	int maxThreadsPerBlock; /**< Maximum number of threads per block */		int maxThreadsPerBlock; /**< Maximum number of threads per block */
	int maxThreadsDim[3]; /**< Maximum size of each dimension of a bl ock */		int maxThreadsDim[3]; /**< Maximum size of each dimension of a bl ock */
	int maxGridSize[3]; /**< Maximum size of each dimension of a gr id */		int maxGridSize[3]; /**< Maximum size of each dimension of a gr id */
	int sharedMemPerBlock; /**< Shared memory available per block in b ytes */		int sharedMemPerBlock; /**< Shared memory available per block in b ytes */
	int totalConstantMemory; /**< Constant memory available on device in bytes */		int totalConstantMemory; /**< Constant memory available on device in bytes */
	int SIMDWidth; /**< Warp size in threads */		int SIMDWidth; /**< Warp size in threads */
	int memPitch; /**< Maximum pitch in bytes allowed by memo ry copies */		int memPitch; /**< Maximum pitch in bytes allowed by memo ry copies */
	int regsPerBlock; /**< 32-bit registers available per block * /		int regsPerBlock; /**< 32-bit registers available per block * /
	int clockRate; /**< Clock frequency in kilohertz */		int clockRate; /**< Clock frequency in kilohertz */
	int textureAlign; /**< Alignment requirement for textures */		int textureAlign; /**< Alignment requirement for textures */
	} CUdevprop;		} CUdevprop;
	/**		/**
			* Pointer information
			*/
			typedef enum CUpointer_attribute_enum {
			CU_POINTER_ATTRIBUTE_CONTEXT = 1, /**< The ::CUcontext on which
			a pointer was allocated or registered */
			CU_POINTER_ATTRIBUTE_MEMORY_TYPE = 2, /**< The ::CUmemorytype descri
			bing the physical location of a pointer */
			CU_POINTER_ATTRIBUTE_DEVICE_POINTER = 3, /**< The address at which a po
			inter's memory may be accessed on the device */
			CU_POINTER_ATTRIBUTE_HOST_POINTER = 4, /**< The address at which a po
			inter's memory may be accessed on the host */
			} CUpointer_attribute;
			/**
	* Function properties		* Function properties
	*/		*/
	typedef enum CUfunction_attribute_enum {		typedef enum CUfunction_attribute_enum {
	/**		/**
	* The maximum number of threads per block, beyond which a launch of th e		* The maximum number of threads per block, beyond which a launch of th e
	* function would fail. This number depends on both the function and th e		* function would fail. This number depends on both the function and th e
	* device on which the function is currently loaded.		* device on which the function is currently loaded.
	*/		*/
	CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK = 0,		CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK = 0,
	skipping to change at line 332		skipping to change at line 376
	typedef enum CUfunc_cache_enum {		typedef enum CUfunc_cache_enum {
	CU_FUNC_CACHE_PREFER_NONE = 0x00, /**< no preference for shared memo ry or L1 (default) */		CU_FUNC_CACHE_PREFER_NONE = 0x00, /**< no preference for shared memo ry or L1 (default) */
	CU_FUNC_CACHE_PREFER_SHARED = 0x01, /**< prefer larger shared memory a nd smaller L1 cache */		CU_FUNC_CACHE_PREFER_SHARED = 0x01, /**< prefer larger shared memory a nd smaller L1 cache */
	CU_FUNC_CACHE_PREFER_L1 = 0x02 /**< prefer larger L1 cache and sm aller shared memory */		CU_FUNC_CACHE_PREFER_L1 = 0x02 /**< prefer larger L1 cache and sm aller shared memory */
	} CUfunc_cache;		} CUfunc_cache;
	/**		/**
	* Memory types		* Memory types
	*/		*/
	typedef enum CUmemorytype_enum {		typedef enum CUmemorytype_enum {
	CU_MEMORYTYPE_HOST = 0x01, /**< Host memory */		CU_MEMORYTYPE_HOST = 0x01, /**< Host memory */
	CU_MEMORYTYPE_DEVICE = 0x02, /**< Device memory */		CU_MEMORYTYPE_DEVICE = 0x02, /**< Device memory */
	CU_MEMORYTYPE_ARRAY = 0x03 /**< Array memory */		CU_MEMORYTYPE_ARRAY = 0x03, /**< Array memory */
			CU_MEMORYTYPE_UNIFIED = 0x04 /**< Unified device or host memory */
	} CUmemorytype;		} CUmemorytype;
	/**		/**
	* Compute Modes		* Compute Modes
	*/		*/
	typedef enum CUcomputemode_enum {		typedef enum CUcomputemode_enum {
	CU_COMPUTEMODE_DEFAULT = 0, /**< Default compute mode (Multiple con texts allowed per device) */		CU_COMPUTEMODE_DEFAULT = 0, /**< Default compute mode (Multiple con texts allowed per device) */
	CU_COMPUTEMODE_EXCLUSIVE = 1, /**< Compute-exclusive mode (Only one c		CU_COMPUTEMODE_EXCLUSIVE = 1, /**< Compute-exclusive-thread mod
	ontext can be present on this device at a time) */		e (Only one context used by a single thread can be present on this device a
	CU_COMPUTEMODE_PROHIBITED = 2 /**< Compute-prohibited mode (No contex		t a time) */
	ts can be created on this device at this time) */		CU_COMPUTEMODE_PROHIBITED = 2, /**< Compute-prohibited mode (No
			contexts can be created on this device at this time) */
			CU_COMPUTEMODE_EXCLUSIVE_PROCESS = 3 /**< Compute-exclusive-process mo
			de (Only one context used by a single process can be present on this device
			at a time) */
	} CUcomputemode;		} CUcomputemode;
	/**		/**
	* Online compiler options		* Online compiler options
	*/		*/
	typedef enum CUjit_option_enum		typedef enum CUjit_option_enum
	{		{
	/**		/**
	* Max number of registers that a thread may use.\n		* Max number of registers that a thread may use.\n
	* Option type: unsigned int		* Option type: unsigned int
	skipping to change at line 466		skipping to change at line 512
	CU_PREFER_PTX = 0, /**< Prefer to compile ptx */		CU_PREFER_PTX = 0, /**< Prefer to compile ptx */
	CU_PREFER_BINARY /**< Prefer to fall back to compatible binary code */		CU_PREFER_BINARY /**< Prefer to fall back to compatible binary code */
	} CUjit_fallback;		} CUjit_fallback;
	/**		/**
	* Flags to register a graphics resource		* Flags to register a graphics resource
	*/		*/
	typedef enum CUgraphicsRegisterFlags_enum {		typedef enum CUgraphicsRegisterFlags_enum {
	CU_GRAPHICS_REGISTER_FLAGS_NONE = 0x00		CU_GRAPHICS_REGISTER_FLAGS_NONE = 0x00,
			CU_GRAPHICS_REGISTER_FLAGS_READ_ONLY = 0x01,
			CU_GRAPHICS_REGISTER_FLAGS_WRITE_DISCARD = 0x02,
			CU_GRAPHICS_REGISTER_FLAGS_SURFACE_LDST = 0x04
	} CUgraphicsRegisterFlags;		} CUgraphicsRegisterFlags;
	/**		/**
	* Flags for mapping and unmapping interop resources		* Flags for mapping and unmapping interop resources
	*/		*/
	typedef enum CUgraphicsMapResourceFlags_enum {		typedef enum CUgraphicsMapResourceFlags_enum {
	CU_GRAPHICS_MAP_RESOURCE_FLAGS_NONE = 0x00,		CU_GRAPHICS_MAP_RESOURCE_FLAGS_NONE = 0x00,
	CU_GRAPHICS_MAP_RESOURCE_FLAGS_READ_ONLY = 0x01,		CU_GRAPHICS_MAP_RESOURCE_FLAGS_READ_ONLY = 0x01,
	CU_GRAPHICS_MAP_RESOURCE_FLAGS_WRITE_DISCARD = 0x02		CU_GRAPHICS_MAP_RESOURCE_FLAGS_WRITE_DISCARD = 0x02
	} CUgraphicsMapResourceFlags;		} CUgraphicsMapResourceFlags;
	skipping to change at line 534		skipping to change at line 583
	* ::cuInit() or that initialization has failed.		* ::cuInit() or that initialization has failed.
	*/		*/
	CUDA_ERROR_NOT_INITIALIZED = 3,		CUDA_ERROR_NOT_INITIALIZED = 3,
	/**		/**
	* This indicates that the CUDA driver is in the process of shutting do wn.		* This indicates that the CUDA driver is in the process of shutting do wn.
	*/		*/
	CUDA_ERROR_DEINITIALIZED = 4,		CUDA_ERROR_DEINITIALIZED = 4,
	/**		/**
			* This indicates profiling APIs are called while application is runnin
			g
			* in visual profiler mode.
			*/
			CUDA_ERROR_PROFILER_DISABLED = 5,
			/**
			* This indicates profiling has not been initialized for this context.
			* Call cuProfilerInitialize() to resolve this.
			*/
			CUDA_ERROR_PROFILER_NOT_INITIALIZED = 6,
			/**
			* This indicates profiler has already been started and probably
			* cuProfilerStart() is incorrectly called.
			*/
			CUDA_ERROR_PROFILER_ALREADY_STARTED = 7,
			/**
			* This indicates profiler has already been stopped and probably
			* cuProfilerStop() is incorrectly called.
			*/
			CUDA_ERROR_PROFILER_ALREADY_STOPPED = 8,
			/**
	* This indicates that no CUDA-capable devices were detected by the ins talled		* This indicates that no CUDA-capable devices were detected by the ins talled
	* CUDA driver.		* CUDA driver.
	*/		*/
	CUDA_ERROR_NO_DEVICE = 100,		CUDA_ERROR_NO_DEVICE = 100,
	/**		/**
	* This indicates that the device ordinal supplied by the user does not		* This indicates that the device ordinal supplied by the user does not
	* correspond to a valid CUDA device.		* correspond to a valid CUDA device.
	*/		*/
	CUDA_ERROR_INVALID_DEVICE = 101,		CUDA_ERROR_INVALID_DEVICE = 101,
	skipping to change at line 634		skipping to change at line 703
	*/		*/
	CUDA_ERROR_ECC_UNCORRECTABLE = 214,		CUDA_ERROR_ECC_UNCORRECTABLE = 214,
	/**		/**
	* This indicates that the ::CUlimit passed to the API call is not		* This indicates that the ::CUlimit passed to the API call is not
	* supported by the active device.		* supported by the active device.
	*/		*/
	CUDA_ERROR_UNSUPPORTED_LIMIT = 215,		CUDA_ERROR_UNSUPPORTED_LIMIT = 215,
	/**		/**
			* This indicates that the ::CUcontext passed to the API call can
			* only be bound to a single CPU thread at a time but is already
			* bound to a CPU thread.
			*/
			CUDA_ERROR_CONTEXT_ALREADY_IN_USE = 216,
			/**
	* This indicates that the device kernel source is invalid.		* This indicates that the device kernel source is invalid.
	*/		*/
	CUDA_ERROR_INVALID_SOURCE = 300,		CUDA_ERROR_INVALID_SOURCE = 300,
	/**		/**
	* This indicates that the file specified was not found.		* This indicates that the file specified was not found.
	*/		*/
	CUDA_ERROR_FILE_NOT_FOUND = 301,		CUDA_ERROR_FILE_NOT_FOUND = 301,
	/**		/**
	skipping to change at line 717		skipping to change at line 793
	*/		*/
	CUDA_ERROR_LAUNCH_TIMEOUT = 702,		CUDA_ERROR_LAUNCH_TIMEOUT = 702,
	/**		/**
	* This error indicates a kernel launch that uses an incompatible textu ring		* This error indicates a kernel launch that uses an incompatible textu ring
	* mode.		* mode.
	*/		*/
	CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING = 703,		CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING = 703,
	/**		/**
			* This error indicates that a call to ::cuCtxEnablePeerAccess() is
			* trying to re-enable peer access to a context which has already
			* had peer access to it enabled.
			*/
			CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED = 704,
			/**
			* This error indicates that ::cuCtxDisablePeerAccess() is
			* trying to disable peer access which has not been enabled yet
			* via ::cuCtxEnablePeerAccess().
			*/
			CUDA_ERROR_PEER_ACCESS_NOT_ENABLED = 705,
			/**
			* This error indicates that the primary context for the specified devi
			ce
			* has already been initialized.
			*/
			CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE = 708,
			/**
			* This error indicates that the context current to the calling thread
			* has been destroyed using ::cuCtxDestroy, or is a primary context whi
			ch
			* has not yet been initialized.
			*/
			CUDA_ERROR_CONTEXT_IS_DESTROYED = 709,
			/**
	* This indicates that an unknown internal error has occurred.		* This indicates that an unknown internal error has occurred.
	*/		*/
	CUDA_ERROR_UNKNOWN = 999		CUDA_ERROR_UNKNOWN = 999
	} CUresult;		} CUresult;
	/**		/**
	* If set, host memory is portable between CUDA contexts.		* If set, host memory is portable between CUDA contexts.
	* Flag for ::cuMemHostAlloc()		* Flag for ::cuMemHostAlloc()
	*/		*/
	#define CU_MEMHOSTALLOC_PORTABLE 0x01		#define CU_MEMHOSTALLOC_PORTABLE 0x01
	skipping to change at line 743		skipping to change at line 846
	#define CU_MEMHOSTALLOC_DEVICEMAP 0x02		#define CU_MEMHOSTALLOC_DEVICEMAP 0x02
	/**		/**
	* If set, host memory is allocated as write-combined - fast to write,		* If set, host memory is allocated as write-combined - fast to write,
	* faster to DMA, slow to read except via SSE4 streaming load instruction		* faster to DMA, slow to read except via SSE4 streaming load instruction
	* (MOVNTDQA).		* (MOVNTDQA).
	* Flag for ::cuMemHostAlloc()		* Flag for ::cuMemHostAlloc()
	*/		*/
	#define CU_MEMHOSTALLOC_WRITECOMBINED 0x04		#define CU_MEMHOSTALLOC_WRITECOMBINED 0x04
			/**
			* If set, host memory is portable between CUDA contexts.
			* Flag for ::cuMemHostRegister()
			*/
			#define CU_MEMHOSTREGISTER_PORTABLE 0x01
			/**
			* If set, host memory is mapped into CUDA address space and
			* ::cuMemHostGetDevicePointer() may be called on the host pointer.
			* Flag for ::cuMemHostRegister()
			*/
			#define CU_MEMHOSTREGISTER_DEVICEMAP 0x02
	#if __CUDA_API_VERSION >= 3020		#if __CUDA_API_VERSION >= 3020
	/**		/**
	* 2D memory copy parameters		* 2D memory copy parameters
	*/		*/
	typedef struct CUDA_MEMCPY2D_st {		typedef struct CUDA_MEMCPY2D_st {
	size_t srcXInBytes; /**< Source X in bytes */		size_t srcXInBytes; /**< Source X in bytes */
	size_t srcY; /**< Source Y */		size_t srcY; /**< Source Y */
	CUmemorytype srcMemoryType; /**< Source memory type (host, device, arra y) */		CUmemorytype srcMemoryType; /**< Source memory type (host, device, arra y) */
	skipping to change at line 805		skipping to change at line 921
	void *reserved1; /**< Must be NULL */		void *reserved1; /**< Must be NULL */
	size_t dstPitch; /**< Destination pitch (ignored when dst is array) */		size_t dstPitch; /**< Destination pitch (ignored when dst is array) */
	size_t dstHeight; /**< Destination height (ignored when dst i s array; may be 0 if Depth==1) */		size_t dstHeight; /**< Destination height (ignored when dst i s array; may be 0 if Depth==1) */
	size_t WidthInBytes; /**< Width of 3D memory copy in bytes */		size_t WidthInBytes; /**< Width of 3D memory copy in bytes */
	size_t Height; /**< Height of 3D memory copy */		size_t Height; /**< Height of 3D memory copy */
	size_t Depth; /**< Depth of 3D memory copy */		size_t Depth; /**< Depth of 3D memory copy */
	} CUDA_MEMCPY3D;		} CUDA_MEMCPY3D;
	/**		/**
			* 3D memory cross-context copy parameters
			*/
			typedef struct CUDA_MEMCPY3D_PEER_st {
			size_t srcXInBytes; /**< Source X in bytes */
			size_t srcY; /**< Source Y */
			size_t srcZ; /**< Source Z */
			size_t srcLOD; /**< Source LOD */
			CUmemorytype srcMemoryType; /**< Source memory type (host, device, arra
			y) */
			const void *srcHost; /**< Source host pointer */
			CUdeviceptr srcDevice; /**< Source device pointer */
			CUarray srcArray; /**< Source array reference */
			CUcontext srcContext; /**< Source context (ignored with srcMemory
			Type is ::CU_MEMORYTYPE_ARRAY) */
			size_t srcPitch; /**< Source pitch (ignored when src is arra
			y) */
			size_t srcHeight; /**< Source height (ignored when src is arr
			ay; may be 0 if Depth==1) */
			size_t dstXInBytes; /**< Destination X in bytes */
			size_t dstY; /**< Destination Y */
			size_t dstZ; /**< Destination Z */
			size_t dstLOD; /**< Destination LOD */
			CUmemorytype dstMemoryType; /**< Destination memory type (host, device,
			array) */
			void *dstHost; /**< Destination host pointer */
			CUdeviceptr dstDevice; /**< Destination device pointer */
			CUarray dstArray; /**< Destination array reference */
			CUcontext dstContext; /**< Destination context (ignored with dstM
			emoryType is ::CU_MEMORYTYPE_ARRAY) */
			size_t dstPitch; /**< Destination pitch (ignored when dst is
			array) */
			size_t dstHeight; /**< Destination height (ignored when dst i
			s array; may be 0 if Depth==1) */
			size_t WidthInBytes; /**< Width of 3D memory copy in bytes */
			size_t Height; /**< Height of 3D memory copy */
			size_t Depth; /**< Depth of 3D memory copy */
			} CUDA_MEMCPY3D_PEER;
			/**
	* Array descriptor		* Array descriptor
	*/		*/
	typedef struct CUDA_ARRAY_DESCRIPTOR_st		typedef struct CUDA_ARRAY_DESCRIPTOR_st
	{		{
	size_t Width; /**< Width of array */		size_t Width; /**< Width of array */
	size_t Height; /**< Height of array */		size_t Height; /**< Height of array */
	CUarray_format Format; /**< Array format */		CUarray_format Format; /**< Array format */
	unsigned int NumChannels; /**< Channels per array element */		unsigned int NumChannels; /**< Channels per array element */
	} CUDA_ARRAY_DESCRIPTOR;		} CUDA_ARRAY_DESCRIPTOR;
	skipping to change at line 833		skipping to change at line 982
	size_t Depth; /**< Depth of 3D array */		size_t Depth; /**< Depth of 3D array */
	CUarray_format Format; /**< Array format */		CUarray_format Format; /**< Array format */
	unsigned int NumChannels; /**< Channels per array element */		unsigned int NumChannels; /**< Channels per array element */
	unsigned int Flags; /**< Flags */		unsigned int Flags; /**< Flags */
	} CUDA_ARRAY3D_DESCRIPTOR;		} CUDA_ARRAY3D_DESCRIPTOR;
	#endif /* __CUDA_API_VERSION >= 3020 */		#endif /* __CUDA_API_VERSION >= 3020 */
	/**		/**
	* If set, the CUDA array contains an array of 2D slices		* If set, the CUDA array is a collection of layers, where each layer is ei
	* and the Depth member of CUDA_ARRAY3D_DESCRIPTOR specifies		ther a 1D
	* the number of slices, not the depth of a 3D array.		* or a 2D array and the Depth member of CUDA_ARRAY3D_DESCRIPTOR specifies
			the number
			* of layers, not the depth of a 3D array.
			*/
			#define CUDA_ARRAY3D_LAYERED 0x01
			/**
			* Deprecated, use CUDA_ARRAY3D_LAYERED
	*/		*/
	#define CUDA_ARRAY3D_2DARRAY 0x01		#define CUDA_ARRAY3D_2DARRAY 0x01
	/**		/**
	* This flag must be set in order to bind a surface reference		* This flag must be set in order to bind a surface reference
	* to the CUDA array		* to the CUDA array
	*/		*/
	#define CUDA_ARRAY3D_SURFACE_LDST 0x02		#define CUDA_ARRAY3D_SURFACE_LDST 0x02
	/**		/**
	skipping to change at line 871		skipping to change at line 1025
	*/		*/
	#define CU_TRSF_NORMALIZED_COORDINATES 0x02		#define CU_TRSF_NORMALIZED_COORDINATES 0x02
	/**		/**
	* Perform sRGB->linear conversion during texture read.		* Perform sRGB->linear conversion during texture read.
	* Flag for ::cuTexRefSetFlags()		* Flag for ::cuTexRefSetFlags()
	*/		*/
	#define CU_TRSF_SRGB 0x10		#define CU_TRSF_SRGB 0x10
	/**		/**
			* End of array terminator for the \p extra parameter to
			* ::cuLaunchKernel
			*/
			#define CU_LAUNCH_PARAM_END ((void*)0x00)
			/**
			* Indicator that the next value in the \p extra parameter to
			* ::cuLaunchKernel will be a pointer to a buffer containing all kernel
			* parameters used for launching kernel \p f. This buffer needs to
			* honor all alignment/padding requirements of the individual parameters.
			* If ::CU_LAUNCH_PARAM_BUFFER_SIZE is not also specified in the
			* \p extra array, then ::CU_LAUNCH_PARAM_BUFFER_POINTER will have no
			* effect.
			*/
			#define CU_LAUNCH_PARAM_BUFFER_POINTER ((void*)0x01)
			/**
			* Indicator that the next value in the \p extra parameter to
			* ::cuLaunchKernel will be a pointer to a size_t which contains the
			* size of the buffer specified with ::CU_LAUNCH_PARAM_BUFFER_POINTER.
			* It is required that ::CU_LAUNCH_PARAM_BUFFER_POINTER also be specified
			* in the \p extra array if the value associated with
			* ::CU_LAUNCH_PARAM_BUFFER_SIZE is not zero.
			*/
			#define CU_LAUNCH_PARAM_BUFFER_SIZE ((void*)0x02)
			/**
	* For texture references loaded into the module, use default texunit from		* For texture references loaded into the module, use default texunit from
	* texture reference.		* texture reference.
	*/		*/
	#define CU_PARAM_TR_DEFAULT -1		#define CU_PARAM_TR_DEFAULT -1
	/** @} */ /* END CUDA_TYPES */		/** @} */ /* END CUDA_TYPES */
	#ifdef _WIN32		#ifdef _WIN32
	#define CUDAAPI __stdcall		#define CUDAAPI __stdcall
	#else		#else
	skipping to change at line 1170		skipping to change at line 1351
	* - ::CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z: Maximum z-dimension of a grid;		* - ::CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z: Maximum z-dimension of a grid;
	* - ::CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK: Maximum amount of		* - ::CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK: Maximum amount of
	* shared memory available to a thread block in bytes; this amount is sha red		* shared memory available to a thread block in bytes; this amount is sha red
	* by all thread blocks simultaneously resident on a multiprocessor;		* by all thread blocks simultaneously resident on a multiprocessor;
	* - ::CU_DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY: Memory available on devic e for		* - ::CU_DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY: Memory available on devic e for
	* __constant__ variables in a CUDA C kernel in bytes;		* __constant__ variables in a CUDA C kernel in bytes;
	* - ::CU_DEVICE_ATTRIBUTE_WARP_SIZE: Warp size in threads;		* - ::CU_DEVICE_ATTRIBUTE_WARP_SIZE: Warp size in threads;
	* - ::CU_DEVICE_ATTRIBUTE_MAX_PITCH: Maximum pitch in bytes allowed by the		* - ::CU_DEVICE_ATTRIBUTE_MAX_PITCH: Maximum pitch in bytes allowed by the
	* memory copy functions that involve memory regions allocated through		* memory copy functions that involve memory regions allocated through
	* ::cuMemAllocPitch();		* ::cuMemAllocPitch();
			* - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH: Maximum 1D
			* texture width;
			* - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH: Maximum 2D
			* texture width;
			* - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT: Maximum 2D
			* texture height;
			* - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH: Maximum 3D
			* texture width;
			* - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT: Maximum 3D
			* texture height;
			* - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH: Maximum 3D
			* texture depth;
			* - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH:
			* Maximum 1D layered texture width;
			* - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS:
			* Maximum layers in a 1D layered texture;
			* - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH:
			* Maximum 2D layered texture width;
			* - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT:
			* Maximum 2D layered texture height;
			* - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS:
			* Maximum layers in a 2D layered texture;
	* - ::CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK: Maximum number of 32-bi t		* - ::CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK: Maximum number of 32-bi t
	* registers available to a thread block; this number is shared by all th read		* registers available to a thread block; this number is shared by all th read
	* blocks simultaneously resident on a multiprocessor;		* blocks simultaneously resident on a multiprocessor;
	* - ::CU_DEVICE_ATTRIBUTE_CLOCK_RATE: Peak clock frequency in kilohertz;		* - ::CU_DEVICE_ATTRIBUTE_CLOCK_RATE: Peak clock frequency in kilohertz;
	* - ::CU_DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT: Alignment requirement; textur e		* - ::CU_DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT: Alignment requirement; textur e
	* base addresses aligned to ::textureAlign bytes do not need an offset		* base addresses aligned to ::textureAlign bytes do not need an offset
	* applied to texture fetches;		* applied to texture fetches;
	* - ::CU_DEVICE_ATTRIBUTE_GPU_OVERLAP: 1 if the device can concurrently co py		* - ::CU_DEVICE_ATTRIBUTE_GPU_OVERLAP: 1 if the device can concurrently co py
	* memory between host and device while executing a kernel, or 0 if not;		* memory between host and device while executing a kernel, or 0 if not;
	* - ::CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT: Number of multiprocessors on		* - ::CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT: Number of multiprocessors on
	skipping to change at line 1195		skipping to change at line 1398
	* - ::CU_DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY: 1 if the device can map hos t		* - ::CU_DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY: 1 if the device can map hos t
	* memory into the CUDA address space, or 0 if not;		* memory into the CUDA address space, or 0 if not;
	* - ::CU_DEVICE_ATTRIBUTE_COMPUTE_MODE: Compute mode that device is curren tly		* - ::CU_DEVICE_ATTRIBUTE_COMPUTE_MODE: Compute mode that device is curren tly
	* in. Available modes are as follows:		* in. Available modes are as follows:
	* - ::CU_COMPUTEMODE_DEFAULT: Default mode - Device is not restricted an d		* - ::CU_COMPUTEMODE_DEFAULT: Default mode - Device is not restricted an d
	* can have multiple CUDA contexts present at a single time.		* can have multiple CUDA contexts present at a single time.
	* - ::CU_COMPUTEMODE_EXCLUSIVE: Compute-exclusive mode - Device can have		* - ::CU_COMPUTEMODE_EXCLUSIVE: Compute-exclusive mode - Device can have
	* only one CUDA context present on it at a time.		* only one CUDA context present on it at a time.
	* - ::CU_COMPUTEMODE_PROHIBITED: Compute-prohibited mode - Device is		* - ::CU_COMPUTEMODE_PROHIBITED: Compute-prohibited mode - Device is
	* prohibited from creating new CUDA contexts.		* prohibited from creating new CUDA contexts.
			* - ::CU_COMPUTEMODE_EXCLUSIVE_PROCESS: Compute-exclusive-process mode
			- Device
			* can have only one context used by a single process at a time.
	* - ::CU_DEVICE_ATTRIBUTE_CONCURRENT_KERNELS: 1 if the device supports		* - ::CU_DEVICE_ATTRIBUTE_CONCURRENT_KERNELS: 1 if the device supports
	* executing multiple kernels within the same context simultaneously, or 0 if		* executing multiple kernels within the same context simultaneously, or 0 if
	* not. It is not guaranteed that multiple kernels will be resident		* not. It is not guaranteed that multiple kernels will be resident
	* on the device concurrently so this feature should not be relied upon f or		* on the device concurrently so this feature should not be relied upon f or
	* correctness;		* correctness;
	* - ::CU_DEVICE_ATTRIBUTE_ECC_ENABLED: 1 if error correction is enabled on the		* - ::CU_DEVICE_ATTRIBUTE_ECC_ENABLED: 1 if error correction is enabled on the
	* device, 0 if error correction is disabled or not supported by the dev		* device, 0 if error correction is disabled or not supported by the dev
	ice.		ice;
	* - ::CU_DEVICE_ATTRIBUTE_PCI_BUS_ID: PCI bus identifier of the device.		* - ::CU_DEVICE_ATTRIBUTE_PCI_BUS_ID: PCI bus identifier of the device;
	* - ::CU_DEVICE_ATTRIBUTE_PCI_DEVICE_ID: PCI device (also known as slot) i dentifier		* - ::CU_DEVICE_ATTRIBUTE_PCI_DEVICE_ID: PCI device (also known as slot) i dentifier
	* of the device.		* of the device;
	* - ::CU_DEVICE_ATTRIBUTE_TCC_DRIVER: 1 if the device is using a TCC drive r. TCC		* - ::CU_DEVICE_ATTRIBUTE_TCC_DRIVER: 1 if the device is using a TCC drive r. TCC
	is only available on Tesla hardware running Windows Vista or later.		* is only available on Tesla hardware running Windows Vista or later;
			* - ::CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE: Peak memory clock frequency i
			n kilohertz;
			* - ::CU_DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH: Global memory bus width
			in bits;
			* - ::CU_DEVICE_ATTRIBUTE_L2_CACHE_SIZE: Size of L2 cache in bytes. 0 if t
			he device doesn't have L2 cache;
			* - ::CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR: Maximum resident
			threads per multiprocessor;
			* - ::CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING: 1 if the device shares a uni
			fied address space with
			* the host, or 0 if not;
			*
	* \param pi - Returned device attribute value		* \param pi - Returned device attribute value
	* \param attrib - Device attribute to query		* \param attrib - Device attribute to query
	* \param dev - Device handle		* \param dev - Device handle
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,		* ::CUDA_ERROR_NOT_INITIALIZED,
	* ::CUDA_ERROR_INVALID_CONTEXT,		* ::CUDA_ERROR_INVALID_CONTEXT,
	* ::CUDA_ERROR_INVALID_VALUE,		* ::CUDA_ERROR_INVALID_VALUE,
	skipping to change at line 1247		skipping to change at line 1459
	* @{		* @{
	*/		*/
	#if __CUDA_API_VERSION >= 3020		#if __CUDA_API_VERSION >= 3020
	/**		/**
	* \brief Create a CUDA context		* \brief Create a CUDA context
	*		*
	* Creates a new CUDA context and associates it with the calling thread. Th e		* Creates a new CUDA context and associates it with the calling thread. Th e
	* \p flags parameter is described below. The context is created with a usa ge		* \p flags parameter is described below. The context is created with a usa ge
	* count of 1 and the caller of ::cuCtxCreate() must call ::cuCtxDestroy() or		* count of 1 and the caller of ::cuCtxCreate() must call ::cuCtxDestroy() or
	* ::cuCtxDetach() when done using the context. If a context is already cur		* when done using the context. If a context is already current to the thre
	rent		ad,
	* to the thread, it is supplanted by the newly created context and may be		* it is supplanted by the newly created context and may be restored by a s
	* restored by a subsequent call to ::cuCtxPopCurrent().		ubsequent
			* call to ::cuCtxPopCurrent().
	*		*
	* The two LSBs of the \p flags parameter can be used to control how the OS		* The three LSBs of the \p flags parameter can be used to control how the OS
	* thread, which owns the CUDA context at the time of an API call, interact s		* thread, which owns the CUDA context at the time of an API call, interact s
	* with the OS scheduler when waiting for results from the GPU.		* with the OS scheduler when waiting for results from the GPU. Only one of
			* the scheduling flags can be set when creating a context.
	*		*
	* - ::CU_CTX_SCHED_AUTO: The default value if the \p flags parameter is ze ro,		* - ::CU_CTX_SCHED_AUTO: The default value if the \p flags parameter is ze ro,
	* uses a heuristic based on the number of active CUDA contexts in the		* uses a heuristic based on the number of active CUDA contexts in the
	* process \e C and the number of logical processors in the system \e P. If		* process \e C and the number of logical processors in the system \e P. If
	* \e C > \e P, then CUDA will yield to other OS threads when waiting for		* \e C > \e P, then CUDA will yield to other OS threads when waiting for
	* the GPU, otherwise CUDA will not yield while waiting for results and		* the GPU, otherwise CUDA will not yield while waiting for results and
	* actively spin on the processor.		* actively spin on the processor.
	*		*
	* - ::CU_CTX_SCHED_SPIN: Instruct CUDA to actively spin when waiting for		* - ::CU_CTX_SCHED_SPIN: Instruct CUDA to actively spin when waiting for
	* results from the GPU. This can decrease latency when waiting for the GPU ,		* results from the GPU. This can decrease latency when waiting for the GPU ,
	* but may lower the performance of CPU threads if they are performing work in		* but may lower the performance of CPU threads if they are performing work in
	* parallel with the CUDA thread.		* parallel with the CUDA thread.
	*		*
	* - ::CU_CTX_SCHED_YIELD: Instruct CUDA to yield its thread when waiting f or		* - ::CU_CTX_SCHED_YIELD: Instruct CUDA to yield its thread when waiting f or
	* results from the GPU. This can increase latency when waiting for the GPU ,		* results from the GPU. This can increase latency when waiting for the GPU ,
	* but can increase the performance of CPU threads performing work in paral lel		* but can increase the performance of CPU threads performing work in paral lel
	* with the GPU.		* with the GPU.
	*		*
			* - ::CU_CTX_SCHED_BLOCKING_SYNC: Instruct CUDA to block the CPU thread on
			a
			* synchronization primitive when waiting for the GPU to finish work.
			*
	* - ::CU_CTX_BLOCKING_SYNC: Instruct CUDA to block the CPU thread on a		* - ::CU_CTX_BLOCKING_SYNC: Instruct CUDA to block the CPU thread on a
	* synchronization primitive when waiting for the GPU to finish work.		* synchronization primitive when waiting for the GPU to finish work.
			* \deprecated This flag was deprecated as of CUDA 4.0 and was replaced
			* with ::CU_CTX_SCHED_BLOCKING_SYNC.
	*		*
	* - ::CU_CTX_MAP_HOST: Instruct CUDA to support mapped pinned allocations.		* - ::CU_CTX_MAP_HOST: Instruct CUDA to support mapped pinned allocations.
	* This flag must be set in order to allocate pinned host memory that is		* This flag must be set in order to allocate pinned host memory that is
	* accessible to the GPU.		* accessible to the GPU.
	*		*
	* - ::CU_CTX_LMEM_RESIZE_TO_MAX: Instruct CUDA to not reduce local memory		* - ::CU_CTX_LMEM_RESIZE_TO_MAX: Instruct CUDA to not reduce local memory
	* after resizing local memory for a kernel. This can prevent thrashing by		* after resizing local memory for a kernel. This can prevent thrashing by
	* local memory allocations when launching many kernels with high local		* local memory allocations when launching many kernels with high local
	* memory usage at the cost of potentially increased memory usage.		* memory usage at the cost of potentially increased memory usage.
	*		*
	* <b>Note to Linux users</b>:
	*
	* Context creation will fail with ::CUDA_ERROR_UNKNOWN if the compute mode of		* Context creation will fail with ::CUDA_ERROR_UNKNOWN if the compute mode of
	* the device is ::CU_COMPUTEMODE_PROHIBITED. Similarly, context creation w ill		* the device is ::CU_COMPUTEMODE_PROHIBITED. Similarly, context creation w ill
	* also fail with ::CUDA_ERROR_UNKNOWN if the compute mode for the device i s		* also fail with ::CUDA_ERROR_UNKNOWN if the compute mode for the device i s
	* set to ::CU_COMPUTEMODE_EXCLUSIVE and there is already an active context on		* set to ::CU_COMPUTEMODE_EXCLUSIVE and there is already an active context on
	* the device. The function ::cuDeviceGetAttribute() can be used with		* the device. The function ::cuDeviceGetAttribute() can be used with
	* ::CU_DEVICE_ATTRIBUTE_COMPUTE_MODE to determine the compute mode of the		* ::CU_DEVICE_ATTRIBUTE_COMPUTE_MODE to determine the compute mode of the
	* device. The <i>nvidia-smi</i> tool can be used to set the compute mode f or		* device. The <i>nvidia-smi</i> tool can be used to set the compute mode f or
	* devices. Documentation for <i>nvidia-smi</i> can be obtained by passing a		* devices. Documentation for <i>nvidia-smi</i> can be obtained by passing a
	* -h option to it.		* -h option to it.
	*		*
	skipping to change at line 1311		skipping to change at line 1527
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,		* ::CUDA_ERROR_NOT_INITIALIZED,
	* ::CUDA_ERROR_INVALID_CONTEXT,		* ::CUDA_ERROR_INVALID_CONTEXT,
	* ::CUDA_ERROR_INVALID_DEVICE,		* ::CUDA_ERROR_INVALID_DEVICE,
	* ::CUDA_ERROR_INVALID_VALUE,		* ::CUDA_ERROR_INVALID_VALUE,
	* ::CUDA_ERROR_OUT_OF_MEMORY,		* ::CUDA_ERROR_OUT_OF_MEMORY,
	* ::CUDA_ERROR_UNKNOWN		* ::CUDA_ERROR_UNKNOWN
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cuCtxAttach,		* \sa ::cuCtxDestroy,
	* ::cuCtxDestroy,
	* ::cuCtxDetach,
	* ::cuCtxGetApiVersion,		* ::cuCtxGetApiVersion,
	* ::cuCtxGetCacheConfig,		* ::cuCtxGetCacheConfig,
	* ::cuCtxGetDevice,		* ::cuCtxGetDevice,
	* ::cuCtxGetLimit,		* ::cuCtxGetLimit,
	* ::cuCtxPopCurrent,		* ::cuCtxPopCurrent,
	* ::cuCtxPushCurrent,		* ::cuCtxPushCurrent,
	* ::cuCtxSetCacheConfig,		* ::cuCtxSetCacheConfig,
	* ::cuCtxSetLimit,		* ::cuCtxSetLimit,
	* ::cuCtxSynchronize		* ::cuCtxSynchronize
	*/		*/
	CUresult CUDAAPI cuCtxCreate(CUcontext *pctx, unsigned int flags, CUdevice dev);		CUresult CUDAAPI cuCtxCreate(CUcontext *pctx, unsigned int flags, CUdevice dev);
	#endif /* __CUDA_API_VERSION >= 3020 */		#endif /* __CUDA_API_VERSION >= 3020 */
			#if __CUDA_API_VERSION >= 4000
	/**		/**
	* \brief Destroy the current context or a floating CUDA context		* \brief Destroy a CUDA context
	*		*
	* Destroys the CUDA context specified by \p ctx. If the context usage coun		* Destroys the CUDA context specified by \p ctx. The context \p ctx will
	t is		be
	* not equal to 1, or the context is current to any CPU thread other than t		* destroyed regardless of how many threads it is current to. It is the
	he		* caller's responsibility to ensure that no API call is issued to \p ctx
	* current one, this function fails. Floating contexts (detached from a CPU		* while ::cuCtxDestroy() is executing.
	* thread via ::cuCtxPopCurrent()) may be destroyed by this function.		*
			* If \p ctx is current to the calling thread then \p ctx will also be
			* popped from the current thread's context stack (as though ::cuCtxPopCurr
			ent()
			* were called). If \p ctx is current to other threads, then \p ctx will
			* remain current to those threads, and attempting to access \p ctx from
			* those threads will result in the error ::CUDA_ERROR_CONTEXT_IS_DESTROYED
			.
	*		*
	* \param ctx - Context to destroy		* \param ctx - Context to destroy
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,		* ::CUDA_ERROR_NOT_INITIALIZED,
	* ::CUDA_ERROR_INVALID_CONTEXT,		* ::CUDA_ERROR_INVALID_CONTEXT,
	* ::CUDA_ERROR_INVALID_VALUE		* ::CUDA_ERROR_INVALID_VALUE
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cuCtxAttach,		* \sa ::cuCtxCreate,
	* ::cuCtxCreate,
	* ::cuCtxDetach,
	* ::cuCtxGetApiVersion,		* ::cuCtxGetApiVersion,
	* ::cuCtxGetCacheConfig,		* ::cuCtxGetCacheConfig,
	* ::cuCtxGetDevice,		* ::cuCtxGetDevice,
	* ::cuCtxGetLimit,		* ::cuCtxGetLimit,
	* ::cuCtxPopCurrent,		* ::cuCtxPopCurrent,
	* ::cuCtxPushCurrent,		* ::cuCtxPushCurrent,
	* ::cuCtxSetCacheConfig,		* ::cuCtxSetCacheConfig,
	* ::cuCtxSetLimit,		* ::cuCtxSetLimit,
	* ::cuCtxSynchronize		* ::cuCtxSynchronize
	*/		*/
	CUresult CUDAAPI cuCtxDestroy(CUcontext ctx);		CUresult CUDAAPI cuCtxDestroy(CUcontext ctx);
			#endif /* __CUDA_API_VERSION >= 4000 */
			/**
			* \defgroup CUDA_CTX_DEPRECATED Context Management [DEPRECATED]
			*
			* This section describes the deprecated context management functions of th
			e low-level
			* CUDA driver application programming interface.
			*
			* @{
			*/
	/**		/**
	* \brief Increment a context's usage-count		* \brief Increment a context's usage-count
	*		*
			* \deprecated
			*
			* Note that this function is deprecated and should not be used.
			*
	* Increments the usage count of the context and passes back a context hand le		* Increments the usage count of the context and passes back a context hand le
	* in \p *pctx that must be passed to ::cuCtxDetach() when the application is		* in \p *pctx that must be passed to ::cuCtxDetach() when the application is
	* done with the context. ::cuCtxAttach() fails if there is no context curr ent		* done with the context. ::cuCtxAttach() fails if there is no context curr ent
	* to the thread.		* to the thread.
	*		*
	* Currently, the \p flags parameter must be 0.		* Currently, the \p flags parameter must be 0.
	*		*
	* \param pctx - Returned context handle of the current context		* \param pctx - Returned context handle of the current context
	* \param flags - Context attach flags (must be 0)		* \param flags - Context attach flags (must be 0)
	*		*
	skipping to change at line 1399		skipping to change at line 1632
	* ::cuCtxPushCurrent,		* ::cuCtxPushCurrent,
	* ::cuCtxSetCacheConfig,		* ::cuCtxSetCacheConfig,
	* ::cuCtxSetLimit,		* ::cuCtxSetLimit,
	* ::cuCtxSynchronize		* ::cuCtxSynchronize
	*/		*/
	CUresult CUDAAPI cuCtxAttach(CUcontext *pctx, unsigned int flags);		CUresult CUDAAPI cuCtxAttach(CUcontext *pctx, unsigned int flags);
	/**		/**
	* \brief Decrement a context's usage-count		* \brief Decrement a context's usage-count
	*		*
			* \deprecated
			*
			* Note that this function is deprecated and should not be used.
			*
	* Decrements the usage count of the context \p ctx, and destroys the conte xt		* Decrements the usage count of the context \p ctx, and destroys the conte xt
	* if the usage count goes to 0. The context must be a handle that was pass ed		* if the usage count goes to 0. The context must be a handle that was pass ed
	* back by ::cuCtxCreate() or ::cuCtxAttach(), and must be current to the		* back by ::cuCtxCreate() or ::cuCtxAttach(), and must be current to the
	* calling thread.		* calling thread.
	*		*
	* \param ctx - Context to destroy		* \param ctx - Context to destroy
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,		* ::CUDA_ERROR_NOT_INITIALIZED,
	* ::CUDA_ERROR_INVALID_CONTEXT		* ::CUDA_ERROR_INVALID_CONTEXT
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cuCtxAttach,		* \sa ::cuCtxCreate,
	* ::cuCtxCreate,
	* ::cuCtxDestroy,		* ::cuCtxDestroy,
	* ::cuCtxGetApiVersion,		* ::cuCtxGetApiVersion,
	* ::cuCtxGetCacheConfig,		* ::cuCtxGetCacheConfig,
	* ::cuCtxGetDevice,		* ::cuCtxGetDevice,
	* ::cuCtxGetLimit,		* ::cuCtxGetLimit,
	* ::cuCtxPopCurrent,		* ::cuCtxPopCurrent,
	* ::cuCtxPushCurrent,		* ::cuCtxPushCurrent,
	* ::cuCtxSetCacheConfig,		* ::cuCtxSetCacheConfig,
	* ::cuCtxSetLimit,		* ::cuCtxSetLimit,
	* ::cuCtxSynchronize		* ::cuCtxSynchronize
	*/		*/
	CUresult CUDAAPI cuCtxDetach(CUcontext ctx);		CUresult CUDAAPI cuCtxDetach(CUcontext ctx);
			/** @} */ /* END CUDA_CTX_DEPRECATED */
			#if __CUDA_API_VERSION >= 4000
	/**		/**
	* \brief Pushes a floating context on the current CPU thread		* \brief Pushes a context on the current CPU thread
	*		*
	* Pushes the given context \p ctx onto the CPU thread's stack of current		* Pushes the given context \p ctx onto the CPU thread's stack of current
	* contexts. The specified context becomes the CPU thread's current context , so		* contexts. The specified context becomes the CPU thread's current context , so
	* all CUDA functions that operate on the current context are affected.		* all CUDA functions that operate on the current context are affected.
	*		*
	* The previous current context may be made current again by calling		* The previous current context may be made current again by calling
	* ::cuCtxDestroy() or ::cuCtxPopCurrent().		* ::cuCtxDestroy() or ::cuCtxPopCurrent().
	*		*
	* The context must be "floating," i.e. not attached to any thread. Context		* \param ctx - Context to push
	s are
	* made to float by calling ::cuCtxPopCurrent().
	*
	* \param ctx - Floating context to attach
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,		* ::CUDA_ERROR_NOT_INITIALIZED,
	* ::CUDA_ERROR_INVALID_CONTEXT,		* ::CUDA_ERROR_INVALID_CONTEXT,
	* ::CUDA_ERROR_INVALID_VALUE		* ::CUDA_ERROR_INVALID_VALUE
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cuCtxAttach,		* \sa ::cuCtxCreate,
	* ::cuCtxCreate,
	* ::cuCtxDestroy,		* ::cuCtxDestroy,
	* ::cuCtxDetach,
	* ::cuCtxGetApiVersion,		* ::cuCtxGetApiVersion,
	* ::cuCtxGetCacheConfig,		* ::cuCtxGetCacheConfig,
	* ::cuCtxGetDevice,		* ::cuCtxGetDevice,
	* ::cuCtxGetLimit,		* ::cuCtxGetLimit,
	* ::cuCtxPopCurrent,		* ::cuCtxPopCurrent,
	* ::cuCtxSetCacheConfig,		* ::cuCtxSetCacheConfig,
	* ::cuCtxSetLimit,		* ::cuCtxSetLimit,
	* ::cuCtxSynchronize		* ::cuCtxSynchronize
	*/		*/
	CUresult CUDAAPI cuCtxPushCurrent(CUcontext ctx );		CUresult CUDAAPI cuCtxPushCurrent(CUcontext ctx);
	/**		/**
	* \brief Pops the current CUDA context from the current CPU thread		* \brief Pops the current CUDA context from the current CPU thread.
	*
	* Pops the current CUDA context from the CPU thread. The CUDA context must
	* have a usage count of 1. CUDA contexts have a usage count of 1 upon
	* creation; the usage count may be incremented with ::cuCtxAttach() and
	* decremented with ::cuCtxDetach().
	*
	* If successful, ::cuCtxPopCurrent() passes back the old context handle in
	* \p *pctx. That context may then be made current to a different CPU threa
	d
	* by calling ::cuCtxPushCurrent().
	*		*
	* Floating contexts may be destroyed by calling ::cuCtxDestroy().		* Pops the current CUDA context from the CPU thread and passes back the
			* old context handle in \p *pctx. That context may then be made current
			* to a different CPU thread by calling ::cuCtxPushCurrent().
	*		*
	* If a context was current to the CPU thread before ::cuCtxCreate() or		* If a context was current to the CPU thread before ::cuCtxCreate() or
	* ::cuCtxPushCurrent() was called, this function makes that context curren t to		* ::cuCtxPushCurrent() was called, this function makes that context curren t to
	* the CPU thread again.		* the CPU thread again.
	*		*
	* \param pctx - Returned new context handle		* \param pctx - Returned new context handle
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,		* ::CUDA_ERROR_NOT_INITIALIZED,
	* ::CUDA_ERROR_INVALID_CONTEXT		* ::CUDA_ERROR_INVALID_CONTEXT
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cuCtxAttach,		* \sa ::cuCtxCreate,
	* ::cuCtxCreate,
	* ::cuCtxDestroy,		* ::cuCtxDestroy,
	* ::cuCtxDetach,
	* ::cuCtxGetApiVersion,		* ::cuCtxGetApiVersion,
	* ::cuCtxGetCacheConfig,		* ::cuCtxGetCacheConfig,
	* ::cuCtxGetDevice,		* ::cuCtxGetDevice,
	* ::cuCtxGetLimit,		* ::cuCtxGetLimit,
	* ::cuCtxPushCurrent,		* ::cuCtxPushCurrent,
	* ::cuCtxSetCacheConfig,		* ::cuCtxSetCacheConfig,
	* ::cuCtxSetLimit,		* ::cuCtxSetLimit,
	* ::cuCtxSynchronize		* ::cuCtxSynchronize
	*/		*/
	CUresult CUDAAPI cuCtxPopCurrent(CUcontext *pctx);		CUresult CUDAAPI cuCtxPopCurrent(CUcontext *pctx);
	/**		/**
			* \brief Binds the specified CUDA context to the calling CPU thread
			*
			* Binds the specified CUDA context to the calling CPU thread.
			* If \p ctx is NULL then the CUDA context previously bound to the
			* calling CPU thread is unbound and ::CUDA_SUCCESS is returned.
			*
			* If there exists a CUDA context stack on the calling CPU thread, this
			* will replace the top of that stack with \p ctx.
			* If \p ctx is NULL then this will be equivalent to popping the top
			* of the calling CPU thread's CUDA context stack (or a no-op if the
			* calling CPU thread's CUDA context stack is empty).
			*
			* \param ctx - Context to bind to the calling CPU thread
			*
			* \return
			* ::CUDA_SUCCESS,
			* ::CUDA_ERROR_DEINITIALIZED,
			* ::CUDA_ERROR_NOT_INITIALIZED,
			* ::CUDA_ERROR_INVALID_CONTEXT
			* \notefnerr
			*
			* \sa ::cuCtxGetCurrent, ::cuCtxCreate, ::cuCtxDestroy
			*/
			CUresult CUDAAPI cuCtxSetCurrent(CUcontext ctx);
			/**
			* \brief Returns the CUDA context bound to the calling CPU thread.
			*
			* Returns in \p *pctx the CUDA context bound to the calling CPU thread.
			* If no context is bound to the calling CPU thread then \p *pctx is
			* set to NULL and ::CUDA_SUCCESS is returned.
			*
			* \param pctx - Returned context handle
			*
			* \return
			* ::CUDA_SUCCESS,
			* ::CUDA_ERROR_DEINITIALIZED,
			* ::CUDA_ERROR_NOT_INITIALIZED,
			* \notefnerr
			*
			* \sa ::cuCtxSetCurrent, ::cuCtxCreate, ::cuCtxDestroy
			*/
			CUresult CUDAAPI cuCtxGetCurrent(CUcontext *pctx);
			#endif /* __CUDA_API_VERSION >= 4000 */
			/**
	* \brief Returns the device ID for the current context		* \brief Returns the device ID for the current context
	*		*
	* Returns in \p *device the ordinal of the current context's device.		* Returns in \p *device the ordinal of the current context's device.
	*		*
	* \param device - Returned device ID for the current context		* \param device - Returned device ID for the current context
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,		* ::CUDA_ERROR_NOT_INITIALIZED,
	* ::CUDA_ERROR_INVALID_CONTEXT,		* ::CUDA_ERROR_INVALID_CONTEXT,
	* ::CUDA_ERROR_INVALID_VALUE,		* ::CUDA_ERROR_INVALID_VALUE,
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cuCtxAttach,		* \sa ::cuCtxCreate,
	* ::cuCtxCreate,
	* ::cuCtxDestroy,		* ::cuCtxDestroy,
	* ::cuCtxDetach,
	* ::cuCtxGetApiVersion,		* ::cuCtxGetApiVersion,
	* ::cuCtxGetCacheConfig,		* ::cuCtxGetCacheConfig,
	* ::cuCtxGetLimit,		* ::cuCtxGetLimit,
	* ::cuCtxPopCurrent,		* ::cuCtxPopCurrent,
	* ::cuCtxPushCurrent,		* ::cuCtxPushCurrent,
	* ::cuCtxSetCacheConfig,		* ::cuCtxSetCacheConfig,
	* ::cuCtxSetLimit,		* ::cuCtxSetLimit,
	* ::cuCtxSynchronize		* ::cuCtxSynchronize
	*/		*/
	CUresult CUDAAPI cuCtxGetDevice(CUdevice *device);		CUresult CUDAAPI cuCtxGetDevice(CUdevice *device);
	/**		/**
	* \brief Block for a context's tasks to complete		* \brief Block for a context's tasks to complete
	*		*
	* Blocks until the device has completed all preceding requested tasks.		* Blocks until the device has completed all preceding requested tasks.
	* ::cuCtxSynchronize() returns an error if one of the preceding tasks fail ed.		* ::cuCtxSynchronize() returns an error if one of the preceding tasks fail ed.
	* If the context was created with the ::CU_CTX_BLOCKING_SYNC flag, the CPU		* If the context was created with the ::CU_CTX_SCHED_BLOCKING_SYNC flag, t
	* thread will block until the GPU context has finished its work.		he
			* CPU thread will block until the GPU context has finished its work.
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,		* ::CUDA_ERROR_NOT_INITIALIZED,
	* ::CUDA_ERROR_INVALID_CONTEXT		* ::CUDA_ERROR_INVALID_CONTEXT
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cuCtxAttach,		* \sa ::cuCtxCreate,
	* ::cuCtxCreate,
	* ::cuCtxDestroy,		* ::cuCtxDestroy,
	* ::cuCtxDetach,
	* ::cuCtxGetApiVersion,		* ::cuCtxGetApiVersion,
	* ::cuCtxGetCacheConfig,		* ::cuCtxGetCacheConfig,
	* ::cuCtxGetDevice,		* ::cuCtxGetDevice,
	* ::cuCtxGetLimit,		* ::cuCtxGetLimit,
	* ::cuCtxPopCurrent,		* ::cuCtxPopCurrent,
	* ::cuCtxPushCurrent		* ::cuCtxPushCurrent
	* ::cuCtxSetCacheConfig,		* ::cuCtxSetCacheConfig,
	* ::cuCtxSetLimit		* ::cuCtxSetLimit
	*/		*/
	CUresult CUDAAPI cuCtxSynchronize(void);		CUresult CUDAAPI cuCtxSynchronize(void);
	skipping to change at line 1616		skipping to change at line 1883
	*		*
	* \param limit - Limit to set		* \param limit - Limit to set
	* \param value - Size in bytes of limit		* \param value - Size in bytes of limit
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_INVALID_VALUE,		* ::CUDA_ERROR_INVALID_VALUE,
	* ::CUDA_ERROR_UNSUPPORTED_LIMIT		* ::CUDA_ERROR_UNSUPPORTED_LIMIT
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cuCtxAttach,		* \sa ::cuCtxCreate,
	* ::cuCtxCreate,
	* ::cuCtxDestroy,		* ::cuCtxDestroy,
	* ::cuCtxDetach,
	* ::cuCtxGetApiVersion,		* ::cuCtxGetApiVersion,
	* ::cuCtxGetCacheConfig,		* ::cuCtxGetCacheConfig,
	* ::cuCtxGetDevice,		* ::cuCtxGetDevice,
	* ::cuCtxGetLimit,		* ::cuCtxGetLimit,
	* ::cuCtxPopCurrent,		* ::cuCtxPopCurrent,
	* ::cuCtxPushCurrent,		* ::cuCtxPushCurrent,
	* ::cuCtxSetCacheConfig,		* ::cuCtxSetCacheConfig,
	* ::cuCtxSynchronize		* ::cuCtxSynchronize
	*/		*/
	CUresult CUDAAPI cuCtxSetLimit(CUlimit limit, size_t value);		CUresult CUDAAPI cuCtxSetLimit(CUlimit limit, size_t value);
	skipping to change at line 1651		skipping to change at line 1916
	*		*
	* \param limit - Limit to query		* \param limit - Limit to query
	* \param pvalue - Returned size in bytes of limit		* \param pvalue - Returned size in bytes of limit
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_INVALID_VALUE,		* ::CUDA_ERROR_INVALID_VALUE,
	* ::CUDA_ERROR_UNSUPPORTED_LIMIT		* ::CUDA_ERROR_UNSUPPORTED_LIMIT
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cuCtxAttach,		* \sa ::cuCtxCreate,
	* ::cuCtxCreate,
	* ::cuCtxDestroy,		* ::cuCtxDestroy,
	* ::cuCtxDetach,
	* ::cuCtxGetApiVersion,		* ::cuCtxGetApiVersion,
	* ::cuCtxGetCacheConfig,		* ::cuCtxGetCacheConfig,
	* ::cuCtxGetDevice,		* ::cuCtxGetDevice,
	* ::cuCtxPopCurrent,		* ::cuCtxPopCurrent,
	* ::cuCtxPushCurrent,		* ::cuCtxPushCurrent,
	* ::cuCtxSetCacheConfig,		* ::cuCtxSetCacheConfig,
	* ::cuCtxSetLimit,		* ::cuCtxSetLimit,
	* ::cuCtxSynchronize		* ::cuCtxSynchronize
	*/		*/
	CUresult CUDAAPI cuCtxGetLimit(size_t *pvalue, CUlimit limit);		CUresult CUDAAPI cuCtxGetLimit(size_t *pvalue, CUlimit limit);
	skipping to change at line 1693		skipping to change at line 1956
	* \param pconfig - Returned cache configuration		* \param pconfig - Returned cache configuration
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,		* ::CUDA_ERROR_NOT_INITIALIZED,
	* ::CUDA_ERROR_INVALID_CONTEXT,		* ::CUDA_ERROR_INVALID_CONTEXT,
	* ::CUDA_ERROR_INVALID_VALUE		* ::CUDA_ERROR_INVALID_VALUE
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cuCtxAttach,		* \sa ::cuCtxCreate,
	* ::cuCtxCreate,
	* ::cuCtxDestroy,		* ::cuCtxDestroy,
	* ::cuCtxDetach,
	* ::cuCtxGetApiVersion,		* ::cuCtxGetApiVersion,
	* ::cuCtxGetDevice,		* ::cuCtxGetDevice,
	* ::cuCtxGetLimit,		* ::cuCtxGetLimit,
	* ::cuCtxPopCurrent,		* ::cuCtxPopCurrent,
	* ::cuCtxPushCurrent,		* ::cuCtxPushCurrent,
	* ::cuCtxSetCacheConfig,		* ::cuCtxSetCacheConfig,
	* ::cuCtxSetLimit,		* ::cuCtxSetLimit,
	* ::cuCtxSynchronize,		* ::cuCtxSynchronize,
	* ::cuFuncSetCacheConfig		* ::cuFuncSetCacheConfig
	*/		*/
	skipping to change at line 1743		skipping to change at line 2004
	* \param config - Requested cache configuration		* \param config - Requested cache configuration
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,		* ::CUDA_ERROR_NOT_INITIALIZED,
	* ::CUDA_ERROR_INVALID_CONTEXT,		* ::CUDA_ERROR_INVALID_CONTEXT,
	* ::CUDA_ERROR_INVALID_VALUE		* ::CUDA_ERROR_INVALID_VALUE
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cuCtxAttach,		* \sa ::cuCtxCreate,
	* ::cuCtxCreate,
	* ::cuCtxDestroy,		* ::cuCtxDestroy,
	* ::cuCtxDetach,
	* ::cuCtxGetApiVersion,		* ::cuCtxGetApiVersion,
	* ::cuCtxGetCacheConfig,		* ::cuCtxGetCacheConfig,
	* ::cuCtxGetDevice,		* ::cuCtxGetDevice,
	* ::cuCtxGetLimit,		* ::cuCtxGetLimit,
	* ::cuCtxPopCurrent,		* ::cuCtxPopCurrent,
	* ::cuCtxPushCurrent,		* ::cuCtxPushCurrent,
	* ::cuCtxSetLimit,		* ::cuCtxSetLimit,
	* ::cuCtxSynchronize,		* ::cuCtxSynchronize,
	* ::cuFuncSetCacheConfig		* ::cuFuncSetCacheConfig
	*/		*/
	skipping to change at line 1782		skipping to change at line 2041
	* \param version - Pointer to version		* \param version - Pointer to version
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,		* ::CUDA_ERROR_NOT_INITIALIZED,
	* ::CUDA_ERROR_INVALID_CONTEXT,		* ::CUDA_ERROR_INVALID_CONTEXT,
	* ::CUDA_ERROR_UNKNOWN		* ::CUDA_ERROR_UNKNOWN
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cuCtxAttach,		* \sa ::cuCtxCreate,
	* ::cuCtxCreate,
	* ::cuCtxDestroy,		* ::cuCtxDestroy,
	* ::cuCtxDetach,
	* ::cuCtxGetDevice,		* ::cuCtxGetDevice,
	* ::cuCtxGetLimit,		* ::cuCtxGetLimit,
	* ::cuCtxPopCurrent,		* ::cuCtxPopCurrent,
	* ::cuCtxPushCurrent,		* ::cuCtxPushCurrent,
	* ::cuCtxSetCacheConfig,		* ::cuCtxSetCacheConfig,
	* ::cuCtxSetLimit,		* ::cuCtxSetLimit,
	* ::cuCtxSynchronize		* ::cuCtxSynchronize
	*/		*/
	CUresult CUDAAPI cuCtxGetApiVersion(CUcontext ctx, unsigned int *version);		CUresult CUDAAPI cuCtxGetApiVersion(CUcontext ctx, unsigned int *version);
	skipping to change at line 1815		skipping to change at line 2072
	*/		*/
	/**		/**
	* \brief Loads a compute module		* \brief Loads a compute module
	*		*
	* Takes a filename \p fname and loads the corresponding module \p module i nto		* Takes a filename \p fname and loads the corresponding module \p module i nto
	* the current context. The CUDA driver API does not attempt to lazily		* the current context. The CUDA driver API does not attempt to lazily
	* allocate the resources needed by a module; if the memory for functions a nd		* allocate the resources needed by a module; if the memory for functions a nd
	* data (constant and global) needed by the module cannot be allocated,		* data (constant and global) needed by the module cannot be allocated,
	* ::cuModuleLoad() fails. The file should be a \e cubin file as output by		* ::cuModuleLoad() fails. The file should be a \e cubin file as output by
	* \b nvcc or a \e PTX file, either as output by \b nvcc or handwrtten.		* \b nvcc, or a \e PTX file either as output by \b nvcc or handwritten, or
			* a \e fatbin file as output by \b nvcc from toolchain 4.0 or later.
	*		*
	* \param module - Returned module		* \param module - Returned module
	* \param fname - Filename of module to load		* \param fname - Filename of module to load
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,		* ::CUDA_ERROR_NOT_INITIALIZED,
	* ::CUDA_ERROR_INVALID_CONTEXT,		* ::CUDA_ERROR_INVALID_CONTEXT,
	* ::CUDA_ERROR_INVALID_VALUE,		* ::CUDA_ERROR_INVALID_VALUE,
	skipping to change at line 1848		skipping to change at line 2106
	* ::cuModuleLoadFatBinary,		* ::cuModuleLoadFatBinary,
	* ::cuModuleUnload		* ::cuModuleUnload
	*/		*/
	CUresult CUDAAPI cuModuleLoad(CUmodule *module, const char *fname);		CUresult CUDAAPI cuModuleLoad(CUmodule *module, const char *fname);
	/**		/**
	* \brief Load a module's data		* \brief Load a module's data
	*		*
	* Takes a pointer \p image and loads the corresponding module \p module in to		* Takes a pointer \p image and loads the corresponding module \p module in to
	* the current context. The pointer may be obtained by mapping a \e cubin o r		* the current context. The pointer may be obtained by mapping a \e cubin o r
	* \e PTX file, passing a \e cubin or \e PTX file as a NULL-terminated text		* \e PTX or \e fatbin file, passing a \e cubin or \e PTX or \e fatbin file
	* string, or incorporating a \e cubin object into the executable resources		* as a NULL-terminated text string, or incorporating a \e cubin or \e fatb
	* and using operating system calls such as Windows \c FindResource() to		in
	* obtain the pointer.		* object into the executable resources and using operating system calls su
			ch
			* as Windows \c FindResource() to obtain the pointer.
	*		*
	* \param module - Returned module		* \param module - Returned module
	* \param image - Module data to load		* \param image - Module data to load
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,		* ::CUDA_ERROR_NOT_INITIALIZED,
	* ::CUDA_ERROR_INVALID_CONTEXT,		* ::CUDA_ERROR_INVALID_CONTEXT,
	* ::CUDA_ERROR_INVALID_VALUE,		* ::CUDA_ERROR_INVALID_VALUE,
	skipping to change at line 1882		skipping to change at line 2140
	* ::cuModuleLoadFatBinary,		* ::cuModuleLoadFatBinary,
	* ::cuModuleUnload		* ::cuModuleUnload
	*/		*/
	CUresult CUDAAPI cuModuleLoadData(CUmodule *module, const void *image);		CUresult CUDAAPI cuModuleLoadData(CUmodule *module, const void *image);
	/**		/**
	* \brief Load a module's data with options		* \brief Load a module's data with options
	*		*
	* Takes a pointer \p image and loads the corresponding module \p module in to		* Takes a pointer \p image and loads the corresponding module \p module in to
	* the current context. The pointer may be obtained by mapping a \e cubin o r		* the current context. The pointer may be obtained by mapping a \e cubin o r
	* \e PTX file, passing a \e cubin or \e PTX file as a NULL-terminated text		* \e PTX or \e fatbin file, passing a \e cubin or \e PTX or \e fatbin file
	* string, or incorporating a \e cubin object into the executable resources		* as a NULL-terminated text string, or incorporating a \e cubin or \e fatb
	* and using operating system calls such as Windows \c FindResource() to		in
	* obtain the pointer. Options are passed as an array via \p options and an		* object into the executable resources and using operating system calls su
	y		ch
	* corresponding parameters are passed in \p optionValues. The number of to		* as Windows \c FindResource() to obtain the pointer. Options are passed a
	tal		s
	* options is supplied via \p numOptions. Any outputs will be returned via		* an array via \p options and any corresponding parameters are passed in
	* \p optionValues. Supported options are (types for the option values are		* \p optionValues. The number of total options is supplied via \p numOptio
	* specified in parentheses after the option name):		ns.
			* Any outputs will be returned via \p optionValues. Supported options are
			* (types for the option values are specified in parentheses after the opti
			on
			* name):
	*		*
	* - ::CU_JIT_MAX_REGISTERS: (unsigned int) input specifies the maximum num ber		* - ::CU_JIT_MAX_REGISTERS: (unsigned int) input specifies the maximum num ber
	* of registers per thread;		* of registers per thread;
	* - ::CU_JIT_THREADS_PER_BLOCK: (unsigned int) input specifies number of		* - ::CU_JIT_THREADS_PER_BLOCK: (unsigned int) input specifies number of
	* threads per block to target compilation for; output returns the number o f		* threads per block to target compilation for; output returns the number o f
	* threads the compiler actually targeted;		* threads the compiler actually targeted;
	* - ::CU_JIT_WALL_TIME: (float) output returns the float value of wall clo ck		* - ::CU_JIT_WALL_TIME: (float) output returns the float value of wall clo ck
	* time, in milliseconds, spent compiling the \e PTX code;		* time, in milliseconds, spent compiling the \e PTX code;
	* - ::CU_JIT_INFO_LOG_BUFFER: (char*) input is a pointer to a buffer in		* - ::CU_JIT_INFO_LOG_BUFFER: (char*) input is a pointer to a buffer in
	* which to print any informational log messages from \e PTX assembly (the		* which to print any informational log messages from \e PTX assembly (the
	skipping to change at line 1960		skipping to change at line 2219
	* ::cuModuleLoadFatBinary,		* ::cuModuleLoadFatBinary,
	* ::cuModuleUnload		* ::cuModuleUnload
	*/		*/
	CUresult CUDAAPI cuModuleLoadDataEx(CUmodule *module, const void *image, un signed int numOptions, CUjit_option *options, void **optionValues);		CUresult CUDAAPI cuModuleLoadDataEx(CUmodule *module, const void *image, un signed int numOptions, CUjit_option *options, void **optionValues);
	/**		/**
	* \brief Load a module's data		* \brief Load a module's data
	*		*
	* Takes a pointer \p fatCubin and loads the corresponding module \p module		* Takes a pointer \p fatCubin and loads the corresponding module \p module
	* into the current context. The pointer represents a <i>fat binary</i> obj ect,		* into the current context. The pointer represents a <i>fat binary</i> obj ect,
	* which is a collection of different \e cubin files, all representing the		* which is a collection of different \e cubin and/or \e PTX files, all
	same		* representing the same device code, but compiled and optimized for differ
	* device code, but compiled and optimized for different architectures. The		ent
	re		* architectures.
	* is currently no documented API for constructing and using fat binary obj		*
	ects		* Prior to CUDA 4.0, there was no documented API for constructing and usin
	* by programmers, and therefore this function is an internal function in t		g
	his		* fat binary objects by programmers. Starting with CUDA 4.0, fat binary
	* version of CUDA. More information can be found in the \b nvcc document.		* objects can be constructed by providing the <i>-fatbin option</i> to \b
			nvcc.
			* More information can be found in the \b nvcc document.
	*		*
	* \param module - Returned module		* \param module - Returned module
	* \param fatCubin - Fat binary to load		* \param fatCubin - Fat binary to load
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,		* ::CUDA_ERROR_NOT_INITIALIZED,
	* ::CUDA_ERROR_INVALID_CONTEXT,		* ::CUDA_ERROR_INVALID_CONTEXT,
	* ::CUDA_ERROR_INVALID_VALUE,		* ::CUDA_ERROR_INVALID_VALUE,
	skipping to change at line 2358		skipping to change at line 2620
	* accessible to the device. The driver tracks the virtual memory ranges		* accessible to the device. The driver tracks the virtual memory ranges
	* allocated with this function and automatically accelerates calls to		* allocated with this function and automatically accelerates calls to
	* functions such as ::cuMemcpy(). Since the memory can be accessed directl y by		* functions such as ::cuMemcpy(). Since the memory can be accessed directl y by
	* the device, it can be read or written with much higher bandwidth than		* the device, it can be read or written with much higher bandwidth than
	* pageable memory obtained with functions such as ::malloc(). Allocating		* pageable memory obtained with functions such as ::malloc(). Allocating
	* excessive amounts of memory with ::cuMemAllocHost() may degrade system		* excessive amounts of memory with ::cuMemAllocHost() may degrade system
	* performance, since it reduces the amount of memory available to the syst em		* performance, since it reduces the amount of memory available to the syst em
	* for paging. As a result, this function is best used sparingly to allocat e		* for paging. As a result, this function is best used sparingly to allocat e
	* staging areas for data exchange between host and device.		* staging areas for data exchange between host and device.
	*		*
			* Note all host memory allocated using ::cuMemHostAlloc() will automatical
			ly
			* be immediately accessible to all contexts on all devices which support u
			nified
			* addressing (as may be queried using ::CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRES
			SING).
			* The device pointer that may be used to access this host memory from thos
			e
			* contexts is always equal to the returned host pointer \p *pp.
			* See \ref CUDA_UNIFIED for additional details.
			*
	* \param pp - Returned host pointer to page-locked memory		* \param pp - Returned host pointer to page-locked memory
	* \param bytesize - Requested allocation size in bytes		* \param bytesize - Requested allocation size in bytes
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,		* ::CUDA_ERROR_NOT_INITIALIZED,
	* ::CUDA_ERROR_INVALID_CONTEXT,		* ::CUDA_ERROR_INVALID_CONTEXT,
	* ::CUDA_ERROR_INVALID_VALUE,		* ::CUDA_ERROR_INVALID_VALUE,
	* ::CUDA_ERROR_OUT_OF_MEMORY		* ::CUDA_ERROR_OUT_OF_MEMORY
	skipping to change at line 2459		skipping to change at line 2728
	* The CUDA context must have been created with the ::CU_CTX_MAP_HOST flag in		* The CUDA context must have been created with the ::CU_CTX_MAP_HOST flag in
	* order for the ::CU_MEMHOSTALLOC_MAPPED flag to have any effect.		* order for the ::CU_MEMHOSTALLOC_MAPPED flag to have any effect.
	*		*
	* The ::CU_MEMHOSTALLOC_MAPPED flag may be specified on CUDA contexts for		* The ::CU_MEMHOSTALLOC_MAPPED flag may be specified on CUDA contexts for
	* devices that do not support mapped pinned memory. The failure is deferre d		* devices that do not support mapped pinned memory. The failure is deferre d
	* to ::cuMemHostGetDevicePointer() because the memory may be mapped into		* to ::cuMemHostGetDevicePointer() because the memory may be mapped into
	* other CUDA contexts via the ::CU_MEMHOSTALLOC_PORTABLE flag.		* other CUDA contexts via the ::CU_MEMHOSTALLOC_PORTABLE flag.
	*		*
	* The memory allocated by this function must be freed with ::cuMemFreeHost ().		* The memory allocated by this function must be freed with ::cuMemFreeHost ().
	*		*
			* Note all host memory allocated using ::cuMemHostAlloc() will automatical
			ly
			* be immediately accessible to all contexts on all devices which support u
			nified
			* addressing (as may be queried using ::CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRES
			SING).
			* Unless the flag ::CU_MEMHOSTALLOC_WRITECOMBINED is specified, the device
			pointer
			* that may be used to access this host memory from those contexts is alway
			s equal
			* to the returned host pointer \p *pp. If the flag ::CU_MEMHOSTALLOC_WRIT
			ECOMBINED
			* is specified, then the function ::cuMemHostGetDevicePointer() must be us
			ed
			* to query the device pointer, even if the context supports unified addres
			sing.
			* See \ref CUDA_UNIFIED for additional details.
			*
	* \param pp - Returned host pointer to page-locked memory		* \param pp - Returned host pointer to page-locked memory
	* \param bytesize - Requested allocation size in bytes		* \param bytesize - Requested allocation size in bytes
	* \param Flags - Flags for allocation request		* \param Flags - Flags for allocation request
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,		* ::CUDA_ERROR_NOT_INITIALIZED,
	* ::CUDA_ERROR_INVALID_CONTEXT,		* ::CUDA_ERROR_INVALID_CONTEXT,
	* ::CUDA_ERROR_INVALID_VALUE,		* ::CUDA_ERROR_INVALID_VALUE,
	skipping to change at line 2548		skipping to change at line 2827
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,		* ::CUDA_ERROR_NOT_INITIALIZED,
	* ::CUDA_ERROR_INVALID_CONTEXT,		* ::CUDA_ERROR_INVALID_CONTEXT,
	* ::CUDA_ERROR_INVALID_VALUE		* ::CUDA_ERROR_INVALID_VALUE
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cuMemAllocHost, ::cuMemHostAlloc		* \sa ::cuMemAllocHost, ::cuMemHostAlloc
	*/		*/
	CUresult CUDAAPI cuMemHostGetFlags(unsigned int *pFlags, void *p);		CUresult CUDAAPI cuMemHostGetFlags(unsigned int *pFlags, void *p);
			#if __CUDA_API_VERSION >= 4000
			/**
			* \brief Registers an existing host memory range for use by CUDA
			*
			* Page-locks the memory range specified by \p p and \p bytesize and maps i
			t
			* for the device(s) as specified by \p Flags. This memory range also is ad
			ded
			* to the same tracking mechanism as ::cuMemHostAlloc to automatically acce
			lerate
			* calls to functions such as ::cuMemcpyHtoD(). Since the memory can be acc
			essed
			* directly by the device, it can be read or written with much higher bandw
			idth
			* than pageable memory that has not been registered. Page-locking excessi
			ve
			* amounts of memory may degrade system performance, since it reduces the a
			mount
			* of memory available to the system for paging. As a result, this function
			is
			* best used sparingly to register staging areas for data exchange between
			* host and device.
			*
			* This function is not yet supported on Mac OS X.
			*
			* The \p Flags parameter enables different options to be specified that
			* affect the allocation, as follows.
			*
			* - ::CU_MEMHOSTREGISTER_PORTABLE: The memory returned by this call will b
			e
			* considered as pinned memory by all CUDA contexts, not just the one tha
			t
			* performed the allocation.
			*
			* - ::CU_MEMHOSTREGISTER_DEVICEMAP: Maps the allocation into the CUDA addr
			ess
			* space. The device pointer to the memory may be obtained by calling
			* ::cuMemHostGetDevicePointer(). This feature is available only on GPUs
			* with compute capability greater than or equal to 1.1.
			*
			* All of these flags are orthogonal to one another: a developer may page-l
			ock
			* memory that is portable or mapped with no restrictions.
			*
			* The CUDA context must have been created with the ::CU_CTX_MAP_HOST flag
			in
			* order for the ::CU_MEMHOSTREGISTER_DEVICEMAP flag to have any effect.
			*
			* The ::CU_MEMHOSTREGISTER_DEVICEMAP flag may be specified on CUDA context
			s for
			* devices that do not support mapped pinned memory. The failure is deferre
			d
			* to ::cuMemHostGetDevicePointer() because the memory may be mapped into
			* other CUDA contexts via the ::CU_MEMHOSTREGISTER_PORTABLE flag.
			*
			* The pointer \p p and size \p bytesize must be aligned to the host page
			* size (4 KB).
			*
			* The memory page-locked by this function must be unregistered with
			* ::cuMemHostUnregister().
			*
			* \param p - Host pointer to memory to page-lock
			* \param bytesize - Size in bytes of the address range to page-lock
			* \param Flags - Flags for allocation request
			*
			* \return
			* ::CUDA_SUCCESS,
			* ::CUDA_ERROR_DEINITIALIZED,
			* ::CUDA_ERROR_NOT_INITIALIZED,
			* ::CUDA_ERROR_INVALID_CONTEXT,
			* ::CUDA_ERROR_INVALID_VALUE,
			* ::CUDA_ERROR_OUT_OF_MEMORY
			* \notefnerr
			*
			* \sa ::cuMemHostUnregister, ::cuMemHostGetFlags, ::cuMemHostGetDevicePoin
			ter
			*/
			CUresult CUDAAPI cuMemHostRegister(void *p, size_t bytesize, unsigned int F
			lags);
			/**
			* \brief Unregisters a memory range that was registered with ::cuMemHostRe
			gister().
			*
			* Unmaps the memory range whose base address is specified by \p p, and mak
			es
			* it pageable again.
			*
			* The base address must be the same one specified to ::cuMemHostRegister()
			.
			*
			* \param p - Host pointer to memory to unregister
			*
			* \return
			* ::CUDA_SUCCESS,
			* ::CUDA_ERROR_DEINITIALIZED,
			* ::CUDA_ERROR_NOT_INITIALIZED,
			* ::CUDA_ERROR_INVALID_CONTEXT,
			* ::CUDA_ERROR_INVALID_VALUE,
			* ::CUDA_ERROR_OUT_OF_MEMORY
			* \notefnerr
			*
			* \sa ::cuMemHostRegister
			*/
			CUresult CUDAAPI cuMemHostUnregister(void *p);
			/**
			* \brief Copies memory
			*
			* Copies data between two pointers.
			* \p dst and \p src are base pointers of the destination and source, respe
			ctively.
			* \p ByteCount specifies the number of bytes to copy.
			* Note that this function infers the type of the transfer (host to host, h
			ost to
			* device, device to device, or device to host) from the pointer values.
			This
			* function is only allowed in contexts which support unified addressing.
			* Note that this function is synchronous.
			*
			* \param dst - Destination unified virtual address space pointer
			* \param src - Source unified virtual address space pointer
			* \param ByteCount - Size of memory copy in bytes
			*
			* \return
			* ::CUDA_SUCCESS,
			* ::CUDA_ERROR_DEINITIALIZED,
			* ::CUDA_ERROR_NOT_INITIALIZED,
			* ::CUDA_ERROR_INVALID_CONTEXT,
			* ::CUDA_ERROR_INVALID_VALUE
			* \notefnerr
			*
			* \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
			* ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost
			,
			* ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligne
			d,
			* ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
			* ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA,
			* ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync
			,
			* ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
			* ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
			* ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,
			* ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32
			*/
			CUresult CUDAAPI cuMemcpy(CUdeviceptr dst, CUdeviceptr src, size_t ByteCoun
			t);
			/**
			* \brief Copies device memory between two contexts
			*
			* Copies from device memory in one context to device memory in another
			* context. \p dstDevice is the base device pointer of the destination memo
			ry
			* and \p dstContext is the destination context. \p srcDevice is the base
			* device pointer of the source memory and \p srcContext is the source poin
			ter.
			* \p ByteCount specifies the number of bytes to copy.
			* Note that this function is asynchronous with respect to the host, but
			* serialized with respect all pending and future asynchronous work in to t
			he
			* current context, \p srcContext, and \p dstContext (use ::cuMemcpyPeerAsy
			nc
			* to avoid this synchronization).
			*
			* \param dstDevice - Destination device pointer
			* \param dstContext - Destination context
			* \param srcDevice - Source device pointer
			* \param srcContext - Source context
			* \param ByteCount - Size of memory copy in bytes
			*
			* \return
			* ::CUDA_SUCCESS,
			* ::CUDA_ERROR_DEINITIALIZED,
			* ::CUDA_ERROR_NOT_INITIALIZED,
			* ::CUDA_ERROR_INVALID_CONTEXT,
			* ::CUDA_ERROR_INVALID_VALUE
			* \notefnerr
			*
			* \sa ::cuMemcpyDtoD, ::cuMemcpy3DPeer, ::cuMemcpyDtoDAsync, ::cuMemcpyPee
			rAsync,
			* ::cuMemcpy3DPeerAsync
			*/
			CUresult CUDAAPI cuMemcpyPeer(CUdeviceptr dstDevice, CUcontext dstContext,
			CUdeviceptr srcDevice, CUcontext srcContext, size_t ByteCount);
			#endif /* __CUDA_API_VERSION >= 4000 */
	#if __CUDA_API_VERSION >= 3020		#if __CUDA_API_VERSION >= 3020
	/**		/**
	* \brief Copies memory from Host to Device		* \brief Copies memory from Host to Device
	*		*
	* Copies from host memory to device memory. \p dstDevice and \p srcHost ar e		* Copies from host memory to device memory. \p dstDevice and \p srcHost ar e
	* the base addresses of the destination and source, respectively. \p ByteC ount		* the base addresses of the destination and source, respectively. \p ByteC ount
	* specifies the number of bytes to copy. Note that this function is		* specifies the number of bytes to copy. Note that this function is
	* synchronous.		* synchronous.
	*		*
	* \param dstDevice - Destination device pointer		* \param dstDevice - Destination device pointer
	skipping to change at line 2858		skipping to change at line 3294
	} CUDA_MEMCPY2D;		} CUDA_MEMCPY2D;
	* \endcode		* \endcode
	* where:		* where:
	* - ::srcMemoryType and ::dstMemoryType specify the type of memory of the		* - ::srcMemoryType and ::dstMemoryType specify the type of memory of the
	* source and destination, respectively; ::CUmemorytype_enum is defined a s:		* source and destination, respectively; ::CUmemorytype_enum is defined a s:
	*		*
	* \code		* \code
	typedef enum CUmemorytype_enum {		typedef enum CUmemorytype_enum {
	CU_MEMORYTYPE_HOST = 0x01,		CU_MEMORYTYPE_HOST = 0x01,
	CU_MEMORYTYPE_DEVICE = 0x02,		CU_MEMORYTYPE_DEVICE = 0x02,
	CU_MEMORYTYPE_ARRAY = 0x03		CU_MEMORYTYPE_ARRAY = 0x03,
			CU_MEMORYTYPE_UNIFIED = 0x04
	} CUmemorytype;		} CUmemorytype;
	* \endcode		* \endcode
	*		*
	* \par		* \par
			* If ::srcMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::srcDevice and ::srcPitc
			h
			* specify the (unified virtual address space) base address of the source
			data
			* and the bytes per row to apply. ::srcArray is ignored.
			* This value may be used only if unified addressing is supported in the ca
			lling
			* context.
			*
			* \par
	* If ::srcMemoryType is ::CU_MEMORYTYPE_HOST, ::srcHost and ::srcPitch		* If ::srcMemoryType is ::CU_MEMORYTYPE_HOST, ::srcHost and ::srcPitch
	* specify the (host) base address of the source data and the bytes per row to		* specify the (host) base address of the source data and the bytes per row to
	* apply. ::srcArray is ignored.		* apply. ::srcArray is ignored.
	*		*
	* \par		* \par
	* If ::srcMemoryType is ::CU_MEMORYTYPE_DEVICE, ::srcDevice and ::srcPitch		* If ::srcMemoryType is ::CU_MEMORYTYPE_DEVICE, ::srcDevice and ::srcPitch
	* specify the (device) base address of the source data and the bytes per r ow		* specify the (device) base address of the source data and the bytes per r ow
	* to apply. ::srcArray is ignored.		* to apply. ::srcArray is ignored.
	*		*
	* \par		* \par
	* If ::srcMemoryType is ::CU_MEMORYTYPE_ARRAY, ::srcArray specifies the		* If ::srcMemoryType is ::CU_MEMORYTYPE_ARRAY, ::srcArray specifies the
	* handle of the source data. ::srcHost, ::srcDevice and ::srcPitch are		* handle of the source data. ::srcHost, ::srcDevice and ::srcPitch are
	* ignored.		* ignored.
	*		*
	* \par		* \par
	* If ::dstMemoryType is ::CU_MEMORYTYPE_HOST, ::dstHost and ::dstPitch		* If ::dstMemoryType is ::CU_MEMORYTYPE_HOST, ::dstHost and ::dstPitch
	* specify the (host) base address of the destination data and the bytes pe r		* specify the (host) base address of the destination data and the bytes pe r
	* row to apply. ::dstArray is ignored.		* row to apply. ::dstArray is ignored.
	*		*
	* \par		* \par
			* If ::dstMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::dstDevice and ::dstPitc
			h
			* specify the (unified virtual address space) base address of the source
			data
			* and the bytes per row to apply. ::dstArray is ignored.
			* This value may be used only if unified addressing is supported in the ca
			lling
			* context.
			*
			* \par
	* If ::dstMemoryType is ::CU_MEMORYTYPE_DEVICE, ::dstDevice and ::dstPitch		* If ::dstMemoryType is ::CU_MEMORYTYPE_DEVICE, ::dstDevice and ::dstPitch
	* specify the (device) base address of the destination data and the bytes per		* specify the (device) base address of the destination data and the bytes per
	* row to apply. ::dstArray is ignored.		* row to apply. ::dstArray is ignored.
	*		*
	* \par		* \par
	* If ::dstMemoryType is ::CU_MEMORYTYPE_ARRAY, ::dstArray specifies the		* If ::dstMemoryType is ::CU_MEMORYTYPE_ARRAY, ::dstArray specifies the
	* handle of the destination data. ::dstHost, ::dstDevice and ::dstPitch ar e		* handle of the destination data. ::dstHost, ::dstDevice and ::dstPitch ar e
	* ignored.		* ignored.
	*		*
	* - ::srcXInBytes and ::srcY specify the base address of the source data f or		* - ::srcXInBytes and ::srcY specify the base address of the source data f or
	skipping to change at line 2940		skipping to change at line 3391
	* - ::WidthInBytes and ::Height specify the width (in bytes) and height of		* - ::WidthInBytes and ::Height specify the width (in bytes) and height of
	* the 2D copy being performed.		* the 2D copy being performed.
	* - If specified, ::srcPitch must be greater than or equal to ::WidthInByt es +		* - If specified, ::srcPitch must be greater than or equal to ::WidthInByt es +
	* ::srcXInBytes, and ::dstPitch must be greater than or equal to		* ::srcXInBytes, and ::dstPitch must be greater than or equal to
	* ::WidthInBytes + dstXInBytes.		* ::WidthInBytes + dstXInBytes.
	*		*
	* \par		* \par
	* ::cuMemcpy2D() returns an error if any pitch is greater than the maximum		* ::cuMemcpy2D() returns an error if any pitch is greater than the maximum
	* allowed (::CU_DEVICE_ATTRIBUTE_MAX_PITCH). ::cuMemAllocPitch() passes ba ck		* allowed (::CU_DEVICE_ATTRIBUTE_MAX_PITCH). ::cuMemAllocPitch() passes ba ck
	* pitches that always work with ::cuMemcpy2D(). On intra-device memory cop ies		* pitches that always work with ::cuMemcpy2D(). On intra-device memory cop ies
	* (device ? device, CUDA array ? device, CUDA array ? CUDA array),		* (device to device, CUDA array to device, CUDA array to CUDA array),
	* ::cuMemcpy2D() may fail for pitches not computed by ::cuMemAllocPitch().		* ::cuMemcpy2D() may fail for pitches not computed by ::cuMemAllocPitch().
	* ::cuMemcpy2DUnaligned() does not have this restriction, but may run		* ::cuMemcpy2DUnaligned() does not have this restriction, but may run
	* significantly slower in the cases where ::cuMemcpy2D() would have return ed		* significantly slower in the cases where ::cuMemcpy2D() would have return ed
	* an error code.		* an error code.
	*		*
	* \param pCopy - Parameters for the memory copy		* \param pCopy - Parameters for the memory copy
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	skipping to change at line 3001		skipping to change at line 3452
	} CUDA_MEMCPY2D;		} CUDA_MEMCPY2D;
	* \endcode		* \endcode
	* where:		* where:
	* - ::srcMemoryType and ::dstMemoryType specify the type of memory of the		* - ::srcMemoryType and ::dstMemoryType specify the type of memory of the
	* source and destination, respectively; ::CUmemorytype_enum is defined a s:		* source and destination, respectively; ::CUmemorytype_enum is defined a s:
	*		*
	* \code		* \code
	typedef enum CUmemorytype_enum {		typedef enum CUmemorytype_enum {
	CU_MEMORYTYPE_HOST = 0x01,		CU_MEMORYTYPE_HOST = 0x01,
	CU_MEMORYTYPE_DEVICE = 0x02,		CU_MEMORYTYPE_DEVICE = 0x02,
	CU_MEMORYTYPE_ARRAY = 0x03		CU_MEMORYTYPE_ARRAY = 0x03,
			CU_MEMORYTYPE_UNIFIED = 0x04
	} CUmemorytype;		} CUmemorytype;
	* \endcode		* \endcode
	*		*
	* \par		* \par
			* If ::srcMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::srcDevice and ::srcPitc
			h
			* specify the (unified virtual address space) base address of the source
			data
			* and the bytes per row to apply. ::srcArray is ignored.
			* This value may be used only if unified addressing is supported in the ca
			lling
			* context.
			*
			* \par
	* If ::srcMemoryType is ::CU_MEMORYTYPE_HOST, ::srcHost and ::srcPitch		* If ::srcMemoryType is ::CU_MEMORYTYPE_HOST, ::srcHost and ::srcPitch
	* specify the (host) base address of the source data and the bytes per row to		* specify the (host) base address of the source data and the bytes per row to
	* apply. ::srcArray is ignored.		* apply. ::srcArray is ignored.
	*		*
	* \par		* \par
	* If ::srcMemoryType is ::CU_MEMORYTYPE_DEVICE, ::srcDevice and ::srcPitch		* If ::srcMemoryType is ::CU_MEMORYTYPE_DEVICE, ::srcDevice and ::srcPitch
	* specify the (device) base address of the source data and the bytes per r ow		* specify the (device) base address of the source data and the bytes per r ow
	* to apply. ::srcArray is ignored.		* to apply. ::srcArray is ignored.
	*		*
	* \par		* \par
	* If ::srcMemoryType is ::CU_MEMORYTYPE_ARRAY, ::srcArray specifies the		* If ::srcMemoryType is ::CU_MEMORYTYPE_ARRAY, ::srcArray specifies the
	* handle of the source data. ::srcHost, ::srcDevice and ::srcPitch are		* handle of the source data. ::srcHost, ::srcDevice and ::srcPitch are
	* ignored.		* ignored.
	*		*
	* \par		* \par
			* If ::dstMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::dstDevice and ::dstPitc
			h
			* specify the (unified virtual address space) base address of the source
			data
			* and the bytes per row to apply. ::dstArray is ignored.
			* This value may be used only if unified addressing is supported in the ca
			lling
			* context.
			*
			* \par
	* If ::dstMemoryType is ::CU_MEMORYTYPE_HOST, ::dstHost and ::dstPitch		* If ::dstMemoryType is ::CU_MEMORYTYPE_HOST, ::dstHost and ::dstPitch
	* specify the (host) base address of the destination data and the bytes pe r		* specify the (host) base address of the destination data and the bytes pe r
	* row to apply. ::dstArray is ignored.		* row to apply. ::dstArray is ignored.
	*		*
	* \par		* \par
	* If ::dstMemoryType is ::CU_MEMORYTYPE_DEVICE, ::dstDevice and ::dstPitch		* If ::dstMemoryType is ::CU_MEMORYTYPE_DEVICE, ::dstDevice and ::dstPitch
	* specify the (device) base address of the destination data and the bytes per		* specify the (device) base address of the destination data and the bytes per
	* row to apply. ::dstArray is ignored.		* row to apply. ::dstArray is ignored.
	*		*
	* \par		* \par
	skipping to change at line 3083		skipping to change at line 3549
	* - ::WidthInBytes and ::Height specify the width (in bytes) and height of		* - ::WidthInBytes and ::Height specify the width (in bytes) and height of
	* the 2D copy being performed.		* the 2D copy being performed.
	* - If specified, ::srcPitch must be greater than or equal to ::WidthInByt es +		* - If specified, ::srcPitch must be greater than or equal to ::WidthInByt es +
	* ::srcXInBytes, and ::dstPitch must be greater than or equal to		* ::srcXInBytes, and ::dstPitch must be greater than or equal to
	* ::WidthInBytes + dstXInBytes.		* ::WidthInBytes + dstXInBytes.
	*		*
	* \par		* \par
	* ::cuMemcpy2D() returns an error if any pitch is greater than the maximum		* ::cuMemcpy2D() returns an error if any pitch is greater than the maximum
	* allowed (::CU_DEVICE_ATTRIBUTE_MAX_PITCH). ::cuMemAllocPitch() passes ba ck		* allowed (::CU_DEVICE_ATTRIBUTE_MAX_PITCH). ::cuMemAllocPitch() passes ba ck
	* pitches that always work with ::cuMemcpy2D(). On intra-device memory cop ies		* pitches that always work with ::cuMemcpy2D(). On intra-device memory cop ies
	* (device ? device, CUDA array ? device, CUDA array ? CUDA array),		* (device to device, CUDA array to device, CUDA array to CUDA array),
	* ::cuMemcpy2D() may fail for pitches not computed by ::cuMemAllocPitch().		* ::cuMemcpy2D() may fail for pitches not computed by ::cuMemAllocPitch().
	* ::cuMemcpy2DUnaligned() does not have this restriction, but may run		* ::cuMemcpy2DUnaligned() does not have this restriction, but may run
	* significantly slower in the cases where ::cuMemcpy2D() would have return ed		* significantly slower in the cases where ::cuMemcpy2D() would have return ed
	* an error code.		* an error code.
	*		*
	* \param pCopy - Parameters for the memory copy		* \param pCopy - Parameters for the memory copy
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	skipping to change at line 3152		skipping to change at line 3618
	} CUDA_MEMCPY3D;		} CUDA_MEMCPY3D;
	* \endcode		* \endcode
	* where:		* where:
	* - ::srcMemoryType and ::dstMemoryType specify the type of memory of the		* - ::srcMemoryType and ::dstMemoryType specify the type of memory of the
	* source and destination, respectively; ::CUmemorytype_enum is defined a s:		* source and destination, respectively; ::CUmemorytype_enum is defined a s:
	*		*
	* \code		* \code
	typedef enum CUmemorytype_enum {		typedef enum CUmemorytype_enum {
	CU_MEMORYTYPE_HOST = 0x01,		CU_MEMORYTYPE_HOST = 0x01,
	CU_MEMORYTYPE_DEVICE = 0x02,		CU_MEMORYTYPE_DEVICE = 0x02,
	CU_MEMORYTYPE_ARRAY = 0x03		CU_MEMORYTYPE_ARRAY = 0x03,
			CU_MEMORYTYPE_UNIFIED = 0x04
	} CUmemorytype;		} CUmemorytype;
	* \endcode		* \endcode
	*		*
	* \par		* \par
			* If ::srcMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::srcDevice and ::srcPitc
			h
			* specify the (unified virtual address space) base address of the source
			data
			* and the bytes per row to apply. ::srcArray is ignored.
			* This value may be used only if unified addressing is supported in the ca
			lling
			* context.
			*
			* \par
	* If ::srcMemoryType is ::CU_MEMORYTYPE_HOST, ::srcHost, ::srcPitch and		* If ::srcMemoryType is ::CU_MEMORYTYPE_HOST, ::srcHost, ::srcPitch and
	* ::srcHeight specify the (host) base address of the source data, the byte s		* ::srcHeight specify the (host) base address of the source data, the byte s
	* per row, and the height of each 2D slice of the 3D array. ::srcArray is		* per row, and the height of each 2D slice of the 3D array. ::srcArray is
	* ignored.		* ignored.
	*		*
	* \par		* \par
	* If ::srcMemoryType is ::CU_MEMORYTYPE_DEVICE, ::srcDevice, ::srcPitch an d		* If ::srcMemoryType is ::CU_MEMORYTYPE_DEVICE, ::srcDevice, ::srcPitch an d
	* ::srcHeight specify the (device) base address of the source data, the by tes		* ::srcHeight specify the (device) base address of the source data, the by tes
	* per row, and the height of each 2D slice of the 3D array. ::srcArray is		* per row, and the height of each 2D slice of the 3D array. ::srcArray is
	* ignored.		* ignored.
	*		*
	* \par		* \par
	* If ::srcMemoryType is ::CU_MEMORYTYPE_ARRAY, ::srcArray specifies the		* If ::srcMemoryType is ::CU_MEMORYTYPE_ARRAY, ::srcArray specifies the
	* handle of the source data. ::srcHost, ::srcDevice, ::srcPitch and		* handle of the source data. ::srcHost, ::srcDevice, ::srcPitch and
	* ::srcHeight are ignored.		* ::srcHeight are ignored.
	*		*
	* \par		* \par
			* If ::dstMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::dstDevice and ::dstPitc
			h
			* specify the (unified virtual address space) base address of the source
			data
			* and the bytes per row to apply. ::dstArray is ignored.
			* This value may be used only if unified addressing is supported in the ca
			lling
			* context.
			*
			* \par
	* If ::dstMemoryType is ::CU_MEMORYTYPE_HOST, ::dstHost and ::dstPitch		* If ::dstMemoryType is ::CU_MEMORYTYPE_HOST, ::dstHost and ::dstPitch
	* specify the (host) base address of the destination data, the bytes per r ow,		* specify the (host) base address of the destination data, the bytes per r ow,
	* and the height of each 2D slice of the 3D array. ::dstArray is ignored.		* and the height of each 2D slice of the 3D array. ::dstArray is ignored.
	*		*
	* \par		* \par
	* If ::dstMemoryType is ::CU_MEMORYTYPE_DEVICE, ::dstDevice and ::dstPitch		* If ::dstMemoryType is ::CU_MEMORYTYPE_DEVICE, ::dstDevice and ::dstPitch
	* specify the (device) base address of the destination data, the bytes per		* specify the (device) base address of the destination data, the bytes per
	* row, and the height of each 2D slice of the 3D array. ::dstArray is igno red.		* row, and the height of each 2D slice of the 3D array. ::dstArray is igno red.
	*		*
	* \par		* \par
	skipping to change at line 3263		skipping to change at line 3744
	* ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligne d,		* ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligne d,
	* ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,		* ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
	* ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::c uMemcpyDtoDAsync,		* ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::c uMemcpyDtoDAsync,
	* ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync ,		* ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync ,
	* ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,		* ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
	* ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,		* ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
	* ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,		* ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,
	* ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32		* ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32
	*/		*/
	CUresult CUDAAPI cuMemcpy3D(const CUDA_MEMCPY3D *pCopy);		CUresult CUDAAPI cuMemcpy3D(const CUDA_MEMCPY3D *pCopy);
			#endif /* __CUDA_API_VERSION >= 3020 */
			#if __CUDA_API_VERSION >= 4000
			/**
			* \brief Copies memory between contexts
			*
			* Perform a 3D memory copy according to the parameters specified in
			* \p pCopy. See the definition of the ::CUDA_MEMCPY3D_PEER structure
			* for documentation of its parameters.
			*
			* Note that this function is synchronous with respect to the host only if
			* the source or destination memory is of type ::CU_MEMORYTYPE_HOST.
			* Note also that this copy is serialized with respect all pending and futu
			re
			* asynchronous work in to the current context, the copy's source context,
			* and the copy's destination context (use ::cuMemcpy3DPeerAsync to avoid
			* this synchronization).
			*
			* \param pCopy - Parameters for the memory copy
			*
			* \return
			* ::CUDA_SUCCESS,
			* ::CUDA_ERROR_DEINITIALIZED,
			* ::CUDA_ERROR_NOT_INITIALIZED,
			* ::CUDA_ERROR_INVALID_CONTEXT,
			* ::CUDA_ERROR_INVALID_VALUE
			* \notefnerr
			*
			* \sa ::cuMemcpyDtoD, ::cuMemcpyPeer, ::cuMemcpyDtoDAsync, ::cuMemcpyPeerA
			sync,
			* ::cuMemcpy3DPeerAsync
			*/
			CUresult CUDAAPI cuMemcpy3DPeer(const CUDA_MEMCPY3D_PEER *pCopy);
			/**
			* \brief Copies memory asynchronously
			*
			* Copies data between two pointers.
			* \p dst and \p src are base pointers of the destination and source, respe
			ctively.
			* \p ByteCount specifies the number of bytes to copy.
			* Note that this function infers the type of the transfer (host to host, h
			ost to
			* device, device to device, or device to host) from the pointer values.
			This
			* function is only allowed in contexts which support unified addressing.
			* Note that this function is asynchronous and can optionally be associated
			to
			* a stream by passing a non-zero \p hStream argument
			*
			* \param dst - Destination unified virtual address space pointer
			* \param src - Source unified virtual address space pointer
			* \param ByteCount - Size of memory copy in bytes
			* \param hStream - Stream identifier
			*
			* \return
			* ::CUDA_SUCCESS,
			* ::CUDA_ERROR_DEINITIALIZED,
			* ::CUDA_ERROR_NOT_INITIALIZED,
			* ::CUDA_ERROR_INVALID_CONTEXT,
			* ::CUDA_ERROR_INVALID_VALUE
			* \notefnerr
			*
			* \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
			* ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost
			,
			* ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligne
			d,
			* ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
			* ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD,
			* ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync
			,
			* ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
			* ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
			* ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async,
			* ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32A
			sync,
			* ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async,
			* ::cuMemsetD32, ::cuMemsetD32Async
			*/
			CUresult CUDAAPI cuMemcpyAsync(CUdeviceptr dst, CUdeviceptr src, size_t Byt
			eCount, CUstream hStream);
			/**
			* \brief Copies device memory between two contexts asynchronously.
			*
			* Copies from device memory in one context to device memory in another
			* context. \p dstDevice is the base device pointer of the destination memo
			ry
			* and \p dstContext is the destination context. \p srcDevice is the base
			* device pointer of the source memory and \p srcContext is the source poin
			ter.
			* \p ByteCount specifies the number of bytes to copy. Note that this func
			tion
			* is asynchronous with respect to the host and all work in other streams i
			n
			* other devices.
			*
			* \param dstDevice - Destination device pointer
			* \param dstContext - Destination context
			* \param srcDevice - Source device pointer
			* \param srcContext - Source context
			* \param ByteCount - Size of memory copy in bytes
			* \param hStream - Stream identifier
			*
			* \return
			* ::CUDA_SUCCESS,
			* ::CUDA_ERROR_DEINITIALIZED,
			* ::CUDA_ERROR_NOT_INITIALIZED,
			* ::CUDA_ERROR_INVALID_CONTEXT,
			* ::CUDA_ERROR_INVALID_VALUE
			* \notefnerr
			*
			* \sa ::cuMemcpyDtoD, ::cuMemcpyPeer, ::cuMemcpy3DPeer, ::cuMemcpyDtoDAsyn
			c,
			* ::cuMemcpy3DPeerAsync
			*/
			CUresult CUDAAPI cuMemcpyPeerAsync(CUdeviceptr dstDevice, CUcontext dstCont
			ext, CUdeviceptr srcDevice, CUcontext srcContext, size_t ByteCount, CUstrea
			m hStream);
			#endif /* __CUDA_API_VERSION >= 4000 */
			#if __CUDA_API_VERSION >= 3020
	/**		/**
	* \brief Copies memory from Host to Device		* \brief Copies memory from Host to Device
	*		*
	* Copies from host memory to device memory. \p dstDevice and \p srcHost ar e		* Copies from host memory to device memory. \p dstDevice and \p srcHost ar e
	* the base addresses of the destination and source, respectively. \p ByteC ount		* the base addresses of the destination and source, respectively. \p ByteC ount
	* specifies the number of bytes to copy.		* specifies the number of bytes to copy.
	*		*
	* ::cuMemcpyHtoDAsync() is asynchronous and can optionally be associated t o a		* ::cuMemcpyHtoDAsync() is asynchronous and can optionally be associated t o a
	* stream by passing a non-zero \p hStream argument. It only works on		* stream by passing a non-zero \p hStream argument. It only works on
	* page-locked memory and returns an error if a pointer to pageable memory is		* page-locked memory and returns an error if a pointer to pageable memory is
	skipping to change at line 3497		skipping to change at line 4082
	} CUDA_MEMCPY2D;		} CUDA_MEMCPY2D;
	* \endcode		* \endcode
	* where:		* where:
	* - ::srcMemoryType and ::dstMemoryType specify the type of memory of the		* - ::srcMemoryType and ::dstMemoryType specify the type of memory of the
	* source and destination, respectively; ::CUmemorytype_enum is defined a s:		* source and destination, respectively; ::CUmemorytype_enum is defined a s:
	*		*
	* \code		* \code
	typedef enum CUmemorytype_enum {		typedef enum CUmemorytype_enum {
	CU_MEMORYTYPE_HOST = 0x01,		CU_MEMORYTYPE_HOST = 0x01,
	CU_MEMORYTYPE_DEVICE = 0x02,		CU_MEMORYTYPE_DEVICE = 0x02,
	CU_MEMORYTYPE_ARRAY = 0x03		CU_MEMORYTYPE_ARRAY = 0x03,
			CU_MEMORYTYPE_UNIFIED = 0x04
	} CUmemorytype;		} CUmemorytype;
	* \endcode		* \endcode
	*		*
	* \par		* \par
	* If ::srcMemoryType is ::CU_MEMORYTYPE_HOST, ::srcHost and ::srcPitch		* If ::srcMemoryType is ::CU_MEMORYTYPE_HOST, ::srcHost and ::srcPitch
	* specify the (host) base address of the source data and the bytes per row to		* specify the (host) base address of the source data and the bytes per row to
	* apply. ::srcArray is ignored.		* apply. ::srcArray is ignored.
	*		*
	* \par		* \par
			* If ::srcMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::srcDevice and ::srcPitc
			h
			* specify the (unified virtual address space) base address of the source
			data
			* and the bytes per row to apply. ::srcArray is ignored.
			* This value may be used only if unified addressing is supported in the ca
			lling
			* context.
			*
			* \par
	* If ::srcMemoryType is ::CU_MEMORYTYPE_DEVICE, ::srcDevice and ::srcPitch		* If ::srcMemoryType is ::CU_MEMORYTYPE_DEVICE, ::srcDevice and ::srcPitch
	* specify the (device) base address of the source data and the bytes per r ow		* specify the (device) base address of the source data and the bytes per r ow
	* to apply. ::srcArray is ignored.		* to apply. ::srcArray is ignored.
	*		*
	* \par		* \par
	* If ::srcMemoryType is ::CU_MEMORYTYPE_ARRAY, ::srcArray specifies the		* If ::srcMemoryType is ::CU_MEMORYTYPE_ARRAY, ::srcArray specifies the
	* handle of the source data. ::srcHost, ::srcDevice and ::srcPitch are		* handle of the source data. ::srcHost, ::srcDevice and ::srcPitch are
	* ignored.		* ignored.
	*		*
	* \par		* \par
			* If ::dstMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::dstDevice and ::dstPitc
			h
			* specify the (unified virtual address space) base address of the source
			data
			* and the bytes per row to apply. ::dstArray is ignored.
			* This value may be used only if unified addressing is supported in the ca
			lling
			* context.
			*
			* \par
	* If ::dstMemoryType is ::CU_MEMORYTYPE_HOST, ::dstHost and ::dstPitch		* If ::dstMemoryType is ::CU_MEMORYTYPE_HOST, ::dstHost and ::dstPitch
	* specify the (host) base address of the destination data and the bytes pe r		* specify the (host) base address of the destination data and the bytes pe r
	* row to apply. ::dstArray is ignored.		* row to apply. ::dstArray is ignored.
	*		*
	* \par		* \par
	* If ::dstMemoryType is ::CU_MEMORYTYPE_DEVICE, ::dstDevice and ::dstPitch		* If ::dstMemoryType is ::CU_MEMORYTYPE_DEVICE, ::dstDevice and ::dstPitch
	* specify the (device) base address of the destination data and the bytes per		* specify the (device) base address of the destination data and the bytes per
	* row to apply. ::dstArray is ignored.		* row to apply. ::dstArray is ignored.
	*		*
	* \par		* \par
	skipping to change at line 3584		skipping to change at line 4184
	* - If specified, ::srcPitch must be greater than or equal to ::WidthInByt es +		* - If specified, ::srcPitch must be greater than or equal to ::WidthInByt es +
	* ::srcXInBytes, and ::dstPitch must be greater than or equal to		* ::srcXInBytes, and ::dstPitch must be greater than or equal to
	* ::WidthInBytes + dstXInBytes.		* ::WidthInBytes + dstXInBytes.
	* - If specified, ::srcHeight must be greater than or equal to ::Height +		* - If specified, ::srcHeight must be greater than or equal to ::Height +
	* ::srcY, and ::dstHeight must be greater than or equal to ::Height + :: dstY.		* ::srcY, and ::dstHeight must be greater than or equal to ::Height + :: dstY.
	*		*
	* \par		* \par
	* ::cuMemcpy2D() returns an error if any pitch is greater than the maximum		* ::cuMemcpy2D() returns an error if any pitch is greater than the maximum
	* allowed (::CU_DEVICE_ATTRIBUTE_MAX_PITCH). ::cuMemAllocPitch() passes ba ck		* allowed (::CU_DEVICE_ATTRIBUTE_MAX_PITCH). ::cuMemAllocPitch() passes ba ck
	* pitches that always work with ::cuMemcpy2D(). On intra-device memory cop ies		* pitches that always work with ::cuMemcpy2D(). On intra-device memory cop ies
	* (device ? device, CUDA array ? device, CUDA array ? CUDA array),		* (device to device, CUDA array to device, CUDA array to CUDA array),
	* ::cuMemcpy2D() may fail for pitches not computed by ::cuMemAllocPitch().		* ::cuMemcpy2D() may fail for pitches not computed by ::cuMemAllocPitch().
	* ::cuMemcpy2DUnaligned() does not have this restriction, but may run		* ::cuMemcpy2DUnaligned() does not have this restriction, but may run
	* significantly slower in the cases where ::cuMemcpy2D() would have return ed		* significantly slower in the cases where ::cuMemcpy2D() would have return ed
	* an error code.		* an error code.
	*		*
	* ::cuMemcpy2DAsync() is asynchronous and can optionally be associated to a		* ::cuMemcpy2DAsync() is asynchronous and can optionally be associated to a
	* stream by passing a non-zero \p hStream argument. It only works on		* stream by passing a non-zero \p hStream argument. It only works on
	* page-locked host memory and returns an error if a pointer to pageable		* page-locked host memory and returns an error if a pointer to pageable
	* memory is passed as input.		* memory is passed as input.
	*		*
	skipping to change at line 3661		skipping to change at line 4261
	} CUDA_MEMCPY3D;		} CUDA_MEMCPY3D;
	* \endcode		* \endcode
	* where:		* where:
	* - ::srcMemoryType and ::dstMemoryType specify the type of memory of the		* - ::srcMemoryType and ::dstMemoryType specify the type of memory of the
	* source and destination, respectively; ::CUmemorytype_enum is defined a s:		* source and destination, respectively; ::CUmemorytype_enum is defined a s:
	*		*
	* \code		* \code
	typedef enum CUmemorytype_enum {		typedef enum CUmemorytype_enum {
	CU_MEMORYTYPE_HOST = 0x01,		CU_MEMORYTYPE_HOST = 0x01,
	CU_MEMORYTYPE_DEVICE = 0x02,		CU_MEMORYTYPE_DEVICE = 0x02,
	CU_MEMORYTYPE_ARRAY = 0x03		CU_MEMORYTYPE_ARRAY = 0x03,
			CU_MEMORYTYPE_UNIFIED = 0x04
	} CUmemorytype;		} CUmemorytype;
	* \endcode		* \endcode
	*		*
	* \par		* \par
			* If ::srcMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::srcDevice and ::srcPitc
			h
			* specify the (unified virtual address space) base address of the source
			data
			* and the bytes per row to apply. ::srcArray is ignored.
			* This value may be used only if unified addressing is supported in the ca
			lling
			* context.
			*
			* \par
	* If ::srcMemoryType is ::CU_MEMORYTYPE_HOST, ::srcHost, ::srcPitch and		* If ::srcMemoryType is ::CU_MEMORYTYPE_HOST, ::srcHost, ::srcPitch and
	* ::srcHeight specify the (host) base address of the source data, the byte s		* ::srcHeight specify the (host) base address of the source data, the byte s
	* per row, and the height of each 2D slice of the 3D array. ::srcArray is		* per row, and the height of each 2D slice of the 3D array. ::srcArray is
	* ignored.		* ignored.
	*		*
	* \par		* \par
	* If ::srcMemoryType is ::CU_MEMORYTYPE_DEVICE, ::srcDevice, ::srcPitch an d		* If ::srcMemoryType is ::CU_MEMORYTYPE_DEVICE, ::srcDevice, ::srcPitch an d
	* ::srcHeight specify the (device) base address of the source data, the by tes		* ::srcHeight specify the (device) base address of the source data, the by tes
	* per row, and the height of each 2D slice of the 3D array. ::srcArray is		* per row, and the height of each 2D slice of the 3D array. ::srcArray is
	* ignored.		* ignored.
	*		*
	* \par		* \par
	* If ::srcMemoryType is ::CU_MEMORYTYPE_ARRAY, ::srcArray specifies the		* If ::srcMemoryType is ::CU_MEMORYTYPE_ARRAY, ::srcArray specifies the
	* handle of the source data. ::srcHost, ::srcDevice, ::srcPitch and		* handle of the source data. ::srcHost, ::srcDevice, ::srcPitch and
	* ::srcHeight are ignored.		* ::srcHeight are ignored.
	*		*
	* \par		* \par
			* If ::dstMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::dstDevice and ::dstPitc
			h
			* specify the (unified virtual address space) base address of the source
			data
			* and the bytes per row to apply. ::dstArray is ignored.
			* This value may be used only if unified addressing is supported in the ca
			lling
			* context.
			*
			* \par
	* If ::dstMemoryType is ::CU_MEMORYTYPE_HOST, ::dstHost and ::dstPitch		* If ::dstMemoryType is ::CU_MEMORYTYPE_HOST, ::dstHost and ::dstPitch
	* specify the (host) base address of the destination data, the bytes per r ow,		* specify the (host) base address of the destination data, the bytes per r ow,
	* and the height of each 2D slice of the 3D array. ::dstArray is ignored.		* and the height of each 2D slice of the 3D array. ::dstArray is ignored.
	*		*
	* \par		* \par
	* If ::dstMemoryType is ::CU_MEMORYTYPE_DEVICE, ::dstDevice and ::dstPitch		* If ::dstMemoryType is ::CU_MEMORYTYPE_DEVICE, ::dstDevice and ::dstPitch
	* specify the (device) base address of the destination data, the bytes per		* specify the (device) base address of the destination data, the bytes per
	* row, and the height of each 2D slice of the 3D array. ::dstArray is igno red.		* row, and the height of each 2D slice of the 3D array. ::dstArray is igno red.
	*		*
	* \par		* \par
	skipping to change at line 3780		skipping to change at line 4395
	* ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::c uMemcpyDtoDAsync,		* ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::c uMemcpyDtoDAsync,
	* ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync ,		* ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync ,
	* ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,		* ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
	* ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,		* ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
	* ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async,		* ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async,
	* ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32A sync,		* ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32A sync,
	* ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async,		* ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async,
	* ::cuMemsetD32, ::cuMemsetD32Async		* ::cuMemsetD32, ::cuMemsetD32Async
	*/		*/
	CUresult CUDAAPI cuMemcpy3DAsync(const CUDA_MEMCPY3D *pCopy, CUstream hStre am);		CUresult CUDAAPI cuMemcpy3DAsync(const CUDA_MEMCPY3D *pCopy, CUstream hStre am);
			#endif /* __CUDA_API_VERSION >= 3020 */
			#if __CUDA_API_VERSION >= 4000
			/**
			* \brief Copies memory between contexts asynchronously.
			*
			* Perform a 3D memory copy according to the parameters specified in
			* \p pCopy. See the definition of the ::CUDA_MEMCPY3D_PEER structure
			* for documentation of its parameters.
			*
			* \param pCopy - Parameters for the memory copy
			* \param hStream - Stream identifier
			*
			* \return
			* ::CUDA_SUCCESS,
			* ::CUDA_ERROR_DEINITIALIZED,
			* ::CUDA_ERROR_NOT_INITIALIZED,
			* ::CUDA_ERROR_INVALID_CONTEXT,
			* ::CUDA_ERROR_INVALID_VALUE
			* \notefnerr
			*
			* \sa ::cuMemcpyDtoD, ::cuMemcpyPeer, ::cuMemcpyDtoDAsync, ::cuMemcpyPeerA
			sync,
			* ::cuMemcpy3DPeerAsync
			*/
			CUresult CUDAAPI cuMemcpy3DPeerAsync(const CUDA_MEMCPY3D_PEER *pCopy, CUstr
			eam hStream);
			#endif /* __CUDA_API_VERSION >= 4000 */
			#if __CUDA_API_VERSION >= 3020
	/**		/**
	* \brief Initializes device memory		* \brief Initializes device memory
	*		*
	* Sets the memory range of \p N 8-bit values to the specified value		* Sets the memory range of \p N 8-bit values to the specified value
	* \p uc.		* \p uc.
	*		*
	* \param dstDevice - Destination device pointer		* \param dstDevice - Destination device pointer
	* \param uc - Value to set		* \param uc - Value to set
	* \param N - Number of elements		* \param N - Number of elements
	*		*
	skipping to change at line 4419		skipping to change at line 5061
	CUarray_format Format;		CUarray_format Format;
	unsigned int NumChannels;		unsigned int NumChannels;
	unsigned int Flags;		unsigned int Flags;
	} CUDA_ARRAY3D_DESCRIPTOR;		} CUDA_ARRAY3D_DESCRIPTOR;
	* \endcode		* \endcode
	* where:		* where:
	*		*
	* - \p Width, \p Height, and \p Depth are the width, height, and depth of the		* - \p Width, \p Height, and \p Depth are the width, height, and depth of the
	* CUDA array (in elements); the CUDA array is one-dimensional if height an d		* CUDA array (in elements); the CUDA array is one-dimensional if height an d
	* depth are 0, two-dimensional if depth is 0, and three-dimensional otherw ise;		* depth are 0, two-dimensional if depth is 0, and three-dimensional otherw ise;
			* If the ::CUDA_ARRAY3D_LAYERED flag is set, then the CUDA array is a coll
			ection of
			* layers, where \p Depth indicates the number of layers. Each layer is a 1
			D array
			* if \p Height is 0, and a 2D array otherwise.
	* - ::Format specifies the format of the elements; ::CUarray_format is		* - ::Format specifies the format of the elements; ::CUarray_format is
	* defined as:		* defined as:
	* \code		* \code
	typedef enum CUarray_format_enum {		typedef enum CUarray_format_enum {
	CU_AD_FORMAT_UNSIGNED_INT8 = 0x01,		CU_AD_FORMAT_UNSIGNED_INT8 = 0x01,
	CU_AD_FORMAT_UNSIGNED_INT16 = 0x02,		CU_AD_FORMAT_UNSIGNED_INT16 = 0x02,
	CU_AD_FORMAT_UNSIGNED_INT32 = 0x03,		CU_AD_FORMAT_UNSIGNED_INT32 = 0x03,
	CU_AD_FORMAT_SIGNED_INT8 = 0x08,		CU_AD_FORMAT_SIGNED_INT8 = 0x08,
	CU_AD_FORMAT_SIGNED_INT16 = 0x09,		CU_AD_FORMAT_SIGNED_INT16 = 0x09,
	CU_AD_FORMAT_SIGNED_INT32 = 0x0a,		CU_AD_FORMAT_SIGNED_INT32 = 0x0a,
	CU_AD_FORMAT_HALF = 0x10,		CU_AD_FORMAT_HALF = 0x10,
	CU_AD_FORMAT_FLOAT = 0x20		CU_AD_FORMAT_FLOAT = 0x20
	} CUarray_format;		} CUarray_format;
	* \endcode		* \endcode
	* - \p NumChannels specifies the number of packed components per CUDA arra y		* - \p NumChannels specifies the number of packed components per CUDA arra y
	* element; it may be 1, 2, or 4;		* element; it may be 1, 2, or 4;
	* - ::Flags may be set to ::CUDA_ARRAY3D_SURFACE_LDST to enable surface re		* - ::Flags may be set to
	ferences		* - ::CUDA_ARRAY3D_LAYERED to enable creation of layered CUDA arrays. If
	* to be bound to the CUDA array. If this flag is not set, ::cuSurfRefSetA		this flag is set,
	rray		* \p Depth specifies the number of layers, not the depth of a 3D array
	* will fail when attempting to bind the CUDA array to a surface reference.		.
			* - ::CUDA_ARRAY3D_SURFACE_LDST to enable surface references to be bound
			to the CUDA array.
			* If this flag is not set, ::cuSurfRefSetArray will fail when attempti
			ng to bind the CUDA array
			* to a surface reference.
	*		*
	* Here are examples of CUDA array descriptions:		* Here are examples of CUDA array descriptions:
	*		*
	* Description for a CUDA array of 2048 floats:		* Description for a CUDA array of 2048 floats:
	* \code		* \code
	CUDA_ARRAY3D_DESCRIPTOR desc;		CUDA_ARRAY3D_DESCRIPTOR desc;
	desc.Format = CU_AD_FORMAT_FLOAT;		desc.Format = CU_AD_FORMAT_FLOAT;
	desc.NumChannels = 1;		desc.NumChannels = 1;
	desc.Width = 2048;		desc.Width = 2048;
	desc.Height = 0;		desc.Height = 0;
	skipping to change at line 4538		skipping to change at line 5186
	* ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,		* ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
	* ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,		* ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,
	* ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32		* ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32
	*/		*/
	CUresult CUDAAPI cuArray3DGetDescriptor(CUDA_ARRAY3D_DESCRIPTOR *pArrayDesc riptor, CUarray hArray);		CUresult CUDAAPI cuArray3DGetDescriptor(CUDA_ARRAY3D_DESCRIPTOR *pArrayDesc riptor, CUarray hArray);
	#endif /* __CUDA_API_VERSION >= 3020 */		#endif /* __CUDA_API_VERSION >= 3020 */
	/** @} */ /* END CUDA_MEM */		/** @} */ /* END CUDA_MEM */
	/**		/**
			* \defgroup CUDA_UNIFIED Unified Addressing
			*
			* This section describes the unified addressing functions of the
			* low-level CUDA driver application programming interface.
			*
			* @{
			*
			* \section CUDA_UNIFIED_overview Overview
			*
			* CUDA devices can share a unified address space with the host.
			* For these devices there is no distinction between a device
			* pointer and a host pointer -- the same pointer value may be
			* used to access memory from the host program and from a kernel
			* running on the device (with exceptions enumerated below).
			*
			* \section CUDA_UNIFIED_support Supported Platforms
			*
			* Whether or not a device supports unified addressing may be
			* queried by calling ::cuDeviceGetAttribute() with the device
			* attribute ::CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING.
			*
			* Unified addressing is automatically enabled in 64-bit processes
			* on devices with compute capability greater than or equal to 2.0.
			*
			* Unified addressing is not yet supported on Windows Vista or
			* Windows 7 for devices that do not use the TCC driver model.
			*
			* \section CUDA_UNIFIED_lookup Looking Up Information from Pointer Values
			*
			* It is possible to look up information about the memory which backs a
			* pointer value. For instance, one may want to know if a pointer points
			* to host or device memory. As another example, in the case of device
			* memory, one may want to know on which CUDA device the memory
			* resides. These properties may be queried using the function
			* ::cuPointerGetAttribute()
			*
			* Because pointers are unique, it is not necessary to specify information
			* about the pointers specified to the various copy functions in the
			* CUDA API. The function ::cuMemcpy() may be used to perform a copy
			* between two pointers, ignoring whether they point to host or device
			* memory (making ::cuMemcpyHtoD(), ::cuMemcpyDtoD(), and ::cuMemcpyDtoH()
			* unnecessary for devices supporting unified addressing). For
			* multidimensional copies, the memory type ::CU_MEMORYTYPE_UNIFIED may be
			* used to specify that the CUDA driver should infer the location of the
			* pointer from its value.
			*
			* \section CUDA_UNIFIED_automaphost Automatic Mapping of Host Allocated Ho
			st Memory
			*
			* All host memory allocated in all contexts using ::cuMemAllocHost() and
			* ::cuMemHostAlloc() is always directly accessible from all contexts on
			* all devices that support unified addressing. This is the case regardles
			s
			* of whether or not the flags ::CU_MEMHOSTALLOC_PORTABLE and
			* ::CU_MEMHOSTALLOC_DEVICEMAP are specified.
			*
			* The pointer value through which allocated host memory may be accessed
			* in kernels on all devices that support unified addressing is the same
			* as the pointer value through which that memory is accessed on the host,
			* so it is not necessary to call ::cuMemHostGetDevicePointer() to get the
			device
			* pointer for these allocations.
			*
			* Note that this is not the case for memory allocated using the flag
			* ::CU_MEMHOSTALLOC_WRITECOMBINED, as discussed below.
			*
			* \section CUDA_UNIFIED_autopeerregister Automatic Registration of Peer Me
			mory
			*
			* Upon enabling direct access from a context that supports unified address
			ing
			* to another peer context that supports unified addressing using
			* ::cuCtxEnablePeerAccess() all memory allocated in the peer context using
			* ::cuMemAlloc() and ::cuMemAllocPitch() will immediately be accessible
			* by the current context. The device pointer value through
			* which any peer memory may be accessed in the current context
			* is the same pointer value through which that memory may be
			* accessed in the peer context.
			*
			* \section CUDA_UNIFIED_exceptions Exceptions, Disjoint Addressing
			*
			* Not all memory may be accessed on devices through the same pointer
			* value through which they are accessed on the host. These exceptions
			* are host memory registered using ::cuMemHostRegister() and host memory
			* allocated using the flag ::CU_MEMHOSTALLOC_WRITECOMBINED. For these
			* exceptions, there exists a distinct host and device address for the
			* memory. The device address is guaranteed to not overlap any valid host
			* pointer range and is guaranteed to have the same value across all
			* contexts that support unified addressing.
			*
			* This device address may be queried using ::cuMemHostGetDevicePointer()
			* when a context using unified addressing is current. Either the host
			* or the unified device pointer value may be used to refer to this memory
			* through ::cuMemcpy() and similar functions using the
			* ::CU_MEMORYTYPE_UNIFIED memory type.
			*
			*/
			#if __CUDA_API_VERSION >= 4000
			/**
			* \brief Returns information about a pointer
			*
			* The supported attributes are:
			*
			* - ::CU_POINTER_ATTRIBUTE_CONTEXT:
			*
			* Returns in \p *data the ::CUcontext in which \p ptr was allocated o
			r
			* registered.
			* The type of \p data must be ::CUcontext *.
			*
			* If \p ptr was not allocated by, mapped by, or registered with
			* a ::CUcontext which uses unified virtual addressing then
			* ::CUDA_ERROR_INVALID_VALUE is returned.
			*
			* - ::CU_POINTER_ATTRIBUTE_MEMORY_TYPE:
			*
			* Returns in \p *data the physical memory type of the memory that
			* \p ptr addresses as a ::CUmemorytype enumerated value.
			* The type of \p data must be unsigned int.
			*
			* If \p ptr addresses device memory then \p *data is set to
			* ::CU_MEMORYTYPE_DEVICE. The particular ::CUdevice on which the
			* memory resides is the ::CUdevice of the ::CUcontext returned by the
			* ::CU_POINTER_ATTRIBUTE_CONTEXT attribute of \p ptr.
			*
			* If \p ptr addresses host memory then \p *data is set to
			* ::CU_MEMORYTYPE_HOST.
			*
			* If \p ptr was not allocated by, mapped by, or registered with
			* a ::CUcontext which uses unified virtual addressing then
			* ::CUDA_ERROR_INVALID_VALUE is returned.
			*
			* If the current ::CUcontext does not support unified virtual
			* addressing then ::CUDA_ERROR_INVALID_CONTEXT is returned.
			*
			* - ::CU_POINTER_ATTRIBUTE_DEVICE_POINTER:
			*
			* Returns in \p *data the device pointer value through which
			* \p ptr may be accessed by kernels running in the current
			* ::CUcontext.
			* The type of \p data must be CUdeviceptr *.
			*
			* If there exists no device pointer value through which
			* kernels running in the current ::CUcontext may access
			* \p ptr then ::CUDA_ERROR_INVALID_VALUE is returned.
			*
			* If there is no current ::CUcontext then
			* ::CUDA_ERROR_INVALID_CONTEXT is returned.
			*
			* Except in the exceptional disjoint addressing cases discussed
			* below, the value returned in \p *data will equal the input
			* value \p ptr.
			*
			* - ::CU_POINTER_ATTRIBUTE_HOST_POINTER:
			*
			* Returns in \p *data the host pointer value through which
			* \p ptr may be accessed by by the host program.
			* The type of \p data must be void **.
			* If there exists no host pointer value through which
			* the host program may directly access \p ptr then
			* ::CUDA_ERROR_INVALID_VALUE is returned.
			*
			* Except in the exceptional disjoint addressing cases discussed
			* below, the value returned in \p *data will equal the input
			* value \p ptr.
			*
			*
			* \par
			*
			* Note that for most allocations in the unified virtual address space
			* the host and device pointer for accessing the allocation will be the
			* same. The exceptions to this are
			* - user memory registered using ::cuMemHostRegister
			* - host memory allocated using ::cuMemHostAlloc with the
			* ::CU_MEMHOSTALLOC_WRITECOMBINED flag
			* For these types of allocation there will exist separate, disjoint host
			* and device addresses for accessing the allocation. In particular
			* - The host address will correspond to an invalid unmapped device addres
			s
			* (which will result in an exception if accessed from the device)
			* - The device address will correspond to an invalid unmapped host addres
			s
			* (which will result in an exception if accessed from the host).
			* For these types of allocations, querying ::CU_POINTER_ATTRIBUTE_HOST_POI
			NTER
			* and ::CU_POINTER_ATTRIBUTE_DEVICE_POINTER may be used to retrieve the ho
			st
			* and device addresses from either address.
			*
			* \param data - Returned pointer attribute value
			* \param attribute - Pointer attribute to query
			* \param ptr - Pointer
			*
			* \return
			* ::CUDA_SUCCESS,
			* ::CUDA_ERROR_DEINITIALIZED,
			* ::CUDA_ERROR_NOT_INITIALIZED,
			* ::CUDA_ERROR_INVALID_CONTEXT,
			* ::CUDA_ERROR_INVALID_VALUE,
			* ::CUDA_ERROR_INVALID_DEVICE
			* \notefnerr
			*
			* \sa ::cuMemAlloc,
			* ::cuMemFree,
			* ::cuMemAllocHost,
			* ::cuMemFreeHost,
			* ::cuMemHostAlloc,
			* ::cuMemHostRegister,
			* ::cuMemHostUnregister
			*/
			CUresult CUDAAPI cuPointerGetAttribute(void *data, CUpointer_attribute attr
			ibute, CUdeviceptr ptr);
			#endif /* __CUDA_API_VERSION >= 4000 */
			/** @} */ /* END CUDA_UNIFIED */
			/**
	* \defgroup CUDA_STREAM Stream Management		* \defgroup CUDA_STREAM Stream Management
	*		*
	* This section describes the stream management functions of the low-level CUDA		* This section describes the stream management functions of the low-level CUDA
	* driver application programming interface.		* driver application programming interface.
	*		*
	* @{		* @{
	*/		*/
	/**		/**
	* \brief Create a stream		* \brief Create a stream
	skipping to change at line 4576		skipping to change at line 5431
	* ::cuStreamQuery,		* ::cuStreamQuery,
	* ::cuStreamSynchronize		* ::cuStreamSynchronize
	*/		*/
	CUresult CUDAAPI cuStreamCreate(CUstream *phStream, unsigned int Flags);		CUresult CUDAAPI cuStreamCreate(CUstream *phStream, unsigned int Flags);
	/**		/**
	* \brief Make a compute stream wait on an event		* \brief Make a compute stream wait on an event
	*		*
	* Makes all future work submitted to \p hStream wait until \p hEvent		* Makes all future work submitted to \p hStream wait until \p hEvent
	* reports completion before beginning execution. This synchronization		* reports completion before beginning execution. This synchronization
	* will be performed efficiently on the device.		* will be performed efficiently on the device. The event \p hEvent may
			* be from a different context than \p hStream, in which case this function
			* will perform cross-device synchronization.
	*		*
	* The stream \p hStream will wait only for the completion of the most rece nt		* The stream \p hStream will wait only for the completion of the most rece nt
	* host call to ::cuEventRecord() on \p hEvent. Once this call has returne d,		* host call to ::cuEventRecord() on \p hEvent. Once this call has returne d,
	* any functions (including ::cuEventRecord() and ::cuEventDestroy()) may b e		* any functions (including ::cuEventRecord() and ::cuEventDestroy()) may b e
	* called on \p hEvent again, and the subsequent calls will not have any		* called on \p hEvent again, and the subsequent calls will not have any
	* effect on \p hStream.		* effect on \p hStream.
	*		*
	* If \p hStream is 0 (the NULL stream) any future work submitted in any st ream		* If \p hStream is 0 (the NULL stream) any future work submitted in any st ream
	* will wait for \p hEvent to complete before beginning execution. This		* will wait for \p hEvent to complete before beginning execution. This
	* effectively creates a barrier for all future work submitted to the conte xt.		* effectively creates a barrier for all future work submitted to the conte xt.
	skipping to change at line 4639		skipping to change at line 5496
	* ::cuStreamWaitEvent,		* ::cuStreamWaitEvent,
	* ::cuStreamDestroy,		* ::cuStreamDestroy,
	* ::cuStreamSynchronize		* ::cuStreamSynchronize
	*/		*/
	CUresult CUDAAPI cuStreamQuery(CUstream hStream);		CUresult CUDAAPI cuStreamQuery(CUstream hStream);
	/**		/**
	* \brief Wait until a stream's tasks are completed		* \brief Wait until a stream's tasks are completed
	*		*
	* Waits until the device has completed all operations in the stream specif ied		* Waits until the device has completed all operations in the stream specif ied
	* by \p hStream. If the context was created with the ::CU_CTX_BLOCKING_SYN		* by \p hStream. If the context was created with the
	C		* ::CU_CTX_SCHED_BLOCKING_SYNC flag, the CPU thread will block until the
	* flag, the CPU thread will block until the stream is finished with all of		* stream is finished with all of its tasks.
	* its tasks.
	*		*
	* \param hStream - Stream to wait for		* \param hStream - Stream to wait for
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,		* ::CUDA_ERROR_NOT_INITIALIZED,
	* ::CUDA_ERROR_INVALID_CONTEXT,		* ::CUDA_ERROR_INVALID_CONTEXT,
	* ::CUDA_ERROR_INVALID_HANDLE		* ::CUDA_ERROR_INVALID_HANDLE
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cuStreamCreate,		* \sa ::cuStreamCreate,
	* ::cuStreamDestroy,		* ::cuStreamDestroy,
	* ::cuStreamWaitEvent,		* ::cuStreamWaitEvent,
	* ::cuStreamQuery		* ::cuStreamQuery
	*/		*/
	CUresult CUDAAPI cuStreamSynchronize(CUstream hStream);		CUresult CUDAAPI cuStreamSynchronize(CUstream hStream);
			#if __CUDA_API_VERSION >= 4000
	/**		/**
	* \brief Destroys a stream		* \brief Destroys a stream
	*		*
	* Destroys the stream specified by \p hStream.		* Destroys the stream specified by \p hStream.
	*		*
			* In the case that the device is still doing work in the stream \p hStream
			* when ::cuStreamDestroy() is called, the function will return immediately
			* and the resources associated with \p hStream will be released automatica
			lly
			* once the device has completed all work in \p hStream.
			*
	* \param hStream - Stream to destroy		* \param hStream - Stream to destroy
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,		* ::CUDA_ERROR_NOT_INITIALIZED,
	* ::CUDA_ERROR_INVALID_CONTEXT,		* ::CUDA_ERROR_INVALID_CONTEXT,
	* ::CUDA_ERROR_INVALID_VALUE		* ::CUDA_ERROR_INVALID_VALUE
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cuStreamCreate,		* \sa ::cuStreamCreate,
	* ::cuStreamWaitEvent,		* ::cuStreamWaitEvent,
	* ::cuStreamQuery,		* ::cuStreamQuery,
	* ::cuStreamSynchronize		* ::cuStreamSynchronize
	*/		*/
	CUresult CUDAAPI cuStreamDestroy(CUstream hStream);		CUresult CUDAAPI cuStreamDestroy(CUstream hStream);
			#endif /* __CUDA_API_VERSION >= 4000 */
	/** @} */ /* END CUDA_STREAM */		/** @} */ /* END CUDA_STREAM */
	/**		/**
	* \defgroup CUDA_EVENT Event Management		* \defgroup CUDA_EVENT Event Management
	*		*
	* This section describes the event management functions of the low-level C UDA		* This section describes the event management functions of the low-level C UDA
	* driver application programming interface.		* driver application programming interface.
	*		*
	* @{		* @{
	skipping to change at line 4744		skipping to change at line 5608
	* recorded after all preceding operations in the CUDA context have been		* recorded after all preceding operations in the CUDA context have been
	* completed. Since operation is asynchronous, ::cuEventQuery and/or		* completed. Since operation is asynchronous, ::cuEventQuery and/or
	* ::cuEventSynchronize() must be used to determine when the event has actu ally		* ::cuEventSynchronize() must be used to determine when the event has actu ally
	* been recorded.		* been recorded.
	*		*
	* If ::cuEventRecord() has previously been called on \p hEvent, then this		* If ::cuEventRecord() has previously been called on \p hEvent, then this
	* call will overwrite any existing state in \p hEvent. Any subsequent cal ls		* call will overwrite any existing state in \p hEvent. Any subsequent cal ls
	* which examine the status of \p hEvent will only examine the completion o f		* which examine the status of \p hEvent will only examine the completion o f
	* this most recent call to ::cuEventRecord().		* this most recent call to ::cuEventRecord().
	*		*
			* It is necessary that \p hEvent and \p hStream be created on the same con
			text.
			*
	* \param hEvent - Event to record		* \param hEvent - Event to record
	* \param hStream - Stream to record event for		* \param hStream - Stream to record event for
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,		* ::CUDA_ERROR_NOT_INITIALIZED,
	* ::CUDA_ERROR_INVALID_CONTEXT,		* ::CUDA_ERROR_INVALID_CONTEXT,
	* ::CUDA_ERROR_INVALID_HANDLE,		* ::CUDA_ERROR_INVALID_HANDLE,
	* ::CUDA_ERROR_INVALID_VALUE		* ::CUDA_ERROR_INVALID_VALUE
	skipping to change at line 4783		skipping to change at line 5649
	* ::cuEventRecord() has not been called on \p hEvent, then ::CUDA_SUCCESS is		* ::cuEventRecord() has not been called on \p hEvent, then ::CUDA_SUCCESS is
	* returned. If this work has not yet been completed by the device then		* returned. If this work has not yet been completed by the device then
	* ::CUDA_ERROR_NOT_READY is returned.		* ::CUDA_ERROR_NOT_READY is returned.
	*		*
	* \param hEvent - Event to query		* \param hEvent - Event to query
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,		* ::CUDA_ERROR_NOT_INITIALIZED,
	* ::CUDA_ERROR_INVALID_CONTEXT,
	* ::CUDA_ERROR_INVALID_HANDLE,		* ::CUDA_ERROR_INVALID_HANDLE,
	* ::CUDA_ERROR_INVALID_VALUE,		* ::CUDA_ERROR_INVALID_VALUE,
	* ::CUDA_ERROR_NOT_READY		* ::CUDA_ERROR_NOT_READY
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cuEventCreate,		* \sa ::cuEventCreate,
	* ::cuEventRecord,		* ::cuEventRecord,
	* ::cuEventSynchronize,		* ::cuEventSynchronize,
	* ::cuEventDestroy,		* ::cuEventDestroy,
	* ::cuEventElapsedTime		* ::cuEventElapsedTime
	skipping to change at line 4831		skipping to change at line 5696
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cuEventCreate,		* \sa ::cuEventCreate,
	* ::cuEventRecord,		* ::cuEventRecord,
	* ::cuEventQuery,		* ::cuEventQuery,
	* ::cuEventDestroy,		* ::cuEventDestroy,
	* ::cuEventElapsedTime		* ::cuEventElapsedTime
	*/		*/
	CUresult CUDAAPI cuEventSynchronize(CUevent hEvent);		CUresult CUDAAPI cuEventSynchronize(CUevent hEvent);
			#if __CUDA_API_VERSION >= 4000
	/**		/**
	* \brief Destroys an event		* \brief Destroys an event
	*		*
	* Destroys the event specified by \p hEvent.		* Destroys the event specified by \p hEvent.
	*		*
			* In the case that \p hEvent has been recorded but has not yet been comple
			ted
			* when ::cuEventDestroy() is called, the function will return immediately
			and
			* the resources associated with \p hEvent will be released automatically o
			nce
			* the device has completed \p hEvent.
			*
	* \param hEvent - Event to destroy		* \param hEvent - Event to destroy
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,		* ::CUDA_ERROR_NOT_INITIALIZED,
	* ::CUDA_ERROR_INVALID_CONTEXT,		* ::CUDA_ERROR_INVALID_CONTEXT,
	* ::CUDA_ERROR_INVALID_HANDLE		* ::CUDA_ERROR_INVALID_HANDLE
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cuEventCreate,		* \sa ::cuEventCreate,
	* ::cuEventRecord,		* ::cuEventRecord,
	* ::cuEventQuery,		* ::cuEventQuery,
	* ::cuEventSynchronize,		* ::cuEventSynchronize,
	* ::cuEventElapsedTime		* ::cuEventElapsedTime
	*/		*/
	CUresult CUDAAPI cuEventDestroy(CUevent hEvent);		CUresult CUDAAPI cuEventDestroy(CUevent hEvent);
			#endif /* __CUDA_API_VERSION >= 4000 */
	/**		/**
	* \brief Computes the elapsed time between two events		* \brief Computes the elapsed time between two events
	*		*
	* Computes the elapsed time between two events (in milliseconds with a		* Computes the elapsed time between two events (in milliseconds with a
	* resolution of around 0.5 microseconds).		* resolution of around 0.5 microseconds).
	*		*
	* If either event was last recorded in a non-NULL stream, the resulting ti me		* If either event was last recorded in a non-NULL stream, the resulting ti me
	* may be greater than expected (even if both used the same stream handle). This		* may be greater than expected (even if both used the same stream handle). This
	* happens because the ::cuEventRecord() operation takes place asynchronous ly		* happens because the ::cuEventRecord() operation takes place asynchronous ly
	skipping to change at line 4909		skipping to change at line 5781
	/**		/**
	* \defgroup CUDA_EXEC Execution Control		* \defgroup CUDA_EXEC Execution Control
	*		*
	* This section describes the execution control functions of the low-level CUDA		* This section describes the execution control functions of the low-level CUDA
	* driver application programming interface.		* driver application programming interface.
	*		*
	* @{		* @{
	*/		*/
	/**		/**
	* \brief Sets the block-dimensions for the function
	*
	* Specifies the \p x, \p y, and \p z dimensions of the thread blocks that
	are
	* created when the kernel given by \p hfunc is launched.
	*
	* \param hfunc - Kernel to specify dimensions of
	* \param x - X dimension
	* \param y - Y dimension
	* \param z - Z dimension
	*
	* \return
	* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,
	* ::CUDA_ERROR_INVALID_CONTEXT,
	* ::CUDA_ERROR_INVALID_HANDLE,
	* ::CUDA_ERROR_INVALID_VALUE
	* \notefnerr
	*
	* \sa ::cuFuncSetSharedSize,
	* ::cuFuncSetCacheConfig,
	* ::cuFuncGetAttribute,
	* ::cuParamSetSize,
	* ::cuParamSeti,
	* ::cuParamSetf,
	* ::cuParamSetv,
	* ::cuLaunch,
	* ::cuLaunchGrid,
	* ::cuLaunchGridAsync
	*/
	CUresult CUDAAPI cuFuncSetBlockShape(CUfunction hfunc, int x, int y, int z)
	;
	/**
	* \brief Sets the dynamic shared-memory size for the function
	*
	* Sets through \p bytes the amount of dynamic shared memory that will be
	* available to each thread block when the kernel given by \p hfunc is laun
	ched.
	*
	* \param hfunc - Kernel to specify dynamic shared-memory size for
	* \param bytes - Dynamic shared-memory size per thread in bytes
	*
	* \return
	* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,
	* ::CUDA_ERROR_INVALID_CONTEXT,
	* ::CUDA_ERROR_INVALID_HANDLE,
	* ::CUDA_ERROR_INVALID_VALUE
	* \notefnerr
	*
	* \sa ::cuFuncSetBlockShape,
	* ::cuFuncSetCacheConfig,
	* ::cuFuncGetAttribute,
	* ::cuParamSetSize,
	* ::cuParamSeti,
	* ::cuParamSetf,
	* ::cuParamSetv,
	* ::cuLaunch,
	* ::cuLaunchGrid,
	* ::cuLaunchGridAsync
	*/
	CUresult CUDAAPI cuFuncSetSharedSize(CUfunction hfunc, unsigned int bytes);
	/**
	* \brief Returns information about a function		* \brief Returns information about a function
	*		*
	* Returns in \p *pi the integer value of the attribute \p attrib on the ke rnel		* Returns in \p *pi the integer value of the attribute \p attrib on the ke rnel
	* given by \p hfunc. The supported attributes are:		* given by \p hfunc. The supported attributes are:
	* - ::CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK: The maximum number of threa ds		* - ::CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK: The maximum number of threa ds
	* per block, beyond which a launch of the function would fail. This numb er		* per block, beyond which a launch of the function would fail. This numb er
	* depends on both the function and the device on which the function is		* depends on both the function and the device on which the function is
	* currently loaded.		* currently loaded.
	* - ::CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES: The size in bytes of		* - ::CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES: The size in bytes of
	* statically-allocated shared memory per block required by this function .		* statically-allocated shared memory per block required by this function .
	skipping to change at line 5016		skipping to change at line 5824
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,		* ::CUDA_ERROR_NOT_INITIALIZED,
	* ::CUDA_ERROR_INVALID_CONTEXT,		* ::CUDA_ERROR_INVALID_CONTEXT,
	* ::CUDA_ERROR_INVALID_HANDLE,		* ::CUDA_ERROR_INVALID_HANDLE,
	* ::CUDA_ERROR_INVALID_VALUE		* ::CUDA_ERROR_INVALID_VALUE
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cuFuncSetBlockShape,		* \sa ::cuCtxGetCacheConfig,
	* ::cuFuncSetSharedSize,		* ::cuCtxSetCacheConfig,
	* ::cuFuncSetCacheConfig,		* ::cuFuncSetCacheConfig,
	* ::cuParamSetSize,		* ::cuLaunchKernel
	* ::cuParamSeti,
	* ::cuParamSetf,
	* ::cuParamSetv,
	* ::cuLaunch,
	* ::cuLaunchGrid,
	* ::cuLaunchGridAsync
	*/		*/
	CUresult CUDAAPI cuFuncGetAttribute(int *pi, CUfunction_attribute attrib, C Ufunction hfunc);		CUresult CUDAAPI cuFuncGetAttribute(int *pi, CUfunction_attribute attrib, C Ufunction hfunc);
	/**		/**
	* \brief Sets the preferred cache configuration for a device function		* \brief Sets the preferred cache configuration for a device function
	*		*
	* On devices where the L1 cache and shared memory use the same hardware		* On devices where the L1 cache and shared memory use the same hardware
	* resources, this sets through \p config the preferred cache configuration for		* resources, this sets through \p config the preferred cache configuration for
	* the device function \p hfunc. This is only a preference. The driver will use		* the device function \p hfunc. This is only a preference. The driver will use
	* the requested configuration if possible, but it is free to choose a diff erent		* the requested configuration if possible, but it is free to choose a diff erent
	skipping to change at line 5065		skipping to change at line 5867
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,		* ::CUDA_ERROR_NOT_INITIALIZED,
	* ::CUDA_ERROR_INVALID_CONTEXT		* ::CUDA_ERROR_INVALID_CONTEXT
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cuCtxGetCacheConfig,		* \sa ::cuCtxGetCacheConfig,
	* ::cuCtxSetCacheConfig,		* ::cuCtxSetCacheConfig,
	* ::cuFuncSetBlockShape,		* ::cuFuncGetAttribute,
			* ::cuLaunchKernel
			*/
			CUresult CUDAAPI cuFuncSetCacheConfig(CUfunction hfunc, CUfunc_cache config
			);
			#if __CUDA_API_VERSION >= 4000
			/**
			* \brief Launches a CUDA function
			*
			* Invokes the kernel \p f on a \p gridDimX x \p gridDimY x \p gridDimZ
			* grid of blocks. Each block contains \p blockDimX x \p blockDimY x
			* \p blockDimZ threads.
			*
			* \p sharedMemBytes sets the amount of dynamic shared memory that will be
			* available to each thread block.
			*
			* ::cuLaunchKernel() can optionally be associated to a stream by passing a
			* non-zero \p hStream argument.
			*
			* Kernel parameters to \p f can be specified in one of two ways:
			*
			* 1) Kernel parameters can be specified via \p kernelParams. If \p f
			* has N parameters, then \p kernelParams needs to be an array of N
			* pointers. Each of \p kernelParams[0] through \p kernelParams[N-1]
			* must point to a region of memory from which the actual kernel
			* parameter will be copied. The number of kernel parameters and their
			* offsets and sizes do not need to be specified as that information is
			* retrieved directly from the kernel's image.
			*
			* 2) Kernel parameters can also be packaged by the application into
			* a single buffer that is passed in via the \p extra parameter.
			* This places the burden on the application of knowing each kernel
			* parameter's size and alignment/padding within the buffer. Here is
			* an example of using the \p extra parameter in this manner:
			* \code
			size_t argBufferSize;
			char argBuffer[256];
			// populate argBuffer and argBufferSize
			void *config[] = {
			CU_LAUNCH_PARAM_BUFFER_POINTER, argBuffer,
			CU_LAUNCH_PARAM_BUFFER_SIZE, &argBufferSize,
			CU_LAUNCH_PARAM_END
			};
			status = cuLaunchKernel(f, gx, gy, gz, bx, by, bz, sh, s, NULL, config)
			;
			* \endcode
			*
			* The \p extra parameter exists to allow ::cuLaunchKernel to take
			* additional less commonly used arguments. \p extra specifies a list of
			* names of extra settings and their corresponding values. Each extra
			* setting name is immediately followed by the corresponding value. The
			* list must be terminated with either NULL or ::CU_LAUNCH_PARAM_END.
			*
			* - ::CU_LAUNCH_PARAM_END, which indicates the end of the \p extra
			* array;
			* - ::CU_LAUNCH_PARAM_BUFFER_POINTER, which specifies that the next
			* value in \p extra will be a pointer to a buffer containing all
			* the kernel parameters for launching kernel \p f;
			* - ::CU_LAUNCH_PARAM_BUFFER_SIZE, which specifies that the next
			* value in \p extra will be a pointer to a size_t containing the
			* size of the buffer specified with ::CU_LAUNCH_PARAM_BUFFER_POINTER;
			*
			* The error ::CUDA_ERROR_INVALID_VALUE will be returned if kernel
			* parameters are specified with both \p kernelParams and \p extra
			* (i.e. both \p kernelParams and \p extra are non-NULL).
			*
			* Calling ::cuLaunchKernel() sets persistent function state that is
			* the same as function state set through the following deprecated APIs:
			*
			* ::cuFuncSetBlockShape()
			* ::cuFuncSetSharedSize()
			* ::cuParamSetSize()
			* ::cuParamSeti()
			* ::cuParamSetf()
			* ::cuParamSetv()
			*
			* When the kernel \p f is launched via ::cuLaunchKernel(), the previous
			* block shape, shared size and parameter info associated with \p f
			* is overwritten.
			*
			* Note that to use ::cuLaunchKernel(), the kernel \p f must either have
			* been compiled with toolchain version 3.2 or later so that it will
			* contain kernel parameter information, or have no kernel parameters.
			* If either of these conditions is not met, then ::cuLaunchKernel() will
			* return ::CUDA_ERROR_INVALID_IMAGE.
			*
			* \param f - Kernel to launch
			* \param gridDimX - Width of grid in blocks
			* \param gridDimY - Height of grid in blocks
			* \param gridDimZ - Depth of grid in blocks
			* \param blockDimX - X dimension of each thread block
			* \param blockDimY - Y dimension of each thread block
			* \param blockDimZ - Z dimension of each thread block
			* \param sharedMemBytes - Dynamic shared-memory size per thread block in b
			ytes
			* \param hStream - Stream identifier
			* \param kernelParams - Array of pointers to kernel parameters
			* \param extra - Extra options
			*
			* \return
			* ::CUDA_SUCCESS,
			* ::CUDA_ERROR_DEINITIALIZED,
			* ::CUDA_ERROR_NOT_INITIALIZED,
			* ::CUDA_ERROR_INVALID_CONTEXT,
			* ::CUDA_ERROR_INVALID_HANDLE,
			* ::CUDA_ERROR_INVALID_IMAGE,
			* ::CUDA_ERROR_INVALID_VALUE,
			* ::CUDA_ERROR_LAUNCH_FAILED,
			* ::CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES,
			* ::CUDA_ERROR_LAUNCH_TIMEOUT,
			* ::CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING,
			* ::CUDA_ERROR_SHARED_OBJECT_INIT_FAILED
			* \notefnerr
			*
			* \sa ::cuCtxGetCacheConfig,
			* ::cuCtxSetCacheConfig,
			* ::cuFuncSetCacheConfig,
			* ::cuFuncGetAttribute,
			*/
			CUresult CUDAAPI cuLaunchKernel(CUfunction f,
			unsigned int gridDimX,
			unsigned int gridDimY,
			unsigned int gridDimZ,
			unsigned int blockDimX,
			unsigned int blockDimY,
			unsigned int blockDimZ,
			unsigned int sharedMemBytes,
			CUstream hStream,
			void **kernelParams,
			void **extra);
			#endif /* __CUDA_API_VERSION >= 4000 */
			/**
			* \defgroup CUDA_EXEC_DEPRECATED Execution Control [DEPRECATED]
			*
			* This section describes the deprecated execution control functions of the
			* low-level CUDA driver application programming interface.
			*
			* @{
			*/
			/**
			* \brief Sets the block-dimensions for the function
			*
			* \deprecated
			*
			* Specifies the \p x, \p y, and \p z dimensions of the thread blocks that
			are
			* created when the kernel given by \p hfunc is launched.
			*
			* \param hfunc - Kernel to specify dimensions of
			* \param x - X dimension
			* \param y - Y dimension
			* \param z - Z dimension
			*
			* \return
			* ::CUDA_SUCCESS,
			* ::CUDA_ERROR_DEINITIALIZED,
			* ::CUDA_ERROR_NOT_INITIALIZED,
			* ::CUDA_ERROR_INVALID_CONTEXT,
			* ::CUDA_ERROR_INVALID_HANDLE,
			* ::CUDA_ERROR_INVALID_VALUE
			* \notefnerr
			*
			* \sa ::cuFuncSetSharedSize,
			* ::cuFuncSetCacheConfig,
	* ::cuFuncGetAttribute,		* ::cuFuncGetAttribute,
	* ::cuParamSetSize,		* ::cuParamSetSize,
	* ::cuParamSeti,		* ::cuParamSeti,
	* ::cuParamSetf,		* ::cuParamSetf,
	* ::cuParamSetv,		* ::cuParamSetv,
	* ::cuLaunch,		* ::cuLaunch,
	* ::cuLaunchGrid,		* ::cuLaunchGrid,
	* ::cuLaunchGridAsync		* ::cuLaunchGridAsync,
			* ::cuLaunchKernel
	*/		*/
	CUresult CUDAAPI cuFuncSetCacheConfig(CUfunction hfunc, CUfunc_cache config		CUresult CUDAAPI cuFuncSetBlockShape(CUfunction hfunc, int x, int y, int z)
	);		;
			/**
			* \brief Sets the dynamic shared-memory size for the function
			*
			* \deprecated
			*
			* Sets through \p bytes the amount of dynamic shared memory that will be
			* available to each thread block when the kernel given by \p hfunc is laun
			ched.
			*
			* \param hfunc - Kernel to specify dynamic shared-memory size for
			* \param bytes - Dynamic shared-memory size per thread in bytes
			*
			* \return
			* ::CUDA_SUCCESS,
			* ::CUDA_ERROR_DEINITIALIZED,
			* ::CUDA_ERROR_NOT_INITIALIZED,
			* ::CUDA_ERROR_INVALID_CONTEXT,
			* ::CUDA_ERROR_INVALID_HANDLE,
			* ::CUDA_ERROR_INVALID_VALUE
			* \notefnerr
			*
			* \sa ::cuFuncSetBlockShape,
			* ::cuFuncSetCacheConfig,
			* ::cuFuncGetAttribute,
			* ::cuParamSetSize,
			* ::cuParamSeti,
			* ::cuParamSetf,
			* ::cuParamSetv,
			* ::cuLaunch,
			* ::cuLaunchGrid,
			* ::cuLaunchGridAsync,
			* ::cuLaunchKernel
			*/
			CUresult CUDAAPI cuFuncSetSharedSize(CUfunction hfunc, unsigned int bytes);
	/**		/**
	* \brief Sets the parameter size for the function		* \brief Sets the parameter size for the function
	*		*
			* \deprecated
			*
	* Sets through \p numbytes the total size in bytes needed by the function		* Sets through \p numbytes the total size in bytes needed by the function
	* parameters of the kernel corresponding to \p hfunc.		* parameters of the kernel corresponding to \p hfunc.
	*		*
	* \param hfunc - Kernel to set parameter size for		* \param hfunc - Kernel to set parameter size for
	* \param numbytes - Size of parameter list in bytes		* \param numbytes - Size of parameter list in bytes
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,		* ::CUDA_ERROR_NOT_INITIALIZED,
	skipping to change at line 5102		skipping to change at line 6105
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cuFuncSetBlockShape,		* \sa ::cuFuncSetBlockShape,
	* ::cuFuncSetSharedSize,		* ::cuFuncSetSharedSize,
	* ::cuFuncGetAttribute,		* ::cuFuncGetAttribute,
	* ::cuParamSetf,		* ::cuParamSetf,
	* ::cuParamSeti,		* ::cuParamSeti,
	* ::cuParamSetv,		* ::cuParamSetv,
	* ::cuLaunch,		* ::cuLaunch,
	* ::cuLaunchGrid,		* ::cuLaunchGrid,
	* ::cuLaunchGridAsync		* ::cuLaunchGridAsync,
			* ::cuLaunchKernel
	*/		*/
	CUresult CUDAAPI cuParamSetSize(CUfunction hfunc, unsigned int numbytes);		CUresult CUDAAPI cuParamSetSize(CUfunction hfunc, unsigned int numbytes);
	/**		/**
	* \brief Adds an integer parameter to the function's argument list		* \brief Adds an integer parameter to the function's argument list
	*		*
			* \deprecated
			*
	* Sets an integer parameter that will be specified the next time the		* Sets an integer parameter that will be specified the next time the
	* kernel corresponding to \p hfunc will be invoked. \p offset is a byte of fset.		* kernel corresponding to \p hfunc will be invoked. \p offset is a byte of fset.
	*		*
	* \param hfunc - Kernel to add parameter to		* \param hfunc - Kernel to add parameter to
	* \param offset - Offset to add parameter to argument list		* \param offset - Offset to add parameter to argument list
	* \param value - Value of parameter		* \param value - Value of parameter
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	skipping to change at line 5132		skipping to change at line 6138
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cuFuncSetBlockShape,		* \sa ::cuFuncSetBlockShape,
	* ::cuFuncSetSharedSize,		* ::cuFuncSetSharedSize,
	* ::cuFuncGetAttribute,		* ::cuFuncGetAttribute,
	* ::cuParamSetSize,		* ::cuParamSetSize,
	* ::cuParamSetf,		* ::cuParamSetf,
	* ::cuParamSetv,		* ::cuParamSetv,
	* ::cuLaunch,		* ::cuLaunch,
	* ::cuLaunchGrid,		* ::cuLaunchGrid,
	* ::cuLaunchGridAsync		* ::cuLaunchGridAsync,
			* ::cuLaunchKernel
	*/		*/
	CUresult CUDAAPI cuParamSeti(CUfunction hfunc, int offset, unsigned int val ue);		CUresult CUDAAPI cuParamSeti(CUfunction hfunc, int offset, unsigned int val ue);
	/**		/**
	* \brief Adds a floating-point parameter to the function's argument list		* \brief Adds a floating-point parameter to the function's argument list
	*		*
			* \deprecated
			*
	* Sets a floating-point parameter that will be specified the next time the		* Sets a floating-point parameter that will be specified the next time the
	* kernel corresponding to \p hfunc will be invoked. \p offset is a byte of fset.		* kernel corresponding to \p hfunc will be invoked. \p offset is a byte of fset.
	*		*
	* \param hfunc - Kernel to add parameter to		* \param hfunc - Kernel to add parameter to
	* \param offset - Offset to add parameter to argument list		* \param offset - Offset to add parameter to argument list
	* \param value - Value of parameter		* \param value - Value of parameter
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	skipping to change at line 5162		skipping to change at line 6171
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cuFuncSetBlockShape,		* \sa ::cuFuncSetBlockShape,
	* ::cuFuncSetSharedSize,		* ::cuFuncSetSharedSize,
	* ::cuFuncGetAttribute,		* ::cuFuncGetAttribute,
	* ::cuParamSetSize,		* ::cuParamSetSize,
	* ::cuParamSeti,		* ::cuParamSeti,
	* ::cuParamSetv,		* ::cuParamSetv,
	* ::cuLaunch,		* ::cuLaunch,
	* ::cuLaunchGrid,		* ::cuLaunchGrid,
	* ::cuLaunchGridAsync		* ::cuLaunchGridAsync,
			* ::cuLaunchKernel
	*/		*/
	CUresult CUDAAPI cuParamSetf(CUfunction hfunc, int offset, float value);		CUresult CUDAAPI cuParamSetf(CUfunction hfunc, int offset, float value);
	/**		/**
	* \brief Adds arbitrary data to the function's argument list		* \brief Adds arbitrary data to the function's argument list
	*		*
			* \deprecated
			*
	* Copies an arbitrary amount of data (specified in \p numbytes) from \p pt r		* Copies an arbitrary amount of data (specified in \p numbytes) from \p pt r
	* into the parameter space of the kernel corresponding to \p hfunc. \p off set		* into the parameter space of the kernel corresponding to \p hfunc. \p off set
	* is a byte offset.		* is a byte offset.
	*		*
	* \param hfunc - Kernel to add data to		* \param hfunc - Kernel to add data to
	* \param offset - Offset to add data to argument list		* \param offset - Offset to add data to argument list
	* \param ptr - Pointer to arbitrary data		* \param ptr - Pointer to arbitrary data
	* \param numbytes - Size of data to copy in bytes		* \param numbytes - Size of data to copy in bytes
	*		*
	* \return		* \return
	skipping to change at line 5194		skipping to change at line 6206
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cuFuncSetBlockShape,		* \sa ::cuFuncSetBlockShape,
	* ::cuFuncSetSharedSize,		* ::cuFuncSetSharedSize,
	* ::cuFuncGetAttribute,		* ::cuFuncGetAttribute,
	* ::cuParamSetSize,		* ::cuParamSetSize,
	* ::cuParamSetf,		* ::cuParamSetf,
	* ::cuParamSeti,		* ::cuParamSeti,
	* ::cuLaunch,		* ::cuLaunch,
	* ::cuLaunchGrid,		* ::cuLaunchGrid,
	* ::cuLaunchGridAsync		* ::cuLaunchGridAsync,
			* ::cuLaunchKernel
	*/		*/
	CUresult CUDAAPI cuParamSetv(CUfunction hfunc, int offset, void *ptr, unsig ned int numbytes);		CUresult CUDAAPI cuParamSetv(CUfunction hfunc, int offset, void *ptr, unsig ned int numbytes);
	/**		/**
	* \brief Launches a CUDA function		* \brief Launches a CUDA function
	*		*
			* \deprecated
			*
	* Invokes the kernel \p f on a 1 x 1 x 1 grid of blocks. The block		* Invokes the kernel \p f on a 1 x 1 x 1 grid of blocks. The block
	* contains the number of threads specified by a previous call to		* contains the number of threads specified by a previous call to
	* ::cuFuncSetBlockShape().		* ::cuFuncSetBlockShape().
	*		*
	* \param f - Kernel to launch		* \param f - Kernel to launch
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,		* ::CUDA_ERROR_NOT_INITIALIZED,
	skipping to change at line 5228		skipping to change at line 6243
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cuFuncSetBlockShape,		* \sa ::cuFuncSetBlockShape,
	* ::cuFuncSetSharedSize,		* ::cuFuncSetSharedSize,
	* ::cuFuncGetAttribute,		* ::cuFuncGetAttribute,
	* ::cuParamSetSize,		* ::cuParamSetSize,
	* ::cuParamSetf,		* ::cuParamSetf,
	* ::cuParamSeti,		* ::cuParamSeti,
	* ::cuParamSetv,		* ::cuParamSetv,
	* ::cuLaunchGrid,		* ::cuLaunchGrid,
	* ::cuLaunchGridAsync		* ::cuLaunchGridAsync,
			* ::cuLaunchKernel
	*/		*/
	CUresult CUDAAPI cuLaunch(CUfunction f);		CUresult CUDAAPI cuLaunch(CUfunction f);
	/**		/**
	* \brief Launches a CUDA function		* \brief Launches a CUDA function
	*		*
			* \deprecated
			*
	* Invokes the kernel \p f on a \p grid_width x \p grid_height grid of		* Invokes the kernel \p f on a \p grid_width x \p grid_height grid of
	* blocks. Each block contains the number of threads specified by a previou s		* blocks. Each block contains the number of threads specified by a previou s
	* call to ::cuFuncSetBlockShape().		* call to ::cuFuncSetBlockShape().
	*		*
	* \param f - Kernel to launch		* \param f - Kernel to launch
	* \param grid_width - Width of grid in blocks		* \param grid_width - Width of grid in blocks
	* \param grid_height - Height of grid in blocks		* \param grid_height - Height of grid in blocks
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	skipping to change at line 5264		skipping to change at line 6282
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cuFuncSetBlockShape,		* \sa ::cuFuncSetBlockShape,
	* ::cuFuncSetSharedSize,		* ::cuFuncSetSharedSize,
	* ::cuFuncGetAttribute,		* ::cuFuncGetAttribute,
	* ::cuParamSetSize,		* ::cuParamSetSize,
	* ::cuParamSetf,		* ::cuParamSetf,
	* ::cuParamSeti,		* ::cuParamSeti,
	* ::cuParamSetv,		* ::cuParamSetv,
	* ::cuLaunch,		* ::cuLaunch,
	* ::cuLaunchGridAsync		* ::cuLaunchGridAsync,
			* ::cuLaunchKernel
	*/		*/
	CUresult CUDAAPI cuLaunchGrid(CUfunction f, int grid_width, int grid_height );		CUresult CUDAAPI cuLaunchGrid(CUfunction f, int grid_width, int grid_height );
	/**		/**
	* \brief Launches a CUDA function		* \brief Launches a CUDA function
	*		*
			* \deprecated
			*
	* Invokes the kernel \p f on a \p grid_width x \p grid_height grid of		* Invokes the kernel \p f on a \p grid_width x \p grid_height grid of
	* blocks. Each block contains the number of threads specified by a previou s		* blocks. Each block contains the number of threads specified by a previou s
	* call to ::cuFuncSetBlockShape().		* call to ::cuFuncSetBlockShape().
	*		*
	* ::cuLaunchGridAsync() can optionally be associated to a stream by passin g a		* ::cuLaunchGridAsync() can optionally be associated to a stream by passin g a
	* non-zero \p hStream argument.		* non-zero \p hStream argument.
	*		*
	* \param f - Kernel to launch		* \param f - Kernel to launch
	* \param grid_width - Width of grid in blocks		* \param grid_width - Width of grid in blocks
	* \param grid_height - Height of grid in blocks		* \param grid_height - Height of grid in blocks
	* \param hStream - Stream identifier		* \param hStream - Stream identifier
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,		* ::CUDA_ERROR_NOT_INITIALIZED,
	* ::CUDA_ERROR_INVALID_CONTEXT,		* ::CUDA_ERROR_INVALID_CONTEXT,
			* ::CUDA_ERROR_INVALID_HANDLE,
	* ::CUDA_ERROR_INVALID_VALUE,		* ::CUDA_ERROR_INVALID_VALUE,
	* ::CUDA_ERROR_LAUNCH_FAILED,		* ::CUDA_ERROR_LAUNCH_FAILED,
	* ::CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES,		* ::CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES,
	* ::CUDA_ERROR_LAUNCH_TIMEOUT,		* ::CUDA_ERROR_LAUNCH_TIMEOUT,
	* ::CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING,		* ::CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING,
	* ::CUDA_ERROR_SHARED_OBJECT_INIT_FAILED		* ::CUDA_ERROR_SHARED_OBJECT_INIT_FAILED
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cuFuncSetBlockShape,		* \sa ::cuFuncSetBlockShape,
	* ::cuFuncSetSharedSize,		* ::cuFuncSetSharedSize,
	* ::cuFuncGetAttribute,		* ::cuFuncGetAttribute,
	* ::cuParamSetSize,		* ::cuParamSetSize,
	* ::cuParamSetf,		* ::cuParamSetf,
	* ::cuParamSeti,		* ::cuParamSeti,
	* ::cuParamSetv,		* ::cuParamSetv,
	* ::cuLaunch,		* ::cuLaunch,
	* ::cuLaunchGrid		* ::cuLaunchGrid,
			* ::cuLaunchKernel
	*/		*/
	CUresult CUDAAPI cuLaunchGridAsync(CUfunction f, int grid_width, int grid_h eight, CUstream hStream);		CUresult CUDAAPI cuLaunchGridAsync(CUfunction f, int grid_width, int grid_h eight, CUstream hStream);
	/**		/**
	* \defgroup CUDA_EXEC_DEPRECATED Execution Control [DEPRECATED]
	*
	* This section describes the deprecated execution control functions of the
	* low-level CUDA driver application programming interface.
	*
	* @{
	*/
	/**
	* \brief Adds a texture-reference to the function's argument list		* \brief Adds a texture-reference to the function's argument list
	*		*
	* \deprecated		* \deprecated
	*		*
	* Makes the CUDA array or linear memory bound to the texture reference		* Makes the CUDA array or linear memory bound to the texture reference
	* \p hTexRef available to a device program as a texture. In this version o f		* \p hTexRef available to a device program as a texture. In this version o f
	* CUDA, the texture-reference must be obtained via ::cuModuleGetTexRef() a nd		* CUDA, the texture-reference must be obtained via ::cuModuleGetTexRef() a nd
	* the \p texunit parameter must be set to ::CU_PARAM_TR_DEFAULT.		* the \p texunit parameter must be set to ::CU_PARAM_TR_DEFAULT.
	*		*
	* \param hfunc - Kernel to add texture-reference to		* \param hfunc - Kernel to add texture-reference to
	skipping to change at line 5507		skipping to change at line 6521
	* \code		* \code
	typedef enum CUaddress_mode_enum {		typedef enum CUaddress_mode_enum {
	CU_TR_ADDRESS_MODE_WRAP = 0,		CU_TR_ADDRESS_MODE_WRAP = 0,
	CU_TR_ADDRESS_MODE_CLAMP = 1,		CU_TR_ADDRESS_MODE_CLAMP = 1,
	CU_TR_ADDRESS_MODE_MIRROR = 2,		CU_TR_ADDRESS_MODE_MIRROR = 2,
	CU_TR_ADDRESS_MODE_BORDER = 3		CU_TR_ADDRESS_MODE_BORDER = 3
	} CUaddress_mode;		} CUaddress_mode;
	* \endcode		* \endcode
	*		*
	* Note that this call has no effect if \p hTexRef is bound to linear memor y.		* Note that this call has no effect if \p hTexRef is bound to linear memor y.
			* Also, if the flag, ::CU_TRSF_NORMALIZED_COORDINATES, is not set, the onl
			y
			* supported address mode is ::CU_TR_ADDRESS_MODE_CLAMP.
	*		*
	* \param hTexRef - Texture reference		* \param hTexRef - Texture reference
	* \param dim - Dimension		* \param dim - Dimension
	* \param am - Addressing mode to set		* \param am - Addressing mode to set
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_DEINITIALIZED,		* ::CUDA_ERROR_DEINITIALIZED,
	* ::CUDA_ERROR_NOT_INITIALIZED,		* ::CUDA_ERROR_NOT_INITIALIZED,
	* ::CUDA_ERROR_INVALID_CONTEXT,		* ::CUDA_ERROR_INVALID_CONTEXT,
	skipping to change at line 5857		skipping to change at line 6873
	* ::CUDA_ERROR_NOT_INITIALIZED,		* ::CUDA_ERROR_NOT_INITIALIZED,
	* ::CUDA_ERROR_INVALID_CONTEXT,		* ::CUDA_ERROR_INVALID_CONTEXT,
	* ::CUDA_ERROR_INVALID_VALUE		* ::CUDA_ERROR_INVALID_VALUE
	*		*
	* \sa ::cuModuleGetSurfRef, ::cuSurfRefSetArray		* \sa ::cuModuleGetSurfRef, ::cuSurfRefSetArray
	*/		*/
	CUresult CUDAAPI cuSurfRefGetArray(CUarray *phArray, CUsurfref hSurfRef);		CUresult CUDAAPI cuSurfRefGetArray(CUarray *phArray, CUsurfref hSurfRef);
	/** @} */ /* END CUDA_SURFREF */		/** @} */ /* END CUDA_SURFREF */
			#if __CUDA_API_VERSION >= 4000
			/**
			* \defgroup CUDA_PEER_ACCESS Peer Context Memory Access
			*
			* This section describes the direct peer context memory access functions
			* of the low-level CUDA driver application programming interface.
			*
			* @{
			*/
			/**
			* \brief Queries if a device may directly access a peer device's memory.
			*
			* Returns in \p *canAccessPeer a value of 1 if contexts on \p dev are capa
			ble of
			* directly accessing memory from contexts on \p peerDev and 0 otherwise.
			* If direct access of \p peerDev from \p dev is possible, then access may
			be
			* enabled on two specific contexts by calling ::cuCtxEnablePeerAccess().
			*
			* \param canAccessPeer - Returned access capability
			* \param dev - Device from which allocations on \p peerDev are t
			o
			* be directly accessed.
			* \param peerDev - Device on which the allocations to be directly ac
			cessed
			* by \p dev reside.
			*
			* \return
			* ::CUDA_SUCCESS,
			* ::CUDA_ERROR_DEINITIALIZED,
			* ::CUDA_ERROR_NOT_INITIALIZED,
			* ::CUDA_ERROR_INVALID_DEVICE
			* \notefnerr
			*
			* \sa ::cuCtxEnablePeerAccess,
			* ::cuCtxDisablePeerAccess
			*/
			CUresult CUDAAPI cuDeviceCanAccessPeer(int *canAccessPeer, CUdevice dev, CU
			device peerDev);
			/**
			* \brief Enables direct access to memory allocations in a peer context.
			*
			If both the current context and \p peerContext are on devices which suppo
			rt unified
			* addressing (as may be queried using ::CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRES
			SING), then
			* on success all allocations from \p peerContext will immediately be acces
			sible
			* by the current context. See \ref CUDA_UNIFIED for additional
			* details.
			*
			* Note that access granted by this call is unidirectional and that in orde
			r to access
			* memory from the current context in \p peerContext, a separate symmetric
			call
			* to ::cuCtxEnablePeerAccess() is required.
			*
			* Returns ::CUDA_ERROR_INVALID_DEVICE if ::cuDeviceCanAccessPeer() indicat
			es
			* that the ::CUdevice of the current context cannot directly access memory
			* from the ::CUdevice of \p peerContext.
			*
			* Returns ::CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED if direct access of
			* \p peerContext from the current context has already been enabled.
			*
			* Returns ::CUDA_ERROR_INVALID_CONTEXT if there is no current context, \p
			peerContext
			* is not a valid context, or if the current context is \p peerContext.
			*
			* Returns ::CUDA_ERROR_INVALID_VALUE if \p Flags is not 0.
			*
			* \param peerContext - Peer context to enable direct access to from the cu
			rrent context
			* \param Flags - Reserved for future use and must be set to 0
			*
			* \return
			* ::CUDA_SUCCESS,
			* ::CUDA_ERROR_DEINITIALIZED,
			* ::CUDA_ERROR_NOT_INITIALIZED,
			* ::CUDA_ERROR_INVALID_DEVICE,
			* ::CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED,
			* ::CUDA_ERROR_INVALID_CONTEXT,
			* ::CUDA_ERROR_INVALID_VALUE
			* \notefnerr
			*
			* \sa ::cuDeviceCanAccessPeer,
			* ::cuCtxDisablePeerAccess
			*/
			CUresult CUDAAPI cuCtxEnablePeerAccess(CUcontext peerContext, unsigned int
			Flags);
			/**
			* \brief Disables direct access to memory allocations in a peer context an
			d
			* unregisters any registered allocations.
			*
			Returns ::CUDA_ERROR_PEER_ACCESS_NOT_ENABLED if direct peer access has
			* not yet been enabled from \p peerContext to the current context.
			*
			* Returns ::CUDA_ERROR_INVALID_CONTEXT if there is no current context, or
			if
			* \p peerContext is not a valid context.
			*
			* \param peerContext - Peer context to disable direct access to
			*
			* \return
			* ::CUDA_SUCCESS,
			* ::CUDA_ERROR_DEINITIALIZED,
			* ::CUDA_ERROR_NOT_INITIALIZED,
			* ::CUDA_ERROR_PEER_ACCESS_NOT_ENABLED,
			* ::CUDA_ERROR_INVALID_CONTEXT,
			* \notefnerr
			*
			* \sa ::cuDeviceCanAccessPeer,
			* ::cuCtxEnablePeerAccess
			*/
			CUresult CUDAAPI cuCtxDisablePeerAccess(CUcontext peerContext);
			/** @} */ /* END CUDA_PEER_ACCESS */
			#endif /* __CUDA_API_VERSION >= 4000 */
	/**		/**
	* \defgroup CUDA_GRAPHICS Graphics Interoperability		* \defgroup CUDA_GRAPHICS Graphics Interoperability
	*		*
	* This section describes the graphics interoperability functions of the		* This section describes the graphics interoperability functions of the
	* low-level CUDA driver application programming interface.		* low-level CUDA driver application programming interface.
	*		*
	* @{		* @{
	*/		*/
	/**		/**
	skipping to change at line 6133		skipping to change at line 7256
	#undef cuMemsetD2D16		#undef cuMemsetD2D16
	#undef cuMemsetD2D32		#undef cuMemsetD2D32
	#undef cuArrayCreate		#undef cuArrayCreate
	#undef cuArrayGetDescriptor		#undef cuArrayGetDescriptor
	#undef cuArray3DCreate		#undef cuArray3DCreate
	#undef cuArray3DGetDescriptor		#undef cuArray3DGetDescriptor
	#undef cuTexRefSetAddress		#undef cuTexRefSetAddress
	#undef cuTexRefSetAddress2D		#undef cuTexRefSetAddress2D
	#undef cuTexRefGetAddress		#undef cuTexRefGetAddress
	#undef cuGraphicsResourceGetMappedPointer		#undef cuGraphicsResourceGetMappedPointer
			#undef cuCtxDestroy
			#undef cuCtxPopCurrent
			#undef cuCtxPushCurrent
			#undef cuStreamDestroy
			#undef cuEventDestroy
	#endif /* __CUDA_API_VERSION_INTERNAL */		#endif /* __CUDA_API_VERSION_INTERNAL */
	/**		/**
	* CUDA API made obselete at API version 3020		* CUDA API made obselete at API version 3020
	*/		*/
	#if defined(__CUDA_API_VERSION_INTERNAL)		#if defined(__CUDA_API_VERSION_INTERNAL)
	#define CUdeviceptr CUdeviceptr_v1		#define CUdeviceptr CUdeviceptr_v1
	#define CUDA_MEMCPY2D_st CUDA_MEMCPY2D_v1_st		#define CUDA_MEMCPY2D_st CUDA_MEMCPY2D_v1_st
	#define CUDA_MEMCPY2D CUDA_MEMCPY2D_v1		#define CUDA_MEMCPY2D CUDA_MEMCPY2D_v1
	#define CUDA_MEMCPY3D_st CUDA_MEMCPY3D_v1_st		#define CUDA_MEMCPY3D_st CUDA_MEMCPY3D_v1_st
	skipping to change at line 6269		skipping to change at line 7397
	CUresult CUDAAPI cuMemsetD2D16(CUdeviceptr dstDevice, unsigned int dstPitch , unsigned short us, unsigned int Width, unsigned int Height);		CUresult CUDAAPI cuMemsetD2D16(CUdeviceptr dstDevice, unsigned int dstPitch , unsigned short us, unsigned int Width, unsigned int Height);
	CUresult CUDAAPI cuMemsetD2D32(CUdeviceptr dstDevice, unsigned int dstPitch , unsigned int ui, unsigned int Width, unsigned int Height);		CUresult CUDAAPI cuMemsetD2D32(CUdeviceptr dstDevice, unsigned int dstPitch , unsigned int ui, unsigned int Width, unsigned int Height);
	CUresult CUDAAPI cuArrayCreate(CUarray *pHandle, const CUDA_ARRAY_DESCRIPTO R *pAllocateArray);		CUresult CUDAAPI cuArrayCreate(CUarray *pHandle, const CUDA_ARRAY_DESCRIPTO R *pAllocateArray);
	CUresult CUDAAPI cuArrayGetDescriptor(CUDA_ARRAY_DESCRIPTOR *pArrayDescript or, CUarray hArray);		CUresult CUDAAPI cuArrayGetDescriptor(CUDA_ARRAY_DESCRIPTOR *pArrayDescript or, CUarray hArray);
	CUresult CUDAAPI cuArray3DCreate(CUarray *pHandle, const CUDA_ARRAY3D_DESCR IPTOR *pAllocateArray);		CUresult CUDAAPI cuArray3DCreate(CUarray *pHandle, const CUDA_ARRAY3D_DESCR IPTOR *pAllocateArray);
	CUresult CUDAAPI cuArray3DGetDescriptor(CUDA_ARRAY3D_DESCRIPTOR *pArrayDesc riptor, CUarray hArray);		CUresult CUDAAPI cuArray3DGetDescriptor(CUDA_ARRAY3D_DESCRIPTOR *pArrayDesc riptor, CUarray hArray);
	CUresult CUDAAPI cuTexRefSetAddress(unsigned int *ByteOffset, CUtexref hTex Ref, CUdeviceptr dptr, unsigned int bytes);		CUresult CUDAAPI cuTexRefSetAddress(unsigned int *ByteOffset, CUtexref hTex Ref, CUdeviceptr dptr, unsigned int bytes);
	CUresult CUDAAPI cuTexRefSetAddress2D(CUtexref hTexRef, const CUDA_ARRAY_DE SCRIPTOR *desc, CUdeviceptr dptr, unsigned int Pitch);		CUresult CUDAAPI cuTexRefSetAddress2D(CUtexref hTexRef, const CUDA_ARRAY_DE SCRIPTOR *desc, CUdeviceptr dptr, unsigned int Pitch);
	CUresult CUDAAPI cuTexRefGetAddress(CUdeviceptr *pdptr, CUtexref hTexRef);		CUresult CUDAAPI cuTexRefGetAddress(CUdeviceptr *pdptr, CUtexref hTexRef);
	CUresult CUDAAPI cuGraphicsResourceGetMappedPointer(CUdeviceptr *pDevPtr, u nsigned int *pSize, CUgraphicsResource resource);		CUresult CUDAAPI cuGraphicsResourceGetMappedPointer(CUdeviceptr *pDevPtr, u nsigned int *pSize, CUgraphicsResource resource);
	#endif /* __CUDA_API_VERSION_INTERNAL || __CUDA_API_VERSION < 3020 */		#endif /* __CUDA_API_VERSION_INTERNAL || __CUDA_API_VERSION < 3020 */
			#if defined(__CUDA_API_VERSION_INTERNAL) || __CUDA_API_VERSION < 4000
			CUresult CUDAAPI cuCtxDestroy(CUcontext ctx);
			CUresult CUDAAPI cuCtxPopCurrent(CUcontext *pctx);
			CUresult CUDAAPI cuCtxPushCurrent(CUcontext ctx);
			CUresult CUDAAPI cuStreamDestroy(CUstream hStream);
			CUresult CUDAAPI cuEventDestroy(CUevent hEvent);
			#endif /* __CUDA_API_VERSION_INTERNAL || __CUDA_API_VERSION < 4000 */
	#if defined(__CUDA_API_VERSION_INTERNAL)		#if defined(__CUDA_API_VERSION_INTERNAL)
	#undef CUdeviceptr		#undef CUdeviceptr
	#undef CUDA_MEMCPY2D_st		#undef CUDA_MEMCPY2D_st
	#undef CUDA_MEMCPY2D		#undef CUDA_MEMCPY2D
	#undef CUDA_MEMCPY3D_st		#undef CUDA_MEMCPY3D_st
	#undef CUDA_MEMCPY3D		#undef CUDA_MEMCPY3D
	#undef CUDA_ARRAY_DESCRIPTOR_st		#undef CUDA_ARRAY_DESCRIPTOR_st
	#undef CUDA_ARRAY_DESCRIPTOR		#undef CUDA_ARRAY_DESCRIPTOR
	#undef CUDA_ARRAY3D_DESCRIPTOR_st		#undef CUDA_ARRAY3D_DESCRIPTOR_st
End of changes. 139 change blocks.
	276 lines changed or deleted		1591 lines changed or added

	cudaGL.h		cudaGL.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#ifndef CUDAGL_H		#ifndef CUDAGL_H
	#define CUDAGL_H		#define CUDAGL_H
	/**		/**
	* CUDA API versioning support		* CUDA API versioning support
	*/		*/
	#if defined(CUDA_FORCE_API_VERSION)		#if defined(CUDA_FORCE_API_VERSION)
	#if (CUDA_FORCE_API_VERSION == 3010)		#if (CUDA_FORCE_API_VERSION == 3010)
	skipping to change at line 115		skipping to change at line 129
	* ::cuWGLGetDevice		* ::cuWGLGetDevice
	*/		*/
	CUresult CUDAAPI cuGLCtxCreate(CUcontext *pCtx, unsigned int Flags, CUdevic e device );		CUresult CUDAAPI cuGLCtxCreate(CUcontext *pCtx, unsigned int Flags, CUdevic e device );
	#endif /* __CUDA_API_VERSION >= 3020 */		#endif /* __CUDA_API_VERSION >= 3020 */
	/**		/**
	* \brief Registers an OpenGL buffer object		* \brief Registers an OpenGL buffer object
	*		*
	* Registers the buffer object specified by \p buffer for access by		* Registers the buffer object specified by \p buffer for access by
	* CUDA. A handle to the registered object is returned as \p		* CUDA. A handle to the registered object is returned as \p
	* pCudaResource. The map flags \p Flags specify the intended usage,		* pCudaResource. The register flags \p Flags specify the intended usage,
	* as follows:		* as follows:
	*		*
	* - ::CU_GRAPHICS_MAP_RESOURCE_FLAGS_NONE: Specifies no hints about how th is		* - ::CU_GRAPHICS_REGISTER_FLAGS_NONE: Specifies no hints about how this
	* resource will be used. It is therefore assumed that this resource will be		* resource will be used. It is therefore assumed that this resource will be
	* read from and written to by CUDA. This is the default value.		* read from and written to by CUDA. This is the default value.
	* - ::CU_GRAPHICS_MAP_RESOURCE_FLAGS_READ_ONLY: Specifies that CUDA		* - ::CU_GRAPHICS_REGISTER_FLAGS_READ_ONLY: Specifies that CUDA
	* will not write to this resource.		* will not write to this resource.
	* - ::CU_GRAPHICS_MAP_RESOURCE_FLAGS_WRITE_DISCARD: Specifies that		* - ::CU_GRAPHICS_REGISTER_FLAGS_WRITE_DISCARD: Specifies that
	* CUDA will not read from this resource and will write over the		* CUDA will not read from this resource and will write over the
	* entire contents of the resource, so none of the data previously		* entire contents of the resource, so none of the data previously
	* stored in the resource will be preserved.		* stored in the resource will be preserved.
	*		*
	* \param pCudaResource - Pointer to the returned object handle		* \param pCudaResource - Pointer to the returned object handle
	* \param buffer - name of buffer object to be registered		* \param buffer - name of buffer object to be registered
	* \param Flags - Map flags		* \param Flags - Register flags
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_INVALID_HANDLE,		* ::CUDA_ERROR_INVALID_HANDLE,
	* ::CUDA_ERROR_ALREADY_MAPPED,		* ::CUDA_ERROR_ALREADY_MAPPED,
	* ::CUDA_ERROR_INVALID_CONTEXT,		* ::CUDA_ERROR_INVALID_CONTEXT,
	* \notefnerr		* \notefnerr
	*		*
	* \sa		* \sa
	* ::cuGLCtxCreate,		* ::cuGLCtxCreate,
	skipping to change at line 153		skipping to change at line 167
	* ::cuGraphicsResourceGetMappedPointer		* ::cuGraphicsResourceGetMappedPointer
	*/		*/
	CUresult CUDAAPI cuGraphicsGLRegisterBuffer(CUgraphicsResource *pCudaResour ce, GLuint buffer, unsigned int Flags);		CUresult CUDAAPI cuGraphicsGLRegisterBuffer(CUgraphicsResource *pCudaResour ce, GLuint buffer, unsigned int Flags);
	/**		/**
	* \brief Register an OpenGL texture or renderbuffer object		* \brief Register an OpenGL texture or renderbuffer object
	*		*
	* Registers the texture or renderbuffer object specified by \p image for a ccess by CUDA.		* Registers the texture or renderbuffer object specified by \p image for a ccess by CUDA.
	* \p target must match the type of the object.		* \p target must match the type of the object.
	* A handle to the registered object is returned as \p pCudaResource.		* A handle to the registered object is returned as \p pCudaResource.
	* The map flags \p Flags specify the intended usage, as follows:		* The register flags \p Flags specify the intended usage, as follows:
	*		*
	* - ::CU_GRAPHICS_MAP_RESOURCE_FLAGS_NONE: Specifies no hints about how th is		* - ::CU_GRAPHICS_REGISTER_FLAGS_NONE: Specifies no hints about how this
	* resource will be used. It is therefore assumed that this resource will be		* resource will be used. It is therefore assumed that this resource will be
	* read from and written to by CUDA. This is the default value.		* read from and written to by CUDA. This is the default value.
	* - ::CU_GRAPHICS_MAP_RESOURCE_FLAGS_READ_ONLY: Specifies that CUDA		* - ::CU_GRAPHICS_REGISTER_FLAGS_READ_ONLY: Specifies that CUDA
	* will not write to this resource.		* will not write to this resource.
	* - ::CU_GRAPHICS_MAP_RESOURCE_FLAGS_WRITE_DISCARD: Specifies that		* - ::CU_GRAPHICS_REGISTER_FLAGS_WRITE_DISCARD: Specifies that
	* CUDA will not read from this resource and will write over the		* CUDA will not read from this resource and will write over the
	* entire contents of the resource, so none of the data previously		* entire contents of the resource, so none of the data previously
	* stored in the resource will be preserved.		* stored in the resource will be preserved.
			* - ::CU_GRAPHICS_REGISTER_FLAGS_SURFACE_LDST: Specifies that CUDA will
			* bind this resource to a surface reference.
	*		*
	* The following image classes are currently disallowed:		* The following image classes are currently disallowed:
	* - Textures with borders		* - Textures with borders
	* - Multisampled renderbuffers		* - Multisampled renderbuffers
	*		*
	* \param pCudaResource - Pointer to the returned object handle		* \param pCudaResource - Pointer to the returned object handle
	* \param image - name of texture or renderbuffer object to be registered		* \param image - name of texture or renderbuffer object to be registered
	* \param target - Identifies the type of object specified by \p image, and must be one of		* \param target - Identifies the type of object specified by \p image, and must be one of
	* ::GL_TEXTURE_2D,		* ::GL_TEXTURE_2D,
	* ::GL_TEXTURE_RECTANGLE,		* ::GL_TEXTURE_RECTANGLE,
	* ::GL_TEXTURE_CUBE_MAP,		* ::GL_TEXTURE_CUBE_MAP,
	* ::GL_TEXTURE_3D,		* ::GL_TEXTURE_3D,
	* ::GL_TEXTURE_2D_ARRAY, or		* ::GL_TEXTURE_2D_ARRAY, or
	* ::GL_RENDERBUFFER.		* ::GL_RENDERBUFFER.
	* \param Flags - Map flags		* \param Flags - Register flags
	*		*
	* \return		* \return
	* ::CUDA_SUCCESS,		* ::CUDA_SUCCESS,
	* ::CUDA_ERROR_INVALID_HANDLE,		* ::CUDA_ERROR_INVALID_HANDLE,
	* ::CUDA_ERROR_ALREADY_MAPPED,		* ::CUDA_ERROR_ALREADY_MAPPED,
	* ::CUDA_ERROR_INVALID_CONTEXT,		* ::CUDA_ERROR_INVALID_CONTEXT,
	* \notefnerr		* \notefnerr
	*		*
	* \sa		* \sa
	* ::cuGLCtxCreate,		* ::cuGLCtxCreate,
End of changes. 17 change blocks.
	38 lines changed or deleted		53 lines changed or added

	cudaVDPAU.h		cudaVDPAU.h
	/*		/*
	* Copyright 2010 NVIDIA Corporation. All rights reserved.		* Copyright 2010-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#ifndef CUDAVDPAU_H		#ifndef CUDAVDPAU_H
	#define CUDAVDPAU_H		#define CUDAVDPAU_H
	/**		/**
	* CUDA API versioning support		* CUDA API versioning support
	*/		*/
	#if defined(CUDA_FORCE_API_VERSION)		#if defined(CUDA_FORCE_API_VERSION)
	#if (CUDA_FORCE_API_VERSION == 3010)		#if (CUDA_FORCE_API_VERSION == 3010)
End of changes. 6 change blocks.
	28 lines changed or deleted		41 lines changed or added

	cuda_gl_interop.h		cuda_gl_interop.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(__CUDA_GL_INTEROP_H__)		#if !defined(__CUDA_GL_INTEROP_H__)
	#define __CUDA_GL_INTEROP_H__		#define __CUDA_GL_INTEROP_H__
	#include "builtin_types.h"		#include "builtin_types.h"
	#include "host_defines.h"		#include "host_defines.h"
	#if defined(__APPLE__)		#if defined(__APPLE__)
	skipping to change at line 64		skipping to change at line 78
	#endif /* __cplusplus */		#endif /* __cplusplus */
	/**		/**
	* \addtogroup CUDART_OPENGL OpenGL Interoperability		* \addtogroup CUDART_OPENGL OpenGL Interoperability
	* This section describes the OpenGL interoperability functions of the CUDA		* This section describes the OpenGL interoperability functions of the CUDA
	* runtime application programming interface.		* runtime application programming interface.
	*		*
	* @{		* @{
	*/		*/
	/**		/*
	* \brief Sets the CUDA device for use with OpenGL interoperability		* \brief Sets a CUDA device to use OpenGL interoperability
	*		*
	* Records \p device as the device on which the active host thread executes		* Records the calling thread's current OpenGL context as the
	* the device code. Records the thread as using OpenGL interoperability.		* OpenGL context to use for OpenGL interoperability with the CUDA
	* If the host thread has already initialized the CUDA runtime by		* device \p device and sets \p device as the current device for the
	* calling non-device management runtime functions or if there exists a CUD		* calling host thread.
	A		*
	* driver context active on the host thread, then this call returns		* If \p device has already been initialized then this call will fail
	* ::cudaErrorSetOnActiveProcess.		* with the error ::cudaErrorSetOnActiveProcess. In this case it is
			* necessary to reset \p device using ::cudaDeviceReset() before
			* OpenGL interoperability on \p device may be enabled.
	*		*
	* \param device - Device to use for OpenGL interoperability		* \param device - Device to use for OpenGL interoperability
	*		*
	* \return		* \return
	* ::cudaSuccess,		* ::cudaSuccess,
	* ::cudaErrorInvalidDevice,		* ::cudaErrorInvalidDevice,
	* ::cudaErrorSetOnActiveProcess		* ::cudaErrorSetOnActiveProcess
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cudaGLRegisterBufferObject, ::cudaGLMapBufferObject,		* \sa ::cudaGLRegisterBufferObject, ::cudaGLMapBufferObject,
	* ::cudaGLUnmapBufferObject, ::cudaGLUnregisterBufferObject,		* ::cudaGLUnmapBufferObject, ::cudaGLUnregisterBufferObject,
	* ::cudaGLMapBufferObjectAsync, ::cudaGLUnmapBufferObjectAsync		* ::cudaGLMapBufferObjectAsync, ::cudaGLUnmapBufferObjectAsync,
			* ::cudaDeviceReset
	*/		*/
	extern __host__ cudaError_t CUDARTAPI cudaGLSetGLDevice(int device);		extern __host__ cudaError_t CUDARTAPI cudaGLSetGLDevice(int device);
	/**		/**
	* \brief Register an OpenGL texture or renderbuffer object		* \brief Register an OpenGL texture or renderbuffer object
	*		*
	* Registers the texture or renderbuffer object specified by \p image for a ccess by CUDA.		* Registers the texture or renderbuffer object specified by \p image for a ccess by CUDA.
	* \p target must match the type of the object.		* \p target must match the type of the object.
	* A handle to the registered object is returned as \p resource.		* A handle to the registered object is returned as \p resource.
	* The map flags \p flags specify the intended usage, as follows:		* The register flags \p flags specify the intended usage, as follows:
	*		*
	* - ::cudaGraphicsMapFlagsNone: Specifies no hints about how this		* - ::cudaGraphicsRegisterFlagsNone: Specifies no hints about how this
	* resource will be used. It is therefore assumed that this resource will be		* resource will be used. It is therefore assumed that this resource will be
	* read from and written to by CUDA. This is the default value.		* read from and written to by CUDA. This is the default value.
	* - ::cudaGraphicsMapFlagsReadOnly: Specifies that CUDA		* - ::cudaGraphicsRegisterFlagsReadOnly: Specifies that CUDA
	* will not write to this resource.		* will not write to this resource.
	* - ::cudaGraphicsMapFlagsWriteDiscard: Specifies that		* - ::cudaGraphicsRegisterFlagsWriteDiscard: Specifies that
	* CUDA will not read from this resource and will write over the		* CUDA will not read from this resource and will write over the
	* entire contents of the resource, so none of the data previously		* entire contents of the resource, so none of the data previously
	* stored in the resource will be preserved.		* stored in the resource will be preserved.
			* - ::cudaGraphicsRegisterFlagsSurfaceLoadStore: Specifies that CUDA will
			* bind this resource to a surface reference.
	*		*
	* The following image classes are currently disallowed:		* The following image classes are currently disallowed:
	* - Textures with borders		* - Textures with borders
	* - Multisampled renderbuffers		* - Multisampled renderbuffers
	*		*
	* \param resource - Pointer to the returned object handle		* \param resource - Pointer to the returned object handle
	* \param image - name of texture or renderbuffer object to be registere d		* \param image - name of texture or renderbuffer object to be registere d
	* \param target - Identifies the type of object specified by \p image, a nd must be one of		* \param target - Identifies the type of object specified by \p image, a nd must be one of
	* ::GL_TEXTURE_2D,		* ::GL_TEXTURE_2D,
	* ::GL_TEXTURE_RECTANGLE,		* ::GL_TEXTURE_RECTANGLE,
	* ::GL_TEXTURE_CUBE_MAP,		* ::GL_TEXTURE_CUBE_MAP,
	* ::GL_TEXTURE_3D,		* ::GL_TEXTURE_3D,
	* ::GL_TEXTURE_2D_ARRAY, or		* ::GL_TEXTURE_2D_ARRAY, or
	* ::GL_RENDERBUFFER.		* ::GL_RENDERBUFFER.
	* \param flags - Map flags		* \param flags - Register flags
	*		*
	* \return		* \return
	* ::cudaSuccess,		* ::cudaSuccess,
	* ::cudaErrorInvalidDevice,		* ::cudaErrorInvalidDevice,
	* ::cudaErrorInvalidValue,		* ::cudaErrorInvalidValue,
	* ::cudaErrorInvalidResourceHandle,		* ::cudaErrorInvalidResourceHandle,
	* ::cudaErrorUnknown		* ::cudaErrorUnknown
	* \notefnerr		* \notefnerr
	*		*
	* \sa		* \sa
	skipping to change at line 142		skipping to change at line 162
	* ::cudaGraphicsMapResources,		* ::cudaGraphicsMapResources,
	* ::cudaGraphicsSubResourceGetMappedArray		* ::cudaGraphicsSubResourceGetMappedArray
	*/		*/
	extern __host__ cudaError_t CUDARTAPI cudaGraphicsGLRegisterImage(struct cu daGraphicsResource **resource, GLuint image, GLenum target, unsigned int fl ags);		extern __host__ cudaError_t CUDARTAPI cudaGraphicsGLRegisterImage(struct cu daGraphicsResource **resource, GLuint image, GLenum target, unsigned int fl ags);
	/**		/**
	* \brief Registers an OpenGL buffer object		* \brief Registers an OpenGL buffer object
	*		*
	* Registers the buffer object specified by \p buffer for access by		* Registers the buffer object specified by \p buffer for access by
	* CUDA. A handle to the registered object is returned as \p		* CUDA. A handle to the registered object is returned as \p
	* resource. The map flags \p flags specify the intended usage,		* resource. The register flags \p flags specify the intended usage,
	* as follows:		* as follows:
	*		*
	* - ::cudaGraphicsMapFlagsNone: Specifies no hints about how this		* - ::cudaGraphicsRegisterFlagsNone: Specifies no hints about how this
	* resource will be used. It is therefore assumed that this resource will be		* resource will be used. It is therefore assumed that this resource will be
	* read from and written to by CUDA. This is the default value.		* read from and written to by CUDA. This is the default value.
	* - ::cudaGraphicsMapFlagsReadOnly: Specifies that CUDA		* - ::cudaGraphicsRegisterFlagsReadOnly: Specifies that CUDA
	* will not write to this resource.		* will not write to this resource.
	* - ::cudaGraphicsMapFlagsWriteDiscard: Specifies that		* - ::cudaGraphicsRegisterFlagsWriteDiscard: Specifies that
	* CUDA will not read from this resource and will write over the		* CUDA will not read from this resource and will write over the
	* entire contents of the resource, so none of the data previously		* entire contents of the resource, so none of the data previously
	* stored in the resource will be preserved.		* stored in the resource will be preserved.
	*		*
	* \param resource - Pointer to the returned object handle		* \param resource - Pointer to the returned object handle
	* \param buffer - name of buffer object to be registered		* \param buffer - name of buffer object to be registered
	* \param flags - Map flags		* \param flags - Register flags
	*		*
	* \return		* \return
	* ::cudaSuccess,		* ::cudaSuccess,
	* ::cudaErrorInvalidDevice,		* ::cudaErrorInvalidDevice,
	* ::cudaErrorInvalidValue,		* ::cudaErrorInvalidValue,
	* ::cudaErrorInvalidResourceHandle,		* ::cudaErrorInvalidResourceHandle,
	* ::cudaErrorUnknown		* ::cudaErrorUnknown
	* \notefnerr		* \notefnerr
	*		*
	* \sa		* \sa
	skipping to change at line 199		skipping to change at line 219
	* \return		* \return
	* ::cudaSuccess		* ::cudaSuccess
	* \notefnerr		* \notefnerr
	*		*
	* \sa WGL_NV_gpu_affinity, ::cudaGLSetGLDevice		* \sa WGL_NV_gpu_affinity, ::cudaGLSetGLDevice
	*/		*/
	extern __host__ cudaError_t CUDARTAPI cudaWGLGetDevice(int *device, HGPUNV hGpu);		extern __host__ cudaError_t CUDARTAPI cudaWGLGetDevice(int *device, HGPUNV hGpu);
	#endif		#endif
	/**		/**
			* \defgroup CUDART_OPENGL_DEPRECATED OpenGL Interoperability [DEPRECATED]
			* This section describes deprecated OpenGL interoperability functionality.
			*
			* @{
			*/
			/**
	* CUDA GL Map Flags		* CUDA GL Map Flags
	*/		*/
	enum cudaGLMapFlags		enum cudaGLMapFlags
	{		{
	cudaGLMapFlagsNone = 0, /**< Default; Assume resource can be rea d/written */		cudaGLMapFlagsNone = 0, /**< Default; Assume resource can be rea d/written */
	cudaGLMapFlagsReadOnly = 1, /**< CUDA kernels will not write to this resource */		cudaGLMapFlagsReadOnly = 1, /**< CUDA kernels will not write to this resource */
	cudaGLMapFlagsWriteDiscard = 2, /**< CUDA kernels will only write to and will not read from this resource */		cudaGLMapFlagsWriteDiscard = 2 /**< CUDA kernels will only write to and will not read from this resource */
	};		};
	/**		/**
	* \defgroup CUDART_OPENGL_DEPRECATED OpenGL Interoperability [DEPRECATED]
	* This section describes deprecated OpenGL interoperability functionality.
	*
	* @{
	*/
	/**
	* \brief Registers a buffer object for access by CUDA		* \brief Registers a buffer object for access by CUDA
	*		*
	* \deprecated This function is deprecated as of Cuda 3.0.		* \deprecated This function is deprecated as of Cuda 3.0.
	*		*
	* Registers the buffer object of ID \p bufObj for access by		* Registers the buffer object of ID \p bufObj for access by
	* CUDA. This function must be called before CUDA can map the buffer		* CUDA. This function must be called before CUDA can map the buffer
	* object. The OpenGL context used to create the buffer, or another		* object. The OpenGL context used to create the buffer, or another
	* context from the same share group, must be bound to the current		* context from the same share group, must be bound to the current
	* thread when this is called.		* thread when this is called.
	*		*
End of changes. 23 change blocks.
	56 lines changed or deleted		74 lines changed or added

	cuda_runtime.h		cuda_runtime.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(__CUDA_RUNTIME_H__)		#if !defined(__CUDA_RUNTIME_H__)
	#define __CUDA_RUNTIME_H__		#define __CUDA_RUNTIME_H__
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	skipping to change at line 146		skipping to change at line 160
	* ::cudaErrorLaunchFailure,		* ::cudaErrorLaunchFailure,
	* ::cudaErrorMemoryAllocation		* ::cudaErrorMemoryAllocation
	* \notefnerr		* \notefnerr
	*		*
	* \sa \ref ::cudaEventCreate(cudaEvent_t*) "cudaEventCreate (C API)",		* \sa \ref ::cudaEventCreate(cudaEvent_t*) "cudaEventCreate (C API)",
	* ::cudaEventCreateWithFlags, ::cudaEventRecord, ::cudaEventQuery,		* ::cudaEventCreateWithFlags, ::cudaEventRecord, ::cudaEventQuery,
	* ::cudaEventSynchronize, ::cudaEventDestroy, ::cudaEventElapsedTime,		* ::cudaEventSynchronize, ::cudaEventDestroy, ::cudaEventElapsedTime,
	* ::cudaStreamWaitEvent		* ::cudaStreamWaitEvent
	*/		*/
	static __inline__ __host__ cudaError_t cudaEventCreate(		static __inline__ __host__ cudaError_t cudaEventCreate(
	cudaEvent_t *event,		cudaEvent_t *event,
	unsigned int flags		unsigned int flags
	)		)
	{		{
	return cudaEventCreateWithFlags(event, cudaEventDefault);		return cudaEventCreateWithFlags(event, flags);
	}		}
	/**		/**
	* \brief \hl Allocates page-locked memory on the host		* \brief \hl Allocates page-locked memory on the host
	*		*
	* Allocates \p size bytes of host memory that is page-locked and accessibl e		* Allocates \p size bytes of host memory that is page-locked and accessibl e
	* to the device. The driver tracks the virtual memory ranges allocated wit h		* to the device. The driver tracks the virtual memory ranges allocated wit h
	* this function and automatically accelerates calls to functions such as		* this function and automatically accelerates calls to functions such as
	* ::cudaMemcpy(). Since the memory can be accessed directly by the device, it		* ::cudaMemcpy(). Since the memory can be accessed directly by the device, it
	* can be read or written with much higher bandwidth than pageable memory		* can be read or written with much higher bandwidth than pageable memory
	skipping to change at line 209		skipping to change at line 223
	* \return		* \return
	* ::cudaSuccess,		* ::cudaSuccess,
	* ::cudaErrorMemoryAllocation		* ::cudaErrorMemoryAllocation
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cudaSetDeviceFlags,		* \sa ::cudaSetDeviceFlags,
	* \ref ::cudaMallocHost(void**, size_t) "cudaMallocHost (C API)",		* \ref ::cudaMallocHost(void**, size_t) "cudaMallocHost (C API)",
	* ::cudaFreeHost, ::cudaHostAlloc		* ::cudaFreeHost, ::cudaHostAlloc
	*/		*/
	static __inline__ __host__ cudaError_t cudaMallocHost(		static __inline__ __host__ cudaError_t cudaMallocHost(
	void **ptr,		void **ptr,
	size_t size,		size_t size,
	unsigned int flags		unsigned int flags
	)		)
	{		{
	return cudaHostAlloc(ptr, size, flags);		return cudaHostAlloc(ptr, size, flags);
	}		}
	template<class T>		template<class T>
	__inline__ __host__ cudaError_t cudaHostAlloc(		__inline__ __host__ cudaError_t cudaHostAlloc(
	T **ptr,		T **ptr,
	size_t size,		size_t size,
	unsigned int flags		unsigned int flags
	)		)
	{		{
	return cudaHostAlloc((void**)(void*)ptr, size, flags);		return cudaHostAlloc((void**)(void*)ptr, size, flags);
	}		}
	template<class T>		template<class T>
	__inline__ __host__ cudaError_t cudaHostGetDevicePointer(		__inline__ __host__ cudaError_t cudaHostGetDevicePointer(
	T **pDevice,		T **pDevice,
	void *pHost,		void *pHost,
	unsigned int flags		unsigned int flags
	)		)
	{		{
	return cudaHostGetDevicePointer((void**)(void*)pDevice, pHost, flags);		return cudaHostGetDevicePointer((void**)(void*)pDevice, pHost, flags);
	}		}
	template<class T>		template<class T>
	__inline__ __host__ cudaError_t cudaMalloc(		__inline__ __host__ cudaError_t cudaMalloc(
	T **devPtr,		T **devPtr,
	size_t size		size_t size
	)		)
	skipping to change at line 888		skipping to change at line 902
	* \ref ::cudaFuncGetAttributes(struct cudaFuncAttributes*, T*) "cudaFuncGe tAttributes (C++ API)",		* \ref ::cudaFuncGetAttributes(struct cudaFuncAttributes*, T*) "cudaFuncGe tAttributes (C++ API)",
	* \ref ::cudaLaunch(const char*) "cudaLaunch (C API)",		* \ref ::cudaLaunch(const char*) "cudaLaunch (C API)",
	* ::cudaSetDoubleForDevice,		* ::cudaSetDoubleForDevice,
	* ::cudaSetDoubleForHost,		* ::cudaSetDoubleForHost,
	* \ref ::cudaSetupArgument(T, size_t) "cudaSetupArgument (C++ API)",		* \ref ::cudaSetupArgument(T, size_t) "cudaSetupArgument (C++ API)",
	* ::cudaThreadGetCacheConfig,		* ::cudaThreadGetCacheConfig,
	* ::cudaThreadSetCacheConfig		* ::cudaThreadSetCacheConfig
	*/		*/
	template<class T>		template<class T>
	__inline__ __host__ cudaError_t cudaFuncSetCacheConfig(		__inline__ __host__ cudaError_t cudaFuncSetCacheConfig(
	T *func,		T *func,
	enum cudaFuncCache cacheConfig		enum cudaFuncCache cacheConfig
	)		)
	{		{
	return cudaFuncSetCacheConfig((const char*)func, cacheConfig);		return cudaFuncSetCacheConfig((const char*)func, cacheConfig);
	}		}
	/**		/**
	* \brief \hl Launches a device function		* \brief \hl Launches a device function
	*		*
	* Launches the function \p entry on the device. The parameter \p entry can		* Launches the function \p entry on the device. The parameter \p entry can
	* either be a function that executes on the device, or it can be a charact er		* either be a function that executes on the device, or it can be a charact er
	skipping to change at line 973		skipping to change at line 987
	* \ref ::cudaFuncSetCacheConfig(T*, enum cudaFuncCache) "cudaFuncSetCacheC onfig (C++ API)",		* \ref ::cudaFuncSetCacheConfig(T*, enum cudaFuncCache) "cudaFuncSetCacheC onfig (C++ API)",
	* \ref ::cudaFuncGetAttributes(struct cudaFuncAttributes*, const char*) "c udaFuncGetAttributes (C API)",		* \ref ::cudaFuncGetAttributes(struct cudaFuncAttributes*, const char*) "c udaFuncGetAttributes (C API)",
	* \ref ::cudaLaunch(T*) "cudaLaunch (C++ API)",		* \ref ::cudaLaunch(T*) "cudaLaunch (C++ API)",
	* ::cudaSetDoubleForDevice,		* ::cudaSetDoubleForDevice,
	* ::cudaSetDoubleForHost,		* ::cudaSetDoubleForHost,
	* \ref ::cudaSetupArgument(T, size_t) "cudaSetupArgument (C++ API)"		* \ref ::cudaSetupArgument(T, size_t) "cudaSetupArgument (C++ API)"
	*/		*/
	template<class T>		template<class T>
	__inline__ __host__ cudaError_t cudaFuncGetAttributes(		__inline__ __host__ cudaError_t cudaFuncGetAttributes(
	struct cudaFuncAttributes *attr,		struct cudaFuncAttributes *attr,
	T *entry		T *entry
	)		)
	{		{
	return cudaFuncGetAttributes(attr, (const char*)entry);		return cudaFuncGetAttributes(attr, (const char*)entry);
	}		}
	/**		/**
	* \brief \hl Binds an array to a surface		* \brief \hl Binds an array to a surface
	*		*
	* Binds the CUDA array \p array to the surface reference \p surf.		* Binds the CUDA array \p array to the surface reference \p surf.
	* \p desc describes how the memory is interpreted when dealing with		* \p desc describes how the memory is interpreted when dealing with
	skipping to change at line 1001		skipping to change at line 1015
	* ::cudaSuccess,		* ::cudaSuccess,
	* ::cudaErrorInvalidValue,		* ::cudaErrorInvalidValue,
	* ::cudaErrorInvalidSurface		* ::cudaErrorInvalidSurface
	* \notefnerr		* \notefnerr
	*		*
	* \sa \ref ::cudaBindSurfaceToArray(const struct surfaceReference*, const struct cudaArray*, const struct cudaChannelFormatDesc*) "cudaBindSurfaceToA rray (C API)",		* \sa \ref ::cudaBindSurfaceToArray(const struct surfaceReference*, const struct cudaArray*, const struct cudaChannelFormatDesc*) "cudaBindSurfaceToA rray (C API)",
	* \ref ::cudaBindSurfaceToArray(const struct surface<T, dim>&, const struc t cudaArray*) "cudaBindSurfaceToArray (C++ API, inherited channel descripto r)"		* \ref ::cudaBindSurfaceToArray(const struct surface<T, dim>&, const struc t cudaArray*) "cudaBindSurfaceToArray (C++ API, inherited channel descripto r)"
	*/		*/
	template<class T, int dim>		template<class T, int dim>
	__inline__ __host__ cudaError_t cudaBindSurfaceToArray(		__inline__ __host__ cudaError_t cudaBindSurfaceToArray(
	const struct surface<T, dim> &surf,		const struct surface<T, dim> &surf,
	const struct cudaArray *array,		const struct cudaArray *array,
	const struct cudaChannelFormatDesc &desc		const struct cudaChannelFormatDesc &desc
	)		)
	{		{
	return cudaBindSurfaceToArray(&surf, array, &desc);		return cudaBindSurfaceToArray(&surf, array, &desc);
	}		}
	/**		/**
	* \brief \hl Binds an array to a surface		* \brief \hl Binds an array to a surface
	*		*
	* Binds the CUDA array \p array to the surface reference \p surf.		* Binds the CUDA array \p array to the surface reference \p surf.
	* The channel descriptor is inherited from the CUDA array. Any CUDA array		* The channel descriptor is inherited from the CUDA array. Any CUDA array
	skipping to change at line 1030		skipping to change at line 1044
	* ::cudaSuccess,		* ::cudaSuccess,
	* ::cudaErrorInvalidValue,		* ::cudaErrorInvalidValue,
	* ::cudaErrorInvalidSurface		* ::cudaErrorInvalidSurface
	* \notefnerr		* \notefnerr
	*		*
	* \sa \ref ::cudaBindSurfaceToArray(const struct surfaceReference*, const struct cudaArray*, const struct cudaChannelFormatDesc*) "cudaBindSurfaceToA rray (C API)",		* \sa \ref ::cudaBindSurfaceToArray(const struct surfaceReference*, const struct cudaArray*, const struct cudaChannelFormatDesc*) "cudaBindSurfaceToA rray (C API)",
	* \ref ::cudaBindSurfaceToArray(const struct surface<T, dim>&, const struc t cudaArray*, const struct cudaChannelFormatDesc&) "cudaBindSurfaceToArray (C++ API)"		* \ref ::cudaBindSurfaceToArray(const struct surface<T, dim>&, const struc t cudaArray*, const struct cudaChannelFormatDesc&) "cudaBindSurfaceToArray (C++ API)"
	*/		*/
	template<class T, int dim>		template<class T, int dim>
	__inline__ __host__ cudaError_t cudaBindSurfaceToArray(		__inline__ __host__ cudaError_t cudaBindSurfaceToArray(
	const struct surface<T, dim> &surf,		const struct surface<T, dim> &surf,
	const struct cudaArray *array		const struct cudaArray *array
	)		)
	{		{
	struct cudaChannelFormatDesc desc;		struct cudaChannelFormatDesc desc;
	cudaError_t err = cudaGetChannelDesc(&desc, array);		cudaError_t err = cudaGetChannelDesc(&desc, array);
	return err == cudaSuccess ? cudaBindSurfaceToArray(surf, array, desc) : e rr;		return err == cudaSuccess ? cudaBindSurfaceToArray(surf, array, desc) : e rr;
	}		}
	#endif /* __CUDACC__ */		#endif /* __CUDACC__ */
End of changes. 14 change blocks.
	43 lines changed or deleted		56 lines changed or added

	cuda_surface_types.h		cuda_surface_types.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(__CUDA_SURFACE_TYPES_H__)		#if !defined(__CUDA_SURFACE_TYPES_H__)
	#define __CUDA_SURFACE_TYPES_H__		#define __CUDA_SURFACE_TYPES_H__
	#if defined(__cplusplus) && defined(__CUDACC__)		#if defined(__cplusplus) && defined(__CUDACC__)
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
End of changes. 6 change blocks.
	28 lines changed or deleted		41 lines changed or added

	cuda_texture_types.h		cuda_texture_types.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(__CUDA_TEXTURE_TYPES_H__)		#if !defined(__CUDA_TEXTURE_TYPES_H__)
	#define __CUDA_TEXTURE_TYPES_H__		#define __CUDA_TEXTURE_TYPES_H__
	#if defined(__cplusplus) && defined(__CUDACC__)		#if defined(__cplusplus) && defined(__CUDACC__)
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	skipping to change at line 60		skipping to change at line 74
	#include "host_defines.h"		#include "host_defines.h"
	#include "texture_types.h"		#include "texture_types.h"
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	/*TEXTURE_TYPE*/		/*TEXTURE_TYPE*/
	template<class T, int dim = 1, enum cudaTextureReadMode mode = cudaReadMode ElementType>		template<class T, int texType = cudaTextureType1D, enum cudaTextureReadMode mode = cudaReadModeElementType>
	struct texture : public textureReference		struct texture : public textureReference
	{		{
	__host__ texture(int norm = 0,		__host__ texture(int norm = 0,
	enum cudaTextureFilterMode fMode = cudaFilterModePoint,		enum cudaTextureFilterMode fMode = cudaFilterModePoint,
	enum cudaTextureAddressMode aMode = cudaAddressModeClamp )		enum cudaTextureAddressMode aMode = cudaAddressModeClamp )
	{		{
	normalized = norm;		normalized = norm;
	filterMode = fMode;		filterMode = fMode;
	addressMode[0] = aMode;		addressMode[0] = aMode;
	addressMode[1] = aMode;		addressMode[1] = aMode;
	addressMode[2] = aMode;		addressMode[2] = aMode;
	channelDesc = cudaCreateChannelDesc<T>();		channelDesc = cudaCreateChannelDesc<T>();
			sRGB = 0;
	}		}
	__host__ texture(int norm,		__host__ texture(int norm,
	enum cudaTextureFilterMode fMode,		enum cudaTextureFilterMode fMode,
	enum cudaTextureAddressMode aMode,		enum cudaTextureAddressMode aMode,
	struct cudaChannelFormatDesc desc)		struct cudaChannelFormatDesc desc)
	{		{
	normalized = norm;		normalized = norm;
	filterMode = fMode;		filterMode = fMode;
	addressMode[0] = aMode;		addressMode[0] = aMode;
	addressMode[1] = aMode;		addressMode[1] = aMode;
	addressMode[2] = aMode;		addressMode[2] = aMode;
	channelDesc = desc;		channelDesc = desc;
			sRGB = 0;
	}		}
	};		};
	#endif /* __cplusplus && __CUDACC__ */		#endif /* __cplusplus && __CUDACC__ */
	#endif /* !__CUDA_TEXTURE_TYPES_H__ */		#endif /* !__CUDA_TEXTURE_TYPES_H__ */
End of changes. 9 change blocks.
	29 lines changed or deleted		44 lines changed or added

	cuda_vdpau_interop.h		cuda_vdpau_interop.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(__CUDA_VDPAU_INTEROP_H__)		#if !defined(__CUDA_VDPAU_INTEROP_H__)
	#define __CUDA_VDPAU_INTEROP_H__		#define __CUDA_VDPAU_INTEROP_H__
	#include "builtin_types.h"		#include "builtin_types.h"
	#include "host_defines.h"		#include "host_defines.h"
	#include <vdpau/vdpau.h>		#include <vdpau/vdpau.h>
	skipping to change at line 75		skipping to change at line 89
	*		*
	* \return		* \return
	* ::cudaSuccess		* ::cudaSuccess
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cudaVDPAUSetVDPAUDevice		* \sa ::cudaVDPAUSetVDPAUDevice
	*/		*/
	extern __host__ cudaError_t CUDARTAPI cudaVDPAUGetDevice(int *device, VdpDe vice vdpDevice, VdpGetProcAddress *vdpGetProcAddress);		extern __host__ cudaError_t CUDARTAPI cudaVDPAUGetDevice(int *device, VdpDe vice vdpDevice, VdpGetProcAddress *vdpGetProcAddress);
	/**		/**
	* \brief Sets the CUDA device for use with VDPAU interoperability		* \brief Sets a CUDA device to use VDPAU interoperability
	*		*
	* Records \p device as the device on which the active host thread executes		* Records \p vdpDevice as the VdpDevice for VDPAU interoperability
	* the device code. Records the thread as using VDPAU interoperability.		* with the CUDA device \p device and sets \p device as the current
	* If the host thread has already initialized the CUDA runtime by		* device for the calling host thread.
	* calling non-device management runtime functions or if there exists a CUD		*
	A		* If \p device has already been initialized then this call will fail
	* driver context active on the host thread, then this call returns		* with the error ::cudaErrorSetOnActiveProcess. In this case it is
	* ::cudaErrorSetOnActiveProcess.		* necessary to reset \p device using ::cudaDeviceReset() before
			* VDPAU interoperability on \p device may be enabled.
	*		*
	* \param device - Device to use for VDPAU interoperability		* \param device - Device to use for VDPAU interoperability
	* \param vdpDevice - The VdpDevice to interoperate with		* \param vdpDevice - The VdpDevice to interoperate with
	* \param vdpGetProcAddress - VDPAU's VdpGetProcAddress function pointer		* \param vdpGetProcAddress - VDPAU's VdpGetProcAddress function pointer
	*		*
	* \return		* \return
	* ::cudaSuccess,		* ::cudaSuccess,
	* ::cudaErrorInvalidDevice,		* ::cudaErrorInvalidDevice,
	* ::cudaErrorSetOnActiveProcess		* ::cudaErrorSetOnActiveProcess
	* \notefnerr		* \notefnerr
	*		*
	* \sa ::cudaGraphicsVDPAURegisterVideoSurface,		* \sa ::cudaGraphicsVDPAURegisterVideoSurface,
	* ::cudaGraphicsVDPAURegisterOutputSurface		* ::cudaGraphicsVDPAURegisterOutputSurface,
			* ::cudaDeviceReset
	*/		*/
	extern __host__ cudaError_t CUDARTAPI cudaVDPAUSetVDPAUDevice(int device, V dpDevice vdpDevice, VdpGetProcAddress *vdpGetProcAddress);		extern __host__ cudaError_t CUDARTAPI cudaVDPAUSetVDPAUDevice(int device, V dpDevice vdpDevice, VdpGetProcAddress *vdpGetProcAddress);
	/**		/**
	* \brief Register a VdpVideoSurface object		* \brief Register a VdpVideoSurface object
	*		*
	* Registers the VdpVideoSurface specified by \p vdpSurface for access by C UDA.		* Registers the VdpVideoSurface specified by \p vdpSurface for access by C UDA.
	* A handle to the registered object is returned as \p resource.		* A handle to the registered object is returned as \p resource.
	* The surface's intended usage is specified using \p flags, as follows:		* The surface's intended usage is specified using \p flags, as follows:
	*		*
End of changes. 9 change blocks.
	37 lines changed or deleted		52 lines changed or added

	cufft.h		cufft.h
	/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		/* Copyright 2005-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* The source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.		*
	*		* The Licensed Deliverables contained herein are PROPRIETARY and
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* CONFIDENTIAL to NVIDIA and are being provided under the terms and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* conditions of a form of NVIDIA software license agreement by and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* between NVIDIA and Licensee ("License Agreement") or electronically
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* accepted by Licensee. Notwithstanding any terms or conditions to
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* the contrary in the License Agreement, reproduction or disclosure
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* of the Licensed Deliverables to any third party without the express
	,		* written consent of NVIDIA is prohibited.
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		*
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* OR PERFORMANCE OF THIS SOURCE CODE.		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. THEY ARE
	*		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* U.S. Government End Users. This source code is a "commercial item" as		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* "commercial computer software" and "commercial computer software		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* and is provided to the U.S. Government only as a commercial end item.		* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
	* source code with only those rights set forth herein.		* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
	*		* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
	* Any use of this source code in individual and commercial software must		* OF THESE LICENSED DELIVERABLES.
	* include, in the user documentation and internal comments to the code,		*
	* the above Disclaimer and U.S. Government End Users Notice.		* U.S. Government End Users. These Licensed Deliverables are a
	*/		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
			* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
			*/
	/*		/*
	* cufft.h		* cufft.h
	* Public header file for the NVIDIA Cuda FFT library (CUFFT)		* Public header file for the NVIDIA Cuda FFT library (CUFFT)
	*/		*/
	#ifndef _CUFFT_H_		#ifndef _CUFFT_H_
	#define _CUFFT_H_		#define _CUFFT_H_
	#include <stdio.h>		#include <stdio.h>
	#include "cuComplex.h"		#include "cuComplex.h"
	skipping to change at line 150		skipping to change at line 164
	int nx, int ny,		int nx, int ny,
	cufftType type);		cufftType type);
	cufftResult CUFFTAPI cufftPlan3d(cufftHandle *plan,		cufftResult CUFFTAPI cufftPlan3d(cufftHandle *plan,
	int nx, int ny, int nz,		int nx, int ny, int nz,
	cufftType type);		cufftType type);
	cufftResult CUFFTAPI cufftPlanMany(cufftHandle *plan,		cufftResult CUFFTAPI cufftPlanMany(cufftHandle *plan,
	int rank,		int rank,
	int *n,		int *n,
	int *inembed, int istride, int idist,		int *inembed, int istride, int idist,
	// Unused: pass "NULL, 1, 0"		int *onembed, int ostride, int odist,
	int *onembed, int ostride, int odist,
	// Unused: pass "NULL, 1, 0"
	cufftType type,		cufftType type,
	int batch);		int batch);
	cufftResult CUFFTAPI cufftDestroy(cufftHandle plan);		cufftResult CUFFTAPI cufftDestroy(cufftHandle plan);
	cufftResult CUFFTAPI cufftExecC2C(cufftHandle plan,		cufftResult CUFFTAPI cufftExecC2C(cufftHandle plan,
	cufftComplex *idata,		cufftComplex *idata,
	cufftComplex *odata,		cufftComplex *odata,
	int direction);		int direction);
	skipping to change at line 188		skipping to change at line 202
	cufftResult CUFFTAPI cufftExecZ2D(cufftHandle plan,		cufftResult CUFFTAPI cufftExecZ2D(cufftHandle plan,
	cufftDoubleComplex *idata,		cufftDoubleComplex *idata,
	cufftDoubleReal *odata);		cufftDoubleReal *odata);
	cufftResult CUFFTAPI cufftSetStream(cufftHandle plan,		cufftResult CUFFTAPI cufftSetStream(cufftHandle plan,
	cudaStream_t stream);		cudaStream_t stream);
	cufftResult CUFFTAPI cufftSetCompatibilityMode(cufftHandle plan,		cufftResult CUFFTAPI cufftSetCompatibilityMode(cufftHandle plan,
	cufftCompatibility mode);		cufftCompatibility mode);
			cufftResult CUFFTAPI cufftGetVersion(int *version);
	#ifdef __cplusplus		#ifdef __cplusplus
	}		}
	#endif		#endif
	#endif /* _CUFFT_H_ */		#endif /* _CUFFT_H_ */
End of changes. 3 change blocks.
	39 lines changed or deleted		53 lines changed or added

	curand.h		curand.h
	/*
	* Copyright 2010-2010 NVIDIA Corporation. All rights reserved.		/* Copyright 2010-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* The source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.		*
	*		* The Licensed Deliverables contained herein are PROPRIETARY and
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* CONFIDENTIAL to NVIDIA and are being provided under the terms and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* conditions of a form of NVIDIA software license agreement by and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* between NVIDIA and Licensee ("License Agreement") or electronically
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* accepted by Licensee. Notwithstanding any terms or conditions to
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* the contrary in the License Agreement, reproduction or disclosure
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* of the Licensed Deliverables to any third party without the express
	,		* written consent of NVIDIA is prohibited.
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		*
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* OR PERFORMANCE OF THIS SOURCE CODE.		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. THEY ARE
	*		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* U.S. Government End Users. This source code is a "commercial item" as		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* "commercial computer software" and "commercial computer software		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* and is provided to the U.S. Government only as a commercial end item.		* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
	* source code with only those rights set forth herein.		* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
	*		* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
	* Any use of this source code in individual and commercial software must		* OF THESE LICENSED DELIVERABLES.
	* include, in the user documentation and internal comments to the code,		*
	* the above Disclaimer and U.S. Government End Users Notice.		* U.S. Government End Users. These Licensed Deliverables are a
	*/		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
			* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
			*/
	#if !defined(CURAND_H_)		#if !defined(CURAND_H_)
	#define CURAND_H_		#define CURAND_H_
	/**		/**
	* \file		* \file
	* \name CURAND Host API		* \name CURAND Host API
	* \author NVIDIA Corporation		* \author NVIDIA Corporation
	*/		*/
	skipping to change at line 97		skipping to change at line 111
	/**		/**
	CURAND function call status types		CURAND function call status types
	*/		*/
	typedef enum curandStatus curandStatus_t;		typedef enum curandStatus curandStatus_t;
	/**		/**
	* CURAND generator types		* CURAND generator types
	*/		*/
	enum curandRngType {		enum curandRngType {
			CURAND_RNG_TEST = 0,
	CURAND_RNG_PSEUDO_DEFAULT = 100, ///< Default pseudorandom generator		CURAND_RNG_PSEUDO_DEFAULT = 100, ///< Default pseudorandom generator
	CURAND_RNG_PSEUDO_XORWOW = 101, ///< XORWOW pseudorandom generator		CURAND_RNG_PSEUDO_XORWOW = 101, ///< XORWOW pseudorandom generator
	CURAND_RNG_QUASI_DEFAULT = 200, ///< Default quasirandom generator		CURAND_RNG_QUASI_DEFAULT = 200, ///< Default quasirandom generator
	CURAND_RNG_QUASI_SOBOL32 = 201, ///< Sobol32 quasirandom generator		CURAND_RNG_QUASI_SOBOL32 = 201, ///< Sobol32 quasirandom generator
			CURAND_RNG_QUASI_SCRAMBLED_SOBOL32 = 202, ///< Scrambled Sobol32 quasi
			random generator
			CURAND_RNG_QUASI_SOBOL64 = 203, ///< Sobol64 quasirandom generator
			CURAND_RNG_QUASI_SCRAMBLED_SOBOL64 = 204, ///< Scrambled Sobol64 quasi
			random generator
	};		};
	/**		/**
	* CURAND generator types		* CURAND generator types
	*/		*/
	typedef enum curandRngType curandRngType_t;		typedef enum curandRngType curandRngType_t;
	/**		/**
	* CURAND orderings of results in memory		* CURAND orderings of results in memory
	*/		*/
	enum curandOrdering {		enum curandOrdering {
	CURAND_ORDERING_PSEUDO_BEST = 100, ///< Best ordering for pseudorandom results		CURAND_ORDERING_PSEUDO_BEST = 100, ///< Best ordering for pseudorandom results
	CURAND_ORDERING_PSEUDO_DEFAULT = 101, ///< Specific default 4096 thread sequence for pseudorandom results		CURAND_ORDERING_PSEUDO_DEFAULT = 101, ///< Specific default 4096 thread sequence for pseudorandom results
			CURAND_ORDERING_PSEUDO_SEEDED = 102, ///< Specific seeding pattern for fast lower quality pseudorandom results
	CURAND_ORDERING_QUASI_DEFAULT = 201, ///< Specific n-dimensional orderi ng for quasirandom results		CURAND_ORDERING_QUASI_DEFAULT = 201, ///< Specific n-dimensional orderi ng for quasirandom results
	};		};
	/**		/**
	* CURAND orderings of results in memory		* CURAND orderings of results in memory
	*/		*/
	typedef enum curandOrdering curandOrdering_t;		typedef enum curandOrdering curandOrdering_t;
	/**		/**
	* CURAND choice of direction vector set		* CURAND choice of direction vector set
	*/		*/
	enum curandDirectionVectorSet {		enum curandDirectionVectorSet {
	CURAND_DIRECTION_VECTORS_32_JOEKUO6 = 101, ///< Specific set of 32-bit direction vectors generated from polynomials recommended by S. Joe and F. Y . Kuo, for up to 20,000 dimensions		CURAND_DIRECTION_VECTORS_32_JOEKUO6 = 101, ///< Specific set of 32-bit direction vectors generated from polynomials recommended by S. Joe and F. Y . Kuo, for up to 20,000 dimensions
			CURAND_SCRAMBLED_DIRECTION_VECTORS_32_JOEKUO6 = 102, ///< Specific set
			of 32-bit direction vectors generated from polynomials recommended by S. Jo
			e and F. Y. Kuo, for up to 20,000 dimensions, and scrambled
			CURAND_DIRECTION_VECTORS_64_JOEKUO6 = 103, ///< Specific set of 64-bit
			direction vectors generated from polynomials recommended by S. Joe and F. Y
			. Kuo, for up to 20,000 dimensions
			CURAND_SCRAMBLED_DIRECTION_VECTORS_64_JOEKUO6 = 104, ///< Specific set
			of 64-bit direction vectors generated from polynomials recommended by S. Jo
			e and F. Y. Kuo, for up to 20,000 dimensions, and scrambled
	};		};
	/**		/**
	* CURAND choice of direction vector set		* CURAND choice of direction vector set
	*/		*/
	typedef enum curandDirectionVectorSet curandDirectionVectorSet_t;		typedef enum curandDirectionVectorSet curandDirectionVectorSet_t;
	/**		/**
	* CURAND array of 32-bit direction vectors		* CURAND array of 32-bit direction vectors
	*/		*/
	typedef unsigned int curandDirectionVectors32_t[32];		typedef unsigned int curandDirectionVectors32_t[32];
			/**
			* CURAND array of 64-bit direction vectors
			*/
			typedef unsigned long long curandDirectionVectors64_t[64];
	/**		/**
	* CURAND generator (opaque)		* CURAND generator (opaque)
	*/		*/
	struct curandGenerator_st;		struct curandGenerator_st;
	/**		/**
	* CURAND generator		* CURAND generator
	*/		*/
	typedef struct curandGenerator_st *curandGenerator_t;		typedef struct curandGenerator_st *curandGenerator_t;
	skipping to change at line 164		skipping to change at line 191
	* \brief Create new random number generator.		* \brief Create new random number generator.
	*		*
	* Creates a new random number generator of type \p rng_type		* Creates a new random number generator of type \p rng_type
	* and returns it in \p *generator.		* and returns it in \p *generator.
	*		*
	* Legal values for \p rng_type are:		* Legal values for \p rng_type are:
	* - CURAND_RNG_PSEUDO_DEFAULT		* - CURAND_RNG_PSEUDO_DEFAULT
	* - CURAND_RNG_PSEUDO_XORWOW		* - CURAND_RNG_PSEUDO_XORWOW
	* - CURAND_RNG_QUASI_DEFAULT		* - CURAND_RNG_QUASI_DEFAULT
	* - CURAND_RNG_QUASI_SOBOL32		* - CURAND_RNG_QUASI_SOBOL32
			* - CURAND_RNG_QUASI_SCRAMBLED_SOBOL32
			* - CURAND_RNG_QUASI_SOBOL64
			* - CURAND_RNG_QUASI_SCRAMBLED_SOBOL64
	*		*
	* When \p rng_type is CURAND_RNG_PSEUDO_DEFAULT, the type chosen		* When \p rng_type is CURAND_RNG_PSEUDO_DEFAULT, the type chosen
	* is CURAND_RNG_PSEUDO_XORWOW. \n		* is CURAND_RNG_PSEUDO_XORWOW. \n
	* When \p rng_type is CURAND_RNG_QUASI_DEFAULT,		* When \p rng_type is CURAND_RNG_QUASI_DEFAULT,
	* the type chosen is CURAND_RNG_QUASI_SOBOL32.		* the type chosen is CURAND_RNG_QUASI_SOBOL32.
	*		*
	* The default values for \p rng_type = CURAND_RNG_PSEUDO_XORWOW are:		* The default values for \p rng_type = CURAND_RNG_PSEUDO_XORWOW are:
	* - \p seed = 0		* - \p seed = 0
	* - \p offset = 0		* - \p offset = 0
	* - \p ordering = CURAND_ORDERING_PSEUDO_DEFAULT		* - \p ordering = CURAND_ORDERING_PSEUDO_DEFAULT
	*		*
	* The default values for \p rng_type = CURAND_RNG_QUASI_SOBOL32 are:		* The default values for \p rng_type = CURAND_RNG_QUASI_SOBOL32 are:
	* - \p dimensions = 1		* - \p dimensions = 1
	* - \p offset = 0		* - \p offset = 0
	* - \p ordering = CURAND_ORDERING_QUASI_DEFAULT		* - \p ordering = CURAND_ORDERING_QUASI_DEFAULT
	*		*
			* The default values for \p rng_type = CURAND_RNG_QUASI_SOBOL64 are:
			* - \p dimensions = 1
			* - \p offset = 0
			* - \p ordering = CURAND_ORDERING_QUASI_DEFAULT
			*
			* The default values for \p rng_type = CURAND_RNG_QUASI_SCRAMBBLED_SOBOL32
			are:
			* - \p dimensions = 1
			* - \p offset = 0
			* - \p ordering = CURAND_ORDERING_QUASI_DEFAULT
			*
			* The default values for \p rng_type = CURAND_RNG_QUASI_SCRAMBLED_SOBOL64
			are:
			* - \p dimensions = 1
			* - \p offset = 0
			* - \p ordering = CURAND_ORDERING_QUASI_DEFAULT
			*
	* \param generator - Pointer to generator		* \param generator - Pointer to generator
	* \param rng_type - Type of generator to create		* \param rng_type - Type of generator to create
	*		*
	* \return		* \return
	* CURAND_STATUS_ALLOCATION_FAILED if memory could not be allocated \n		* CURAND_STATUS_ALLOCATION_FAILED if memory could not be allocated \n
	* CURAND_STATUS_INITIALIZATION_FAILED if there was a problem setting up th e GPU \n		* CURAND_STATUS_INITIALIZATION_FAILED if there was a problem setting up th e GPU \n
	* CURAND_STATUS_VERSION_MISMATCH if the header file version does not match the		* CURAND_STATUS_VERSION_MISMATCH if the header file version does not match the
	* dynamically linked library version \n		* dynamically linked library version \n
	* CURAND_STATUS_TYPE_ERROR if the value for \p rng_type is invalid \n		* CURAND_STATUS_TYPE_ERROR if the value for \p rng_type is invalid \n
	* CURAND_STATUS_SUCCESS if generator was created successfully \n		* CURAND_STATUS_SUCCESS if generator was created successfully \n
	skipping to change at line 221		skipping to change at line 266
	* The default values for \p rng_type = CURAND_RNG_PSEUDO_XORWOW are:		* The default values for \p rng_type = CURAND_RNG_PSEUDO_XORWOW are:
	* - \p seed = 0		* - \p seed = 0
	* - \p offset = 0		* - \p offset = 0
	* - \p ordering = CURAND_ORDERING_PSEUDO_DEFAULT		* - \p ordering = CURAND_ORDERING_PSEUDO_DEFAULT
	*		*
	* The default values for \p rng_type = CURAND_RNG_QUASI_SOBOL32 are:		* The default values for \p rng_type = CURAND_RNG_QUASI_SOBOL32 are:
	* - \p dimensions = 1		* - \p dimensions = 1
	* - \p offset = 0		* - \p offset = 0
	* - \p ordering = CURAND_ORDERING_QUASI_DEFAULT		* - \p ordering = CURAND_ORDERING_QUASI_DEFAULT
	*		*
			* The default values for \p rng_type = CURAND_RNG_QUASI_SOBOL64 are:
			* - \p dimensions = 1
			* - \p offset = 0
			* - \p ordering = CURAND_ORDERING_QUASI_DEFAULT
			*
			* The default values for \p rng_type = CURAND_RNG_QUASI_SCRAMBLED_SOBOL32
			are:
			* - \p dimensions = 1
			* - \p offset = 0
			* - \p ordering = CURAND_ORDERING_QUASI_DEFAULT
			*
			* The default values for \p rng_type = CURAND_RNG_QUASI_SCRAMBLED_SOBOL64
			are:
			* - \p dimensions = 1
			* - \p offset = 0
			* - \p ordering = CURAND_ORDERING_QUASI_DEFAULT
			*
	* \param generator - Pointer to generator		* \param generator - Pointer to generator
	* \param rng_type - Type of generator to create		* \param rng_type - Type of generator to create
	*		*
	* \return		* \return
	* CURAND_STATUS_ALLOCATION_FAILED if memory could not be allocated \n		* CURAND_STATUS_ALLOCATION_FAILED if memory could not be allocated \n
	* CURAND_STATUS_INITIALIZATION_FAILED if there was a problem setting up th e GPU \n		* CURAND_STATUS_INITIALIZATION_FAILED if there was a problem setting up th e GPU \n
	* CURAND_STATUS_VERSION_MISMATCH if the header file version does not match the		* CURAND_STATUS_VERSION_MISMATCH if the header file version does not match the
	* dynamically linked library version \n		* dynamically linked library version \n
	* CURAND_STATUS_TYPE_ERROR if the value for \p rng_type is invalid \n		* CURAND_STATUS_TYPE_ERROR if the value for \p rng_type is invalid \n
	* CURAND_STATUS_SUCCESS if generator was created successfully \n		* CURAND_STATUS_SUCCESS if generator was created successfully \n
	skipping to change at line 324		skipping to change at line 384
	*/		*/
	curandStatus_t CURANDAPI		curandStatus_t CURANDAPI
	curandSetGeneratorOffset(curandGenerator_t generator, unsigned long long of fset);		curandSetGeneratorOffset(curandGenerator_t generator, unsigned long long of fset);
	/**		/**
	* \brief Set the ordering of results of the pseudo or quasirandom number g enerator.		* \brief Set the ordering of results of the pseudo or quasirandom number g enerator.
	*		*
	* Set the ordering of results of the pseudo or quasirandom number generato r.		* Set the ordering of results of the pseudo or quasirandom number generato r.
	*		*
	* Legal values of \p order for pseudorandom generators are:		* Legal values of \p order for pseudorandom generators are:
	* - CURAND_ORDERING_PSEUDO_BEST
	* - CURAND_ORDERING_PSEUDO_DEFAULT		* - CURAND_ORDERING_PSEUDO_DEFAULT
			* - CURAND_ORDERING_PSEUDO_BEST
			* - CURAND_ORDERING_PSEUDO_SEEDED
	*		*
	* Legal values of \p order for quasirandom generators are:		* Legal values of \p order for quasirandom generators are:
	* - CURAND_ORDERING_QUASI_DEFAULT		* - CURAND_ORDERING_QUASI_DEFAULT
	*		*
	* \param generator - Generator to modify		* \param generator - Generator to modify
	* \param order - Ordering of results		* \param order - Ordering of results
	*		*
	* \return		* \return
	* CURAND_STATUS_NOT_INITIALIZED if the generator was never created \n		* CURAND_STATUS_NOT_INITIALIZED if the generator was never created \n
	* CURAND_STATUS_OUT_OF_RANGE if the ordering is not valid \n		* CURAND_STATUS_OUT_OF_RANGE if the ordering is not valid \n
	skipping to change at line 389		skipping to change at line 450
	* a previous kernel launch \n		* a previous kernel launch \n
	* CURAND_STATUS_LENGTH_NOT_MULTIPLE if the number of output samples is		* CURAND_STATUS_LENGTH_NOT_MULTIPLE if the number of output samples is
	* not a multiple of the quasirandom dimension \n		* not a multiple of the quasirandom dimension \n
	* CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason \n		* CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason \n
	* CURAND_STATUS_SUCCESS if the results were generated successfully \n		* CURAND_STATUS_SUCCESS if the results were generated successfully \n
	*/		*/
	curandStatus_t CURANDAPI		curandStatus_t CURANDAPI
	curandGenerate(curandGenerator_t generator, unsigned int *outputPtr, size_t num);		curandGenerate(curandGenerator_t generator, unsigned int *outputPtr, size_t num);
	/**		/**
			* \brief Generate 64-bit quasirandom numbers.
			*
			* Use \p generator to generate \p num 64-bit results into the device memor
			y at
			* \p outputPtr. The device memory must have been previously allocated and
			be
			* large enough to hold all the results. Launches are done with the stream
			* set using ::curandSetStream(), or the null stream if no stream has been
			set.
			*
			* Results are 64-bit values with every bit random.
			*
			* \param generator - Generator to use
			* \param outputPtr - Pointer to device memory to store CUDA-generated resu
			lts, or
			* Pointer to host memory to store CPU-generated resluts
			* \param num - Number of random 64-bit values to generate
			*
			* \return
			* CURAND_STATUS_NOT_INITIALIZED if the generator was never created \n
			* CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from
			* a previous kernel launch \n
			* CURAND_STATUS_LENGTH_NOT_MULTIPLE if the number of output samples is
			* not a multiple of the quasirandom dimension \n
			* CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason
			\n
			* CURAND_STATUS_SUCCESS if the results were generated successfully \n
			*/
			curandStatus_t CURANDAPI
			curandGenerateLongLong(curandGenerator_t generator, unsigned long long *out
			putPtr, size_t num);
			/**
	* \brief Generate uniformly distributed floats.		* \brief Generate uniformly distributed floats.
	*		*
	* Use \p generator to generate \p num float results into the device memory at		* Use \p generator to generate \p num float results into the device memory at
	* \p outputPtr. The device memory must have been previously allocated and be		* \p outputPtr. The device memory must have been previously allocated and be
	* large enough to hold all the results. Launches are done with the stream		* large enough to hold all the results. Launches are done with the stream
	* set using ::curandSetStream(), or the null stream if no stream has been set.		* set using ::curandSetStream(), or the null stream if no stream has been set.
	*		*
	* Results are 32-bit floating point values between \p 0.0f and \p 1.0f,		* Results are 32-bit floating point values between \p 0.0f and \p 1.0f,
	* excluding \p 0.0f and including \p 1.0f.		* excluding \p 0.0f and including \p 1.0f.
	*		*
	skipping to change at line 473		skipping to change at line 561
	* and results calculated on the CPU with generators created with		* and results calculated on the CPU with generators created with
	* ::curandCreateGeneratorHost(). These differences arise because of		* ::curandCreateGeneratorHost(). These differences arise because of
	* differences in results for transcendental functions. In addition,		* differences in results for transcendental functions. In addition,
	* future versions of CURAND may use newer versions of the CUDA math		* future versions of CURAND may use newer versions of the CUDA math
	* library, so different versions of CURAND may give slightly different		* library, so different versions of CURAND may give slightly different
	* numerical values.		* numerical values.
	*		*
	* \param generator - Generator to use		* \param generator - Generator to use
	* \param outputPtr - Pointer to device memory to store CUDA-generated resu lts, or		* \param outputPtr - Pointer to device memory to store CUDA-generated resu lts, or
	* Pointer to host memory to store CPU-generated resluts		* Pointer to host memory to store CPU-generated resluts
	* \param num - Number of floats to generate		* \param n - Number of floats to generate
	* \param mean - Mean of normal distribution		* \param mean - Mean of normal distribution
	* \param stddev - Standard deviation of normal distribution		* \param stddev - Standard deviation of normal distribution
	*		*
	* \return		* \return
	* CURAND_STATUS_NOT_INITIALIZED if the generator was never created \n		* CURAND_STATUS_NOT_INITIALIZED if the generator was never created \n
	* CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from		* CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from
	* a previous kernel launch \n		* a previous kernel launch \n
	* CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason \n		* CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason \n
	* CURAND_STATUS_LENGTH_NOT_MULTIPLE if the number of output samples is		* CURAND_STATUS_LENGTH_NOT_MULTIPLE if the number of output samples is
	* not a multiple of the quasirandom dimension, or is not a multiple		* not a multiple of the quasirandom dimension, or is not a multiple
	skipping to change at line 519		skipping to change at line 607
	* and results calculated on the CPU with generators created with		* and results calculated on the CPU with generators created with
	* ::curandCreateGeneratorHost(). These differences arise because of		* ::curandCreateGeneratorHost(). These differences arise because of
	* differences in results for transcendental functions. In addition,		* differences in results for transcendental functions. In addition,
	* future versions of CURAND may use newer versions of the CUDA math		* future versions of CURAND may use newer versions of the CUDA math
	* library, so different versions of CURAND may give slightly different		* library, so different versions of CURAND may give slightly different
	* numerical values.		* numerical values.
	*		*
	* \param generator - Generator to use		* \param generator - Generator to use
	* \param outputPtr - Pointer to device memory to store CUDA-generated resu lts, or		* \param outputPtr - Pointer to device memory to store CUDA-generated resu lts, or
	* Pointer to host memory to store CPU-generated resluts		* Pointer to host memory to store CPU-generated resluts
	* \param num - Number of doubles to generate		* \param n - Number of doubles to generate
	* \param mean - Mean of normal distribution		* \param mean - Mean of normal distribution
	* \param stddev - Standard deviation of normal distribution		* \param stddev - Standard deviation of normal distribution
	*		*
	* \return		* \return
	* CURAND_STATUS_NOT_INITIALIZED if the generator was never created \n		* CURAND_STATUS_NOT_INITIALIZED if the generator was never created \n
	* CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from		* CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from
	* a previous kernel launch \n		* a previous kernel launch \n
	* CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason \n		* CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason \n
	* CURAND_STATUS_LENGTH_NOT_MULTIPLE if the number of output samples is		* CURAND_STATUS_LENGTH_NOT_MULTIPLE if the number of output samples is
	* not a multiple of the quasirandom dimension, or is not a multiple		* not a multiple of the quasirandom dimension, or is not a multiple
	* of two for pseudorandom generators \n		* of two for pseudorandom generators \n
	* CURAND_STATUS_ARCH_MISMATCH if the GPU does not support double precision \n		* CURAND_STATUS_ARCH_MISMATCH if the GPU does not support double precision \n
	* CURAND_STATUS_SUCCESS if the results were generated successfully \n		* CURAND_STATUS_SUCCESS if the results were generated successfully \n
	*/		*/
	curandStatus_t CURANDAPI		curandStatus_t CURANDAPI
	curandGenerateNormalDouble(curandGenerator_t generator, double *outputPtr,		curandGenerateNormalDouble(curandGenerator_t generator, double *outputPtr,
	size_t n, double mean, double stddev);		size_t n, double mean, double stddev);
	/**		/**
			* \brief Generate log-normally distributed floats.
			*
			* Use \p generator to generate \p num float results into the device memory
			at
			* \p outputPtr. The device memory must have been previously allocated and
			be
			* large enough to hold all the results. Launches are done with the stream
			* set using ::curandSetStream(), or the null stream if no stream has been
			set.
			*
			* Results are 32-bit floating point values with log-normal distribution ba
			sed on
			* an associated normal distribution with mean \p mean and standard deviati
			on \p stddev.
			*
			* Normally distributed results are generated from pseudorandom generators
			* with a Box-Muller transform, and so require \p num to be even.
			* Quasirandom generators use an inverse cumulative distribution
			* function to preserve dimensionality.
			* The normally distributed results are transformed into log-normal distrib
			ution.
			*
			* There may be slight numerical differences between results generated
			* on the GPU with generators created with ::curandCreateGenerator()
			* and results calculated on the CPU with generators created with
			* ::curandCreateGeneratorHost(). These differences arise because of
			* differences in results for transcendental functions. In addition,
			* future versions of CURAND may use newer versions of the CUDA math
			* library, so different versions of CURAND may give slightly different
			* numerical values.
			*
			* \param generator - Generator to use
			* \param outputPtr - Pointer to device memory to store CUDA-generated resu
			lts, or
			* Pointer to host memory to store CPU-generated resluts
			* \param n - Number of floats to generate
			* \param mean - Mean of associated normal distribution
			* \param stddev - Standard deviation of associated normal distribution
			*
			* \return
			* CURAND_STATUS_NOT_INITIALIZED if the generator was never created \n
			* CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from
			* a previous kernel launch \n
			* CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason
			\n
			* CURAND_STATUS_LENGTH_NOT_MULTIPLE if the number of output samples is
			* not a multiple of the quasirandom dimension, or is not a multiple
			* of two for pseudorandom generators \n
			* CURAND_STATUS_SUCCESS if the results were generated successfully \n
			*/
			curandStatus_t CURANDAPI
			curandGenerateLogNormal(curandGenerator_t generator, float *outputPtr,
			size_t n, float mean, float stddev);
			/**
			* \brief Generate log-normally distributed doubles.
			*
			* Use \p generator to generate \p num double results into the device memor
			y at
			* \p outputPtr. The device memory must have been previously allocated and
			be
			* large enough to hold all the results. Launches are done with the stream
			* set using ::curandSetStream(), or the null stream if no stream has been
			set.
			*
			* Results are 64-bit floating point values with log-normal distribution ba
			sed on
			* an associated normal distribution with mean \p mean and standard deviati
			on \p stddev.
			*
			* Normally distributed results are generated from pseudorandom generators
			* with a Box-Muller transform, and so require \p num to be even.
			* Quasirandom generators use an inverse cumulative distribution
			* function to preserve dimensionality.
			* The normally distributed results are transformed into log-normal distrib
			ution.
			*
			* There may be slight numerical differences between results generated
			* on the GPU with generators created with ::curandCreateGenerator()
			* and results calculated on the CPU with generators created with
			* ::curandCreateGeneratorHost(). These differences arise because of
			* differences in results for transcendental functions. In addition,
			* future versions of CURAND may use newer versions of the CUDA math
			* library, so different versions of CURAND may give slightly different
			* numerical values.
			*
			* \param generator - Generator to use
			* \param outputPtr - Pointer to device memory to store CUDA-generated resu
			lts, or
			* Pointer to host memory to store CPU-generated resluts
			* \param n - Number of doubles to generate
			* \param mean - Mean of normal distribution
			* \param stddev - Standard deviation of normal distribution
			*
			* \return
			* CURAND_STATUS_NOT_INITIALIZED if the generator was never created \n
			* CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from
			* a previous kernel launch \n
			* CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason
			\n
			* CURAND_STATUS_LENGTH_NOT_MULTIPLE if the number of output samples is
			* not a multiple of the quasirandom dimension, or is not a multiple
			* of two for pseudorandom generators \n
			* CURAND_STATUS_ARCH_MISMATCH if the GPU does not support double precision
			\n
			* CURAND_STATUS_SUCCESS if the results were generated successfully \n
			*/
			curandStatus_t CURANDAPI
			curandGenerateLogNormalDouble(curandGenerator_t generator, double *outputPt
			r,
			size_t n, double mean, double stddev);
			/**
	* \brief Setup starting states.		* \brief Setup starting states.
	*		*
	* Generate the starting state of the generator. This function is		* Generate the starting state of the generator. This function is
	* automatically called by generation functions such as		* automatically called by generation functions such as
	* ::curandGenerate() and ::curandGenerateUniform().		* ::curandGenerate() and ::curandGenerateUniform().
	* It can be called manually for performance testing reasons to separate		* It can be called manually for performance testing reasons to separate
	* timings for starting state generation and random number generation.		* timings for starting state generation and random number generation.
	*		*
	* \param generator - Generator to update		* \param generator - Generator to update
	*		*
	skipping to change at line 560		skipping to change at line 743
	* CURAND_STATUS_NOT_INITIALIZED if the generator was never created \n		* CURAND_STATUS_NOT_INITIALIZED if the generator was never created \n
	* CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from		* CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from
	* a previous kernel launch \n		* a previous kernel launch \n
	* CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason \n		* CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason \n
	* CURAND_STATUS_SUCCESS if the seeds were generated successfully \n		* CURAND_STATUS_SUCCESS if the seeds were generated successfully \n
	*/		*/
	curandStatus_t CURANDAPI		curandStatus_t CURANDAPI
	curandGenerateSeeds(curandGenerator_t generator);		curandGenerateSeeds(curandGenerator_t generator);
	/**		/**
	* \brief Get direction vectors for quasirandom number generation.		* \brief Get direction vectors for 32-bit quasirandom number generation.
	*		*
	* Get a pointer to an array of direction vectors that can be used		* Get a pointer to an array of direction vectors that can be used
	* for quasirandom number generation. The resulting pointer will		* for quasirandom number generation. The resulting pointer will
	* reference an array of direction vectors in host memory.		* reference an array of direction vectors in host memory.
	*		*
	* The array contains vectors for many dimensions. Each dimension		* The array contains vectors for many dimensions. Each dimension
	* has 32 vectors. Each individual vector is an unsigned int.		* has 32 vectors. Each individual vector is an unsigned int.
	*		*
	* Legal values for \p set are:		* Legal values for \p set are:
	* - CURAND_DIRECTION_VECTORS_32_JOEKUO6 (20,000 dimensions)		* - CURAND_DIRECTION_VECTORS_32_JOEKUO6 (20,000 dimensions)
			* - CURAND_SCRAMBLED_DIRECTION_VECTORS_32_JOEKUO6 (20,000 dimensions)
	*		*
	* \param vectors - Address of pointer in which to return direction vectors		* \param vectors - Address of pointer in which to return direction vectors
	* \param set - Which set of direction vectors to use		* \param set - Which set of direction vectors to use
	*		*
	* \return		* \return
	* CURAND_STATUS_OUT_OF_RANGE if the choice of set is invalid \n		* CURAND_STATUS_OUT_OF_RANGE if the choice of set is invalid \n
	* CURAND_STATUS_SUCCESS if the pointer was set successfully \n		* CURAND_STATUS_SUCCESS if the pointer was set successfully \n
	*/		*/
	curandStatus_t CURANDAPI		curandStatus_t CURANDAPI
	curandGetDirectionVectors32(curandDirectionVectors32_t *vectors[], curandDi rectionVectorSet_t set);		curandGetDirectionVectors32(curandDirectionVectors32_t *vectors[], curandDi rectionVectorSet_t set);
	/**		/**
			* \brief Get scramble constants for 32-bit scrambled Sobol' .
			*
			* Get a pointer to an array of scramble constants that can be used
			* for quasirandom number generation. The resulting pointer will
			* reference an array of unsinged ints in host memory.
			*
			* The array contains constants for many dimensions. Each dimension
			* has a single unsigned int constant.
			*
			* \param constants - Address of pointer in which to return scramble consta
			nts
			*
			* \return
			* CURAND_STATUS_SUCCESS if the pointer was set successfully \n
			*/
			curandStatus_t CURANDAPI
			curandGetScrambleConstants32(unsigned int * * constants);
			/**
			* \brief Get direction vectors for 64-bit quasirandom number generation.
			*
			* Get a pointer to an array of direction vectors that can be used
			* for quasirandom number generation. The resulting pointer will
			* reference an array of direction vectors in host memory.
			*
			* The array contains vectors for many dimensions. Each dimension
			* has 64 vectors. Each individual vector is an unsigned long long.
			*
			* Legal values for \p set are:
			* - CURAND_DIRECTION_VECTORS_64_JOEKUO6 (20,000 dimensions)
			* - CURAND_SCRAMBLED_DIRECTION_VECTORS_64_JOEKUO6 (20,000 dimensions)
			*
			* \param vectors - Address of pointer in which to return direction vectors
			* \param set - Which set of direction vectors to use
			*
			* \return
			* CURAND_STATUS_OUT_OF_RANGE if the choice of set is invalid \n
			* CURAND_STATUS_SUCCESS if the pointer was set successfully \n
			*/
			curandStatus_t CURANDAPI
			curandGetDirectionVectors64(curandDirectionVectors64_t *vectors[], curandDi
			rectionVectorSet_t set);
			/**
			* \brief Get scramble constants for 64-bit scrambled Sobol' .
			*
			* Get a pointer to an array of scramble constants that can be used
			* for quasirandom number generation. The resulting pointer will
			* reference an array of unsinged long longs in host memory.
			*
			* The array contains constants for many dimensions. Each dimension
			* has a single unsigned long long constant.
			*
			* \param constans - Address of pointer in which to return scramble constan
			ts
			*
			* \return
			* CURAND_STATUS_SUCCESS if the pointer was set successfully \n
			*/
			curandStatus_t CURANDAPI
			curandGetScrambleConstants64(unsigned long long * * constants);
			/**
	* @}		* @}
	*/		*/
	#if defined(__cplusplus)		#if defined(__cplusplus)
	}		}
	#endif /* __cplusplus */		#endif /* __cplusplus */
	#endif /* !defined(CURAND_H_) */		#endif /* !defined(CURAND_H_) */
End of changes. 18 change blocks.
	39 lines changed or deleted		321 lines changed or added

	curand_kernel.h		curand_kernel.h
	/*
	* Copyright 2010-2010 NVIDIA Corporation. All rights reserved.		/* Copyright 2010-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* The source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.		*
	*		* The Licensed Deliverables contained herein are PROPRIETARY and
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* CONFIDENTIAL to NVIDIA and are being provided under the terms and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* conditions of a form of NVIDIA software license agreement by and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* between NVIDIA and Licensee ("License Agreement") or electronically
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* accepted by Licensee. Notwithstanding any terms or conditions to
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* the contrary in the License Agreement, reproduction or disclosure
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* of the Licensed Deliverables to any third party without the express
	,		* written consent of NVIDIA is prohibited.
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		*
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* OR PERFORMANCE OF THIS SOURCE CODE.		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. THEY ARE
	*		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* U.S. Government End Users. This source code is a "commercial item" as		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* "commercial computer software" and "commercial computer software		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* and is provided to the U.S. Government only as a commercial end item.		* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
	* source code with only those rights set forth herein.		* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
	*		* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
	* Any use of this source code in individual and commercial software must		* OF THESE LICENSED DELIVERABLES.
	* include, in the user documentation and internal comments to the code,		*
	* the above Disclaimer and U.S. Government End Users Notice.		* U.S. Government End Users. These Licensed Deliverables are a
	*/		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
			* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
			*/
	#if !defined(CURAND_KERNEL_H_)		#if !defined(CURAND_KERNEL_H_)
	#define CURAND_KERNEL_H_		#define CURAND_KERNEL_H_
	/**		/**
	* \file		* \file
	* \name CURAND Device API		* \name CURAND Device API
	* \author NVIDIA Corporation		* \author NVIDIA Corporation
	*/		*/
	skipping to change at line 130		skipping to change at line 144
	unsigned int i, x;		unsigned int i, x;
	unsigned int direction_vectors[32];		unsigned int direction_vectors[32];
	};		};
	/**		/**
	* CURAND Sobol32 state		* CURAND Sobol32 state
	*/		*/
	typedef struct curandStateSobol32 curandStateSobol32_t;		typedef struct curandStateSobol32 curandStateSobol32_t;
	/**		/**
			* CURAND Scrambled Sobol32 state
			*/
			struct curandStateScrambledSobol32;
			/* Implementation details not in reference documentation */
			struct curandStateScrambledSobol32 {
			unsigned int i, x, c;
			unsigned int direction_vectors[32];
			};
			/**
			* CURAND Scrambled Sobol32 state
			*/
			typedef struct curandStateScrambledSobol32 curandStateScrambledSobol32_t;
			/**
			* CURAND Sobol64 state
			*/
			struct curandStateSobol64;
			/* Implementation details not in reference documentation */
			struct curandStateSobol64 {
			unsigned long long i, x;
			unsigned long long direction_vectors[64];
			};
			/**
			* CURAND Sobol64 state
			*/
			typedef struct curandStateSobol64 curandStateSobol64_t;
			/**
			* CURAND Scrambled Sobol64 state
			*/
			struct curandStateScrambledSobol64;
			/* Implementation details not in reference documentation */
			struct curandStateScrambledSobol64 {
			unsigned long long i, x, c;
			unsigned long long direction_vectors[64];
			};
			/**
			* CURAND Scrambled Sobol64 state
			*/
			typedef struct curandStateScrambledSobol64 curandStateScrambledSobol64_t;
			/**
	* Default RNG		* Default RNG
	*/		*/
	typedef struct curandStateXORWOW curandState_t;		typedef struct curandStateXORWOW curandState_t;
	typedef struct curandStateXORWOW curandState;		typedef struct curandStateXORWOW curandState;
	/************************************************************************** **/		/************************************************************************** **/
	/* Utility functions needed by RNGs */		/* Utility functions needed by RNGs */
	/************************************************************************** **/		/************************************************************************** **/
	/* multiply vector by matrix, store in result		/* multiply vector by matrix, store in result
	skipping to change at line 318		skipping to change at line 380
	}		}
	state.v[i / 32] = (1 << (i & 31));		state.v[i / 32] = (1 << (i & 31));
	curand(&state);		curand(&state);
	for(int j = 0; j < n; j++) {		for(int j = 0; j < n; j++) {
	matrix[i * n + j] = state.v[j];		matrix[i * n + j] = state.v[j];
	}		}
	}		}
	}		}
	template <typename T, int n>		template <typename T, int n>
	QUALIFIERS void skipahead(unsigned long long x, T *state)		QUALIFIERS void _skipahead_scratch(unsigned long long x, T *state, unsigned int *scratch)
	{		{
	unsigned int matrix[n * n * 32], matrixA[n * n * 32], vector[n], result		// unsigned int matrix[n * n * 32];
	[n];		unsigned int *matrix = scratch;
			// unsigned int matrixA[n * n * 32];
			unsigned int *matrixA = scratch + (n * n * 32);
			// unsigned int vector[n];
			unsigned int *vector = scratch + (n * n * 32) + (n * n * 32);
			// unsigned int result[n];
			unsigned int *result = scratch + (n * n * 32) + (n * n * 32) + n;
	unsigned long long p = x;		unsigned long long p = x;
	for(int i = 0; i < n; i++) {		for(int i = 0; i < n; i++) {
	vector[i] = state->v[i];		vector[i] = state->v[i];
	}		}
			int matrix_num = 0;
			while(p && matrix_num < PRECALC_NUM_MATRICES - 1) {
			for(unsigned int t = 0; t < (p & PRECALC_BLOCK_MASK); t++) {
			#ifdef __CUDA_ARCH__
			__curand_matvec(vector, precalc_xorwow_offset_matrix[matrix_num
			], result, n);
			#else
			__curand_matvec(vector, precalc_xorwow_offset_matrix_host[matri
			x_num], result, n);
			#endif
			__curand_veccopy(vector, result, n);
			}
			p >>= PRECALC_BLOCK_SIZE;
			matrix_num++;
			}
	if(p) {		if(p) {
	__curand_generate_skipahead_matrix_xor<T, n>(matrixA);		#ifdef __CUDA_ARCH__
	__curand_matcopy(matrix, matrixA, n);		__curand_matcopy(matrix, precalc_xorwow_offset_matrix[PRECALC_NUM_M
			ATRICES - 1], n);
			__curand_matcopy(matrixA, precalc_xorwow_offset_matrix[PRECALC_NUM_
			MATRICES - 1], n);
			#else
			__curand_matcopy(matrix, precalc_xorwow_offset_matrix_host[PRECALC_
			NUM_MATRICES - 1], n);
			__curand_matcopy(matrixA, precalc_xorwow_offset_matrix_host[PRECALC
			_NUM_MATRICES - 1], n);
			#endif
	}		}
	while(p) {		while(p) {
	for(unsigned int t = 0; t < (p & SKIPAHEAD_MASK); t++) {		for(unsigned int t = 0; t < (p & SKIPAHEAD_MASK); t++) {
	__curand_matvec(vector, matrixA, result, n);		__curand_matvec(vector, matrixA, result, n);
	__curand_veccopy(vector, result, n);		__curand_veccopy(vector, result, n);
	}		}
	p >>= SKIPAHEAD_BLOCKSIZE;		p >>= SKIPAHEAD_BLOCKSIZE;
	if(p) {		if(p) {
	for(int i = 0; i < SKIPAHEAD_BLOCKSIZE; i++) {		for(int i = 0; i < SKIPAHEAD_BLOCKSIZE; i++) {
	__curand_matmat(matrix, matrixA, n);		__curand_matmat(matrix, matrixA, n);
	skipping to change at line 349		skipping to change at line 436
	}		}
	}		}
	}		}
	for(int i = 0; i < n; i++) {		for(int i = 0; i < n; i++) {
	state->v[i] = vector[i];		state->v[i] = vector[i];
	}		}
	state->d += 362437 * (unsigned int)x;		state->d += 362437 * (unsigned int)x;
	}		}
	template <typename T, int n>		template <typename T, int n>
	QUALIFIERS void skipahead_sequence(unsigned long long x, T *state)		QUALIFIERS void _skipahead_sequence_scratch(unsigned long long x, T *state, unsigned int *scratch)
	{		{
	unsigned int matrix[n * n * 32], matrixA[n * n * 32], vector[n], result		// unsigned int matrix[n * n * 32];
	[n];		unsigned int *matrix = scratch;
			// unsigned int matrixA[n * n * 32];
			unsigned int *matrixA = scratch + (n * n * 32);
			// unsigned int vector[n];
			unsigned int *vector = scratch + (n * n * 32) + (n * n * 32);
			// unsigned int result[n];
			unsigned int *result = scratch + (n * n * 32) + (n * n * 32) + n;
	unsigned long long p = x;		unsigned long long p = x;
	for(int i = 0; i < n; i++) {		for(int i = 0; i < n; i++) {
	vector[i] = state->v[i];		vector[i] = state->v[i];
	}		}
	int matrix_num = 0;		int matrix_num = 0;
	while(p && matrix_num < PRECALC_NUM_MATRICES - 1) {		while(p && matrix_num < PRECALC_NUM_MATRICES - 1) {
	for(unsigned int t = 0; t < (p & PRECALC_BLOCK_MASK); t++) {		for(unsigned int t = 0; t < (p & PRECALC_BLOCK_MASK); t++) {
	#ifdef __CUDA_ARCH__		#ifdef __CUDA_ARCH__
	__curand_matvec(vector, precalc_xorwow_matrix[matrix_num], resu lt, n);		__curand_matvec(vector, precalc_xorwow_matrix[matrix_num], resu lt, n);
	#else		#else
	skipping to change at line 410		skipping to change at line 504
	* Update the XORWOW state in \p state to skip ahead \p n elements.		* Update the XORWOW state in \p state to skip ahead \p n elements.
	*		*
	* All values of \p n are valid. Large values require more computation and so		* All values of \p n are valid. Large values require more computation and so
	* will take more time to complete.		* will take more time to complete.
	*		*
	* \param n - Number of elements to skip		* \param n - Number of elements to skip
	* \param state - Pointer to state to update		* \param state - Pointer to state to update
	*/		*/
	QUALIFIERS void skipahead(unsigned long long n, curandStateXORWOW_t *state)		QUALIFIERS void skipahead(unsigned long long n, curandStateXORWOW_t *state)
	{		{
	skipahead<curandStateXORWOW_t, 5>(n, state);		unsigned int scratch[5 * 5 * 32 * 2 + 5 * 2];
			_skipahead_scratch<curandStateXORWOW_t, 5>(n, state, (unsigned int *)sc
			ratch);
	}		}
	/**		/**
	* \brief Update XORWOW state to skip ahead \p n subsequences.		* \brief Update XORWOW state to skip ahead \p n subsequences.
	*		*
	* Update the XORWOW state in \p state to skip ahead \p n subsequences. Ea ch		* Update the XORWOW state in \p state to skip ahead \p n subsequences. Ea ch
	* subsequence is \f$ 2^{67} \f$ elements long, so this means the function will skip ahead		* subsequence is \f$ 2^{67} \f$ elements long, so this means the function will skip ahead
	* \f$ 2^{67} \cdot n\f$ elements.		* \f$ 2^{67} \cdot n\f$ elements.
	*		*
	* All values of \p n are valid. Large values require more computation and so		* All values of \p n are valid. Large values require more computation and so
	* will take more time to complete.		* will take more time to complete.
	*		*
	* \param n - Number of subsequences to skip		* \param n - Number of subsequences to skip
	* \param state - Pointer to state to update		* \param state - Pointer to state to update
	*/		*/
	QUALIFIERS void skipahead_sequence(unsigned long long n, curandStateXORWOW_ t *state)		QUALIFIERS void skipahead_sequence(unsigned long long n, curandStateXORWOW_ t *state)
	{		{
	skipahead_sequence<curandStateXORWOW_t, 5>(n, state);		unsigned int scratch[5 * 5 * 32 * 2 + 5 * 2];
			_skipahead_sequence_scratch<curandStateXORWOW_t, 5>(n, state, (unsigned
			int *)scratch);
			}
			QUALIFIERS void _curand_init_scratch(unsigned long long seed,
			unsigned long long subsequence,
			unsigned long long offset,
			curandStateXORWOW_t *state,
			unsigned int *scratch)
			{
			// Break up seed, apply salt
			// Constants are arbitrary nonzero values
			unsigned int s0 = ((unsigned int)seed) ^ 0xaad26b49UL;
			unsigned int s1 = (unsigned int)(seed >> 32) ^ 0xf7dcefddUL;
			// Simple multiplication to mix up bits
			// Constants are arbitrary odd values
			unsigned int t0 = 1099087573UL * s0;
			unsigned int t1 = 2591861531UL * s1;
			state->d = 6615241 + t1 + t0;
			state->v[0] = 123456789UL + t0;
			state->v[1] = 362436069UL ^ t0;
			state->v[2] = 521288629UL + t1;
			state->v[3] = 88675123UL ^ t1;
			state->v[4] = 5783321UL + t0;
			_skipahead_scratch<curandStateXORWOW_t, 5>(offset, state, scratch);
			_skipahead_sequence_scratch<curandStateXORWOW_t, 5>(subsequence, state,
			scratch);
			state->boxmuller_flag = 0;
	}		}
	/**		/**
	* \brief Initialize XORWOW state.		* \brief Initialize XORWOW state.
	*		*
	* Initialize XORWOW state in \p state with the given \p seed, \p subsequen ce,		* Initialize XORWOW state in \p state with the given \p seed, \p subsequen ce,
	* and \p offset.		* and \p offset.
	*		*
	* All input values of \p seed, \p subsequence, and \p offset are legal. L arge		* All input values of \p seed, \p subsequence, and \p offset are legal. L arge
	* values for \p subsequence and \p offset require more computation and so will		* values for \p subsequence and \p offset require more computation and so will
	skipping to change at line 450		skipping to change at line 571
	*		*
	* A value of 0 for \p seed sets the state to the values of the original		* A value of 0 for \p seed sets the state to the values of the original
	* published version of the \p xorwow algorithm.		* published version of the \p xorwow algorithm.
	*		*
	* \param seed - Arbitrary bits to use as a seed		* \param seed - Arbitrary bits to use as a seed
	* \param subsequence - Subsequence to start at		* \param subsequence - Subsequence to start at
	* \param offset - Absolute offset into sequence		* \param offset - Absolute offset into sequence
	* \param state - Pointer to state to initialize		* \param state - Pointer to state to initialize
	*/		*/
	QUALIFIERS void curand_init(unsigned long long seed,		QUALIFIERS void curand_init(unsigned long long seed,
	unsigned long long subsequence,		unsigned long long subsequence,
	unsigned long long offset,		unsigned long long offset,
	curandStateXORWOW_t *state)		curandStateXORWOW_t *state)
	{		{
	// Break up seed, apply salt		unsigned int scratch[5 * 5 * 32 * 2 + 5 * 2];
	unsigned int s0 = ((unsigned int)seed) ^ 0xaad26b49UL;		_curand_init_scratch(seed, subsequence, offset, state, (unsigned int*)s
	unsigned int s1 = (unsigned int)(seed >> 32) ^ 0xf7dcefddUL;		cratch);
	// Simple multiplication to mix up bits
	unsigned int t0 = 1099087573UL * s0;
	unsigned int t1 = 2591861531UL * s1;
	state->d = 6615241 + t1 + t0;
	state->v[0] = 123456789UL + t0;
	state->v[1] = 362436069UL ^ t0;
	state->v[2] = 521288629UL + t1;
	state->v[3] = 88675123UL ^ t1;
	state->v[4] = 5783321UL + t0;
	skipahead(offset, state);
	skipahead_sequence(subsequence, state);
	state->boxmuller_flag = 0;
	}		}
	/**		/**
	* \brief Return 32-bits of pseudorandomness from an XORWOW generator.		* \brief Return 32-bits of pseudorandomness from an XORWOW generator.
	*		*
	* Return 32-bits of pseudorandomness from the XORWOW generator in \p state ,		* Return 32-bits of pseudorandomness from the XORWOW generator in \p state ,
	* increment position of generator by one.		* increment position of generator by one.
	*		*
	* \param state - Pointer to state to update		* \param state - Pointer to state to update
	*		*
	* \return 32-bits of pseudorandomness as an unsigned int, all bits valid t o use.		* \return 32-bits of pseudorandomness as an unsigned int, all bits valid t o use.
	*/		*/
	skipping to change at line 504		skipping to change at line 611
	/**		/**
	* \brief Update Sobol32 state to skip \p n elements.		* \brief Update Sobol32 state to skip \p n elements.
	*		*
	* Update the Sobol32 state in \p state to skip ahead \p n elements.		* Update the Sobol32 state in \p state to skip ahead \p n elements.
	*		*
	* All values of \p n are valid.		* All values of \p n are valid.
	*		*
	* \param n - Number of elements to skip		* \param n - Number of elements to skip
	* \param state - Pointer to state to update		* \param state - Pointer to state to update
	*/		*/
			template <typename T>
	QUALIFIERS void skipahead(unsigned int n, curandStateSobol32_t *state)		QUALIFIERS void skipahead(unsigned int n, T state)
	{		{
	unsigned int i_gray;		unsigned int i_gray;
	state->i += n;		state->i += n;
	/* Convert state->i to gray code */		/* Convert state->i to gray code */
	i_gray = state->i ^ (state->i >> 1);		i_gray = state->i ^ (state->i >> 1);
	state->x = 0;
	for(unsigned int k = 0; k < 32; k++) {		for(unsigned int k = 0; k < 32; k++) {
	if(i_gray & (1 << k)) {		if(i_gray & (1 << k)) {
	state->x ^= state->direction_vectors[k];		state->x ^= state->direction_vectors[k];
	}		}
	}		}
	return;		return;
	}		}
	/**		/**
			* \brief Update Sobol64 state to skip \p n elements.
			*
			* Update the Sobol64 state in \p state to skip ahead \p n elements.
			*
			* All values of \p n are valid.
			*
			* \param n - Number of elements to skip
			* \param state - Pointer to state to update
			*/
			template <typename T>
			QUALIFIERS void skipahead(unsigned long long n, T state)
			{
			unsigned long long i_gray;
			state->i += n;
			/* Convert state->i to gray code */
			i_gray = state->i ^ (state->i >> 1);
			for(unsigned k = 0; k < 64; k++) {
			if(i_gray & (1ULL << k)) {
			state->x ^= state->direction_vectors[k];
			}
			}
			return;
			}
			/**
	* \brief Initialize Sobol32 state.		* \brief Initialize Sobol32 state.
	*		*
	* Initialize Sobol32 state in \p state with the given \p direction \p vect ors and		* Initialize Sobol32 state in \p state with the given \p direction \p vect ors and
	* \p offset.		* \p offset.
	*		*
	* The direction vector is a device pointer to an array of 32 unsigned ints .		* The direction vector is a device pointer to an array of 32 unsigned ints .
	* All input values of \p offset are legal.		* All input values of \p offset are legal.
	*		*
	* \param direction vectors - Pointer to array of 32 unsigned ints represen		* \param direction_vectors - Pointer to array of 32 unsigned ints represen
	ting the		ting the
	direction vectors for the desired dimension		* direction vectors for the desired dimension
	* \param offset - Absolute offset into sequence		* \param offset - Absolute offset into sequence
	* \param state - Pointer to state to initialize		* \param state - Pointer to state to initialize
	*/		*/
	QUALIFIERS void curand_init(curandDirectionVectors32_t direction_vectors,		QUALIFIERS void curand_init(curandDirectionVectors32_t direction_vectors,
	unsigned int offset,		unsigned int offset,
	curandStateSobol32_t *state)		curandStateSobol32_t *state)
	{		{
	state->i = 0;		state->i = 0;
	for(int i = 0; i < 32; i++) {		for(int i = 0; i < 32; i++) {
	state->direction_vectors[i] = direction_vectors[i];		state->direction_vectors[i] = direction_vectors[i];
	}		}
	state->x = 0;		state->x = 0;
	skipahead(offset, state);		skipahead<curandStateSobol32_t *>(offset, state);
			}
			/**
			* \brief Initialize Scrambled Sobol32 state.
			*
			* Initialize Sobol32 state in \p state with the given \p direction \p vect
			ors and
			* \p offset.
			*
			* The direction vector is a device pointer to an array of 32 unsigned ints
			.
			* All input values of \p offset are legal.
			*
			* \param direction_vectors - Pointer to array of 32 unsigned ints represen
			ting the
			direction vectors for the desired dimension
			* \param scramble_c Scramble constant
			* \param offset - Absolute offset into sequence
			* \param state - Pointer to state to initialize
			*/
			QUALIFIERS void curand_init(curandDirectionVectors32_t direction_vectors,
			unsigned int scramble_c,
			unsigned int offset,
			curandStateScrambledSobol32_t *
			state)
			{
			state->i = 0;
			for(int i = 0; i < 32; i++) {
			state->direction_vectors[i] = direction_vectors[i];
			}
			state->x = scramble_c;
			skipahead<curandStateScrambledSobol32_t *>(offset, state);
	}		}
	QUALIFIERS int __curand_find_trailing_zero(unsigned int x)		template<typename XT>
			QUALIFIERS int __curand_find_trailing_zero(XT x)
	{		{
	#if __CUDA_ARCH__ > 0		#if __CUDA_ARCH__ > 0
	int y = __ffs(~x);		unsigned long long z = x;
	return y > 0 ? y - 1 : 31;		int y = __ffsll(~z) | 0x40;
			return (y - 1) & 0x3F;
	#else		#else
	int i = 0;		unsigned long long z = x;
	while(x & 1) {		int i = 1;
			while(z & 1) {
	i ++;		i ++;
	x >>= 1;		z >>= 1;
	}		}
	return i < 32 ? i : 31;		return i - 1;
	#endif		#endif
	}		}
			/**
			* \brief Initialize Sobol64 state.
			*
			* Initialize Sobol64 state in \p state with the given \p direction \p vect
			ors and
			* \p offset.
			*
			* The direction vector is a device pointer to an array of 64 unsigned long
			longs.
			* All input values of \p offset are legal.
			*
			* \param direction_vectors - Pointer to array of 64 unsigned long longs re
			presenting the
			direction vectors for the desired dimension
			* \param offset - Absolute offset into sequence
			* \param state - Pointer to state to initialize
			*/
			QUALIFIERS void curand_init(curandDirectionVectors64_t direction_vectors,
			unsigned long long offset,
			curandStateSobol64_t *state)
			{
			state->i = 0;
			for(int i = 0; i < 64; i++) {
			state->direction_vectors[i] = direction_vectors[i];
			}
			state->x = 0;
			skipahead<curandStateSobol64_t *>(offset, state);
			}
	QUALIFIERS void _skipahead_stride(int n_log2, curandStateSobol32_t *state)		template<typename PT>
			QUALIFIERS void _skipahead_stride(int n_log2, PT state)
	{		{
	/* Moving from i to i+2^n_log2 element in gray code is flipping two bit s */		/* Moving from i to i+2^n_log2 element in gray code is flipping two bit s */
	unsigned int shifted_i = state->i >> n_log2;		unsigned int shifted_i = state->i >> n_log2;
	state->x ^= state->direction_vectors[n_log2 - 1];		state->x ^= state->direction_vectors[n_log2 - 1];
	state->x ^= state->direction_vectors[		state->x ^= state->direction_vectors[
	__curand_find_trailing_zero(shifted_i) + n_log2];		__curand_find_trailing_zero(shifted_i) + n_log2];
	state->i += 1 << n_log2;		state->i += 1 << n_log2;
			}
			/**
			* \brief Initialize Scrambled Sobol64 state.
			*
			* Initialize Sobol64 state in \p state with the given \p direction \p vect
			ors and
			* \p offset.
			*
			* The direction vector is a device pointer to an array of 64 unsigned long
			longs.
			* All input values of \p offset are legal.
			*
			* \param direction_vectors - Pointer to array of 64 unsigned long longs re
			presenting the
			direction vectors for the desired dimension
			* \param scramble_c Scramble constant
			* \param offset - Absolute offset into sequence
			* \param state - Pointer to state to initialize
			*/
			QUALIFIERS void curand_init(curandDirectionVectors64_t direction_vectors,
			unsigned long long scramble_c,
			unsigned long long offset,
			curandStateScrambledSobol64_t *
			state)
			{
			state->i = 0;
			for(int i = 0; i < 64; i++) {
			state->direction_vectors[i] = direction_vectors[i];
			}
			state->x = scramble_c;
			skipahead<curandStateScrambledSobol64_t *>(offset, state);
	}		}
	/**		/**
	* \brief Return 32-bits of quasirandomness from a Sobol32 generator.		* \brief Return 32-bits of quasirandomness from a Sobol32 generator.
	*		*
	* Return 32-bits of quasirandomness from the Sobol32 generator in \p state ,		* Return 32-bits of quasirandomness from the Sobol32 generator in \p state ,
	* increment position of generator by one.		* increment position of generator by one.
	*		*
	* \param state - Pointer to state to update		* \param state - Pointer to state to update
	*		*
	* \return 32-bits of quasirandomness as an unsigned int, all bits valid to use.		* \return 32-bits of quasirandomness as an unsigned int, all bits valid to use.
	*/		*/
	QUALIFIERS unsigned int curand(curandStateSobol32_t *state)
			QUALIFIERS unsigned int curand(curandStateSobol32_t * state)
			{
			/* Moving from i to i+1 element in gray code is flipping one bit,
			the trailing zero bit of i
			*/
			unsigned int res = state->x;
			state->x ^= state->direction_vectors[__curand_find_trailing_zero(state-
			>i)];
			state->i ++;
			return res;
			}
			/**
			* \brief Return 32-bits of quasirandomness from a scrambled Sobol32 genera
			tor.
			*
			* Return 32-bits of quasirandomness from the scrambled Sobol32 generator i
			n \p state,
			* increment position of generator by one.
			*
			* \param state - Pointer to state to update
			*
			* \return 32-bits of quasirandomness as an unsigned int, all bits valid to
			use.
			*/
			QUALIFIERS unsigned int curand(curandStateScrambledSobol32_t * state)
	{		{
	/* Moving from i to i+1 element in gray code is flipping one bit,		/* Moving from i to i+1 element in gray code is flipping one bit,
	the trailing zero bit of i		the trailing zero bit of i
	*/		*/
	unsigned int res = state->x;		unsigned int res = state->x;
	state->x ^= state->direction_vectors[__curand_find_trailing_zero(state- >i)];		state->x ^= state->direction_vectors[__curand_find_trailing_zero(state- >i)];
	state->i ++;		state->i ++;
	return res;		return res;
	}		}
			/**
			* \brief Return 64-bits of quasirandomness from a Sobol64 generator.
			*
			* Return 64-bits of quasirandomness from the Sobol64 generator in \p state
			,
			* increment position of generator by one.
			*
			* \param state - Pointer to state to update
			*
			* \return 64-bits of quasirandomness as an unsigned long long, all bits va
			lid to use.
			*/
			QUALIFIERS unsigned long long curand(curandStateSobol64_t * state)
			{
			/* Moving from i to i+1 element in gray code is flipping one bit,
			the trailing zero bit of i
			*/
			unsigned long long res = state->x;
			state->x ^= state->direction_vectors[__curand_find_trailing_zero(state-
			>i)];
			state->i ++;
			return res;
			}
			/**
			* \brief Return 64-bits of quasirandomness from a scrambled Sobol64 genera
			tor.
			*
			* Return 64-bits of quasirandomness from the scrambled Sobol32 generator i
			n \p state,
			* increment position of generator by one.
			*
			* \param state - Pointer to state to update
			*
			* \return 64-bits of quasirandomness as an unsigned long long, all bits va
			lid to use.
			*/
			QUALIFIERS unsigned long long curand(curandStateScrambledSobol64_t * state)
			{
			/* Moving from i to i+1 element in gray code is flipping one bit,
			the trailing zero bit of i
			*/
			unsigned long long res = state->x;
			state->x ^= state->direction_vectors[__curand_find_trailing_zero(state-
			>i)];
			state->i ++;
			return res;
			}
	/******************************************************/		/******************************************************/
	QUALIFIERS float _curand_uniform(unsigned int x)		QUALIFIERS float _curand_uniform(unsigned int x)
	{		{
	return x * CURAND_2POW32_INV + (CURAND_2POW32_INV/2.0f);		return x * CURAND_2POW32_INV + (CURAND_2POW32_INV/2.0f);
	}		}
			QUALIFIERS float _curand_uniform(unsigned long long x)
			{
			unsigned int t;
			t = (unsigned int)(x >> 32);
			return t * CURAND_2POW32_INV + (CURAND_2POW32_INV/2.0f);
			}
	QUALIFIERS double _curand_uniform_double(unsigned int x)		QUALIFIERS double _curand_uniform_double(unsigned int x)
	{		{
	return x * CURAND_2POW32_INV_DOUBLE + (CURAND_2POW32_INV_DOUBLE/2.0);		return x * CURAND_2POW32_INV_DOUBLE + (CURAND_2POW32_INV_DOUBLE/2.0);
	}		}
			QUALIFIERS double _curand_uniform_double(unsigned long long x)
			{
			return (x >> 11) * CURAND_2POW53_INV_DOUBLE + (CURAND_2POW53_INV_DOUBLE
			/2.0);
			}
	QUALIFIERS double _curand_uniform_double_hq(unsigned int x, unsigned int y)		QUALIFIERS double _curand_uniform_double_hq(unsigned int x, unsigned int y)
	{		{
	unsigned long long z = (unsigned long long)x ^		unsigned long long z = (unsigned long long)x ^
	((unsigned long long)y << (53 - 32));		((unsigned long long)y << (53 - 32));
	return z * CURAND_2POW53_INV_DOUBLE + (CURAND_2POW53_INV_DOUBLE/2.0);		return z * CURAND_2POW53_INV_DOUBLE + (CURAND_2POW53_INV_DOUBLE/2.0);
	}		}
			QUALIFIERS double _curand_uniform_double_64(unsigned long long x)
			{
			unsigned long long z = (unsigned long long)x >> (64 - 53);
			return z * CURAND_2POW53_INV_DOUBLE + (CURAND_2POW53_INV_DOUBLE/2.0);
			}
	QUALIFIERS float curand_uniform(curandStateTest_t *state)		QUALIFIERS float curand_uniform(curandStateTest_t *state)
	{		{
	return _curand_uniform(curand(state));		return _curand_uniform(curand(state));
	}		}
	QUALIFIERS double curand_uniform_double(curandStateTest_t *state)		QUALIFIERS double curand_uniform_double(curandStateTest_t *state)
	{		{
	return _curand_uniform_double(curand(state));		return _curand_uniform_double(curand(state));
	}		}
	skipping to change at line 704		skipping to change at line 1004
	* to preserve the quasirandom properties of the sequence.		* to preserve the quasirandom properties of the sequence.
	*		*
	* \param state - Pointer to state to update		* \param state - Pointer to state to update
	*		*
	* \return uniformly distributed double between \p 0.0 and \p 1.0		* \return uniformly distributed double between \p 0.0 and \p 1.0
	*/		*/
	QUALIFIERS double curand_uniform_double(curandStateSobol32_t *state)		QUALIFIERS double curand_uniform_double(curandStateSobol32_t *state)
	{		{
	return _curand_uniform_double(curand(state));		return _curand_uniform_double(curand(state));
	}		}
			/**
			* \brief Return a uniformly distributed float from a scrambled Sobol32 gen
			erator.
			*
			* Return a uniformly distributed float between \p 0.0f and \p 1.0f
			* from the scrambled Sobol32 generator in \p state, increment position of
			generator.
			* Output range excludes \p 0.0f but includes \p 1.0f. Denormalized floati
			ng
			* point outputs are never returned.
			*
			* The implementation is guaranteed to use a single call to \p curand().
			*
			* \param state - Pointer to state to update
			*
			* \return uniformly distributed float between \p 0.0f and \p 1.0f
			*/
			QUALIFIERS float curand_uniform(curandStateScrambledSobol32_t *state)
			{
			return _curand_uniform(curand(state));
			}
			/**
			* \brief Return a uniformly distributed double from a scrambled Sobol32 ge
			nerator.
			*
			* Return a uniformly distributed double between \p 0.0 and \p 1.0
			* from the scrambled Sobol32 generator in \p state, increment position of
			generator.
			* Output range excludes \p 0.0 but includes \p 1.0. Denormalized floating
			* point outputs are never returned.
			*
			* The implementation is guaranteed to use a single call to \p curand()
			* to preserve the quasirandom properties of the sequence.
			*
			* \param state - Pointer to state to update
			*
			* \return uniformly distributed double between \p 0.0 and \p 1.0
			*/
			QUALIFIERS double curand_uniform_double(curandStateScrambledSobol32_t *stat
			e)
			{
			return _curand_uniform_double(curand(state));
			}
			/**
			* \brief Return a uniformly distributed float from a Sobol64 generator.
			*
			* Return a uniformly distributed float between \p 0.0f and \p 1.0f
			* from the Sobol64 generator in \p state, increment position of generator.
			* Output range excludes \p 0.0f but includes \p 1.0f. Denormalized floati
			ng
			* point outputs are never returned.
			*
			* The implementation is guaranteed to use a single call to \p curand().
			*
			* \param state - Pointer to state to update
			*
			* \return uniformly distributed float between \p 0.0f and \p 1.0f
			*/
			QUALIFIERS float curand_uniform(curandStateSobol64_t *state)
			{
			return _curand_uniform(curand(state));
			}
			/**
			* \brief Return a uniformly distributed double from a Sobol64 generator.
			*
			* Return a uniformly distributed double between \p 0.0 and \p 1.0
			* from the Sobol64 generator in \p state, increment position of generator.
			* Output range excludes \p 0.0 but includes \p 1.0. Denormalized floating
			* point outputs are never returned.
			*
			* The implementation is guaranteed to use a single call to \p curand()
			* to preserve the quasirandom properties of the sequence.
			*
			* \param state - Pointer to state to update
			*
			* \return uniformly distributed double between \p 0.0 and \p 1.0
			*/
			QUALIFIERS double curand_uniform_double(curandStateSobol64_t *state)
			{
			return _curand_uniform_double(curand(state));
			}
			/**
			* \brief Return a uniformly distributed float from a scrambled Sobol64 gen
			erator.
			*
			* Return a uniformly distributed float between \p 0.0f and \p 1.0f
			* from the scrambled Sobol64 generator in \p state, increment position of
			generator.
			* Output range excludes \p 0.0f but includes \p 1.0f. Denormalized floati
			ng
			* point outputs are never returned.
			*
			* The implementation is guaranteed to use a single call to \p curand().
			*
			* \param state - Pointer to state to update
			*
			* \return uniformly distributed float between \p 0.0f and \p 1.0f
			*/
			QUALIFIERS float curand_uniform(curandStateScrambledSobol64_t *state)
			{
			return _curand_uniform(curand(state));
			}
			/**
			* \brief Return a uniformly distributed double from a scrambled Sobol64 ge
			nerator.
			*
			* Return a uniformly distributed double between \p 0.0 and \p 1.0
			* from the scrambled Sobol64 generator in \p state, increment position of
			generator.
			* Output range excludes \p 0.0 but includes \p 1.0. Denormalized floating
			* point outputs are never returned.
			*
			* The implementation is guaranteed to use a single call to \p curand()
			* to preserve the quasirandom properties of the sequence.
			*
			* \param state - Pointer to state to update
			*
			* \return uniformly distributed double between \p 0.0 and \p 1.0
			*/
			QUALIFIERS double curand_uniform_double(curandStateScrambledSobol64_t *stat
			e)
			{
			return _curand_uniform_double(curand(state));
			}
	QUALIFIERS float2 _curand_box_muller(unsigned int x, unsigned int y)		QUALIFIERS float2 _curand_box_muller(unsigned int x, unsigned int y)
	{		{
	float2 result;		float2 result;
	float u = x * CURAND_2POW32_INV + (CURAND_2POW32_INV/2);		float u = x * CURAND_2POW32_INV + (CURAND_2POW32_INV/2);
	float v = y * CURAND_2POW32_INV_2PI + (CURAND_2POW32_INV_2PI/2);		float v = y * CURAND_2POW32_INV_2PI + (CURAND_2POW32_INV_2PI/2);
	#if __CUDA_ARCH__ > 0		#if __CUDA_ARCH__ > 0
	float s = sqrtf(-2.0f * __logf(u));		float s = sqrtf(-2.0f * logf(u));
	result.x = s * __sinf(v);		result.x = s * __sinf(v);
	result.y = s * __cosf(v);		result.y = s * __cosf(v);
	#else		#else
	float s = sqrtf(-2.0f * logf(u));		float s = sqrtf(-2.0f * logf(u));
	result.x = s * sinf(v);		result.x = s * sinf(v);
	result.y = s * cosf(v);		result.y = s * cosf(v);
	#endif		#endif
	return result;		return result;
	}		}
	skipping to change at line 778		skipping to change at line 1192
	s = -s;		s = -s;
	}		}
	float p = x * CURAND_2POW32_INV + (CURAND_2POW32_INV/2.0f);		float p = x * CURAND_2POW32_INV + (CURAND_2POW32_INV/2.0f);
	// p is in (0, 0.5], 2p is in (0, 1]		// p is in (0, 0.5], 2p is in (0, 1]
	return s * erfcinvf(2.0f * p);		return s * erfcinvf(2.0f * p);
	#else		#else
	return 0.0f;		return 0.0f;
	#endif		#endif
	}		}
			QUALIFIERS float _curand_normal_icdf(unsigned long long x)
			{
			#if __CUDA_ARCH__ > 0 || defined(HOST_HAVE_ERFCINVF)
			unsigned int t = (unsigned int)(x >> 32);
			float s = CURAND_SQRT2;
			// Mirror to avoid loss of precision
			if(t > 0x80000000UL) {
			t = 0xffffffffUL - t;
			s = -s;
			}
			float p = t * CURAND_2POW32_INV + (CURAND_2POW32_INV/2.0f);
			// p is in (0, 0.5], 2p is in (0, 1]
			return s * erfcinvf(2.0f * p);
			#else
			return 0.0f;
			#endif
			}
	QUALIFIERS double _curand_normal_icdf_double(unsigned int x)		QUALIFIERS double _curand_normal_icdf_double(unsigned int x)
	{		{
	#if __CUDA_ARCH__ > 0 || defined(HOST_HAVE_ERFCINVF)		#if __CUDA_ARCH__ > 0 || defined(HOST_HAVE_ERFCINVF)
	double s = CURAND_SQRT2_DOUBLE;		double s = CURAND_SQRT2_DOUBLE;
	// Mirror to avoid loss of precision		// Mirror to avoid loss of precision
	if(x > 0x80000000UL) {		if(x > 0x80000000UL) {
	x = 0xffffffffUL - x;		x = 0xffffffffUL - x;
	s = -s;		s = -s;
	}		}
	double p = x * CURAND_2POW32_INV_DOUBLE + (CURAND_2POW32_INV_DOUBLE/2.0 );		double p = x * CURAND_2POW32_INV_DOUBLE + (CURAND_2POW32_INV_DOUBLE/2.0 );
	// p is in (0, 0.5], 2p is in (0, 1]		// p is in (0, 0.5], 2p is in (0, 1]
	return s * erfcinv(2.0 * p);		return s * erfcinv(2.0 * p);
	#else		#else
	return 0.0;		return 0.0;
	#endif		#endif
	}		}
			QUALIFIERS double _curand_normal_icdf_double(unsigned long long x)
			{
			#if __CUDA_ARCH__ > 0 || defined(HOST_HAVE_ERFCINVF)
			double s = CURAND_SQRT2_DOUBLE;
			x >>= 11;
			// Mirror to avoid loss of precision
			if(x > 0x10000000000000UL) {
			x = 0x1fffffffffffffUL - x;
			s = -s;
			}
			double p = x * CURAND_2POW53_INV_DOUBLE + (CURAND_2POW53_INV_DOUBLE/2.0
			);
			// p is in (0, 0.5], 2p is in (0, 1]
			return s * erfcinv(2.0 * p);
			#else
			return 0.0;
			#endif
			}
	/**		/**
	* \brief Return a normally distributed float from an XORWOW generator.		* \brief Return a normally distributed float from an XORWOW generator.
	*		*
	* Return a single normally distributed float with mean \p 0.0f and		* Return a single normally distributed float with mean \p 0.0f and
	* standard deviation \p 1.0f from the XORWOW generator in \p state,		* standard deviation \p 1.0f from the XORWOW generator in \p state,
	* increment position of generator by one.		* increment position of generator by one.
	*		*
	* The implementation uses a Box-Muller transform to generate two		* The implementation uses a Box-Muller transform to generate two
	* normally distributed results, then returns them one at a time.		* normally distributed results, then returns them one at a time.
	* See ::curand_normal2() for a more efficient version that returns		* See ::curand_normal2() for a more efficient version that returns
	skipping to change at line 847		skipping to change at line 1297
	*		*
	* \return Normally distributed float2 where each element is from a		* \return Normally distributed float2 where each element is from a
	* distribution with mean \p 0.0f and standard deviation \p 1.0f		* distribution with mean \p 0.0f and standard deviation \p 1.0f
	*/		*/
	QUALIFIERS float2 curand_normal2(curandStateXORWOW_t *state)		QUALIFIERS float2 curand_normal2(curandStateXORWOW_t *state)
	{		{
	return curand_box_muller(state);		return curand_box_muller(state);
	}		}
	/**		/**
	* \brief Return a normally distributed float from an Sobol32 generator.		* \brief Return a normally distributed float from a Sobol32 generator.
	*		*
	* Return a single normally distributed float with mean \p 0.0f and		* Return a single normally distributed float with mean \p 0.0f and
	* standard deviation \p 1.0f from the Sobol32 generator in \p state,		* standard deviation \p 1.0f from the Sobol32 generator in \p state,
	* increment position of generator by one.		* increment position of generator by one.
	*		*
	* The implementation uses the inverse cumulative distribution function		* The implementation uses the inverse cumulative distribution function
	* to generate normally distributed results.		* to generate normally distributed results.
	*		*
	* \param state - Pointer to state to update		* \param state - Pointer to state to update
	*		*
	* \return Normally distributed float with mean \p 0.0f and standard deviat ion \p 1.0f		* \return Normally distributed float with mean \p 0.0f and standard deviat ion \p 1.0f
	*/		*/
	QUALIFIERS float curand_normal(curandStateSobol32_t *state)		QUALIFIERS float curand_normal(curandStateSobol32_t *state)
	{		{
	return _curand_normal_icdf(curand(state));		return _curand_normal_icdf(curand(state));
	}		}
	/**		/**
			* \brief Return a normally distributed float from a scrambled Sobol32 gene
			rator.
			*
			* Return a single normally distributed float with mean \p 0.0f and
			* standard deviation \p 1.0f from the scrambled Sobol32 generator in \p st
			ate,
			* increment position of generator by one.
			*
			* The implementation uses the inverse cumulative distribution function
			* to generate normally distributed results.
			*
			* \param state - Pointer to state to update
			*
			* \return Normally distributed float with mean \p 0.0f and standard deviat
			ion \p 1.0f
			*/
			QUALIFIERS float curand_normal(curandStateScrambledSobol32_t *state)
			{
			return _curand_normal_icdf(curand(state));
			}
			/**
			* \brief Return a normally distributed float from a Sobol64 generator.
			*
			* Return a single normally distributed float with mean \p 0.0f and
			* standard deviation \p 1.0f from the Sobol64 generator in \p state,
			* increment position of generator by one.
			*
			* The implementation uses the inverse cumulative distribution function
			* to generate normally distributed results.
			*
			* \param state - Pointer to state to update
			*
			* \return Normally distributed float with mean \p 0.0f and standard deviat
			ion \p 1.0f
			*/
			QUALIFIERS float curand_normal(curandStateSobol64_t *state)
			{
			return _curand_normal_icdf(curand(state));
			}
			/**
			* \brief Return a normally distributed float from a scrambled Sobol64 gene
			rator.
			*
			* Return a single normally distributed float with mean \p 0.0f and
			* standard deviation \p 1.0f from the scrambled Sobol64 generator in \p st
			ate,
			* increment position of generator by one.
			*
			* The implementation uses the inverse cumulative distribution function
			* to generate normally distributed results.
			*
			* \param state - Pointer to state to update
			*
			* \return Normally distributed float with mean \p 0.0f and standard deviat
			ion \p 1.0f
			*/
			QUALIFIERS float curand_normal(curandStateScrambledSobol64_t *state)
			{
			return _curand_normal_icdf(curand(state));
			}
			/**
	* \brief Return a normally distributed double from an XORWOW generator.		* \brief Return a normally distributed double from an XORWOW generator.
	*		*
	* Return a single normally distributed double with mean \p 0.0 and		* Return a single normally distributed double with mean \p 0.0 and
	* standard deviation \p 1.0 from the XORWOW generator in \p state,		* standard deviation \p 1.0 from the XORWOW generator in \p state,
	* increment position of generator.		* increment position of generator.
	*		*
	* The implementation uses a Box-Muller transform to generate two		* The implementation uses a Box-Muller transform to generate two
	* normally distributed results, then returns them one at a time.		* normally distributed results, then returns them one at a time.
	* See ::curand_normal2_double() for a more efficient version that returns		* See ::curand_normal2_double() for a more efficient version that returns
	* both results at once.		* both results at once.
	skipping to change at line 937		skipping to change at line 1444
	*		*
	* \param state - Pointer to state to update		* \param state - Pointer to state to update
	*		*
	* \return Normally distributed double with mean \p 0.0 and standard deviat ion \p 1.0		* \return Normally distributed double with mean \p 0.0 and standard deviat ion \p 1.0
	*/		*/
	QUALIFIERS double curand_normal_double(curandStateSobol32_t *state)		QUALIFIERS double curand_normal_double(curandStateSobol32_t *state)
	{		{
	return _curand_normal_icdf_double(curand(state));		return _curand_normal_icdf_double(curand(state));
	}		}
			/**
			* \brief Return a normally distributed double from a scrambled Sobol32 gen
			erator.
			*
			* Return a single normally distributed double with mean \p 0.0 and
			* standard deviation \p 1.0 from the scrambled Sobol32 generator in \p sta
			te,
			* increment position of generator by one.
			*
			* The implementation uses the inverse cumulative distribution function
			* to generate normally distributed results.
			*
			* \param state - Pointer to state to update
			*
			* \return Normally distributed double with mean \p 0.0 and standard deviat
			ion \p 1.0
			*/
			QUALIFIERS double curand_normal_double(curandStateScrambledSobol32_t *state
			)
			{
			return _curand_normal_icdf_double(curand(state));
			}
			/**
			* \brief Return a normally distributed double from a Sobol64 generator.
			*
			* Return a single normally distributed double with mean \p 0.0 and
			* standard deviation \p 1.0 from the Sobol64 generator in \p state,
			* increment position of generator by one.
			*
			* The implementation uses the inverse cumulative distribution function
			* to generate normally distributed results.
			*
			* \param state - Pointer to state to update
			*
			* \return Normally distributed double with mean \p 0.0 and standard deviat
			ion \p 1.0
			*/
			QUALIFIERS double curand_normal_double(curandStateSobol64_t *state)
			{
			return _curand_normal_icdf_double(curand(state));
			}
			/**
			* \brief Return a normally distributed double from a scrambled Sobol64 gen
			erator.
			*
			* Return a single normally distributed double with mean \p 0.0 and
			* standard deviation \p 1.0 from the scrambled Sobol64 generator in \p sta
			te,
			* increment position of generator by one.
			*
			* The implementation uses the inverse cumulative distribution function
			* to generate normally distributed results.
			*
			* \param state - Pointer to state to update
			*
			* \return Normally distributed double with mean \p 0.0 and standard deviat
			ion \p 1.0
			*/
			QUALIFIERS double curand_normal_double(curandStateScrambledSobol64_t *state
			)
			{
			return _curand_normal_icdf_double(curand(state));
			}
			// begin log_normal
			/**
			* \brief Return a log-normally distributed float from an XORWOW generator.
			*
			* Return a single log-normally distributed float derived from a normal
			* distribution with mean \p mean and standard deviation \p stddev
			* from the XORWOW generator in \p state,
			* increment position of generator by one.
			*
			* The implementation uses a Box-Muller transform to generate two
			* normally distributed results, transforms them to log-normal distribution
			,
			* then returns them one at a time.
			* See ::curand_log_normal2() for a more efficient version that returns
			* both results at once.
			*
			* \param state - Pointer to state to update
			* \param mean - Mean of the related normal distribution
			* \param stddev - Standard deviation of the related normal distribution
			*
			* \return Log-normally distributed float with mean \p mean and standard de
			viation \p stddev
			*/
			QUALIFIERS float curand_log_normal(curandStateXORWOW_t *state, float mean,
			float stddev)
			{
			if(state->boxmuller_flag == 0) {
			unsigned int x, y;
			x = curand(state);
			y = curand(state);
			float2 v = _curand_box_muller(x, y);
			state->boxmuller_extra = exp(mean + (stddev * v.y));
			state->boxmuller_flag = 1;
			return exp(mean + (stddev * v.x));
			}
			state->boxmuller_flag = 0;
			return state->boxmuller_extra;
			}
			/**
			* \brief Return two normally distributed floats from an XORWOW generator.
			*
			* Return two log-normally distributed floats derived from a normal
			* distribution with mean \p mean and standard deviation \p stddev
			* from the XORWOW generator in \p state,
			* increment position of generator by two.
			*
			* The implementation uses a Box-Muller transform to generate two
			* normally distributed results, then transforms them to log-normal.
			*
			* \param state - Pointer to state to update
			* \param mean - Mean of the related normal distribution
			* \param stddev - Standard deviation of the related normal distribution
			*
			* \return Log-normally distributed float2 where each element is from a
			* distribution with mean \p mean and standard deviation \p stddev
			*/
			QUALIFIERS float2 curand_log_normal2(curandStateXORWOW_t *state, float mean
			, float stddev)
			{
			float2 v = curand_box_muller(state);
			v.x = exp(mean + (stddev * v.x));
			v.y = exp(mean + (stddev * v.y));
			return v;
			}
			/**
			* \brief Return a log-normally distributed float from a Sobol32 generator.
			*
			* Return a single log-normally distributed float derived from a normal
			* distribution with mean \p mean and standard deviation \p stddev
			* from the Sobol32 generator in \p state,
			* increment position of generator by one.
			*
			* The implementation uses the inverse cumulative distribution function
			* to generate a normally distributed result, then transforms the result
			* to log-normal.
			*
			* \param state - Pointer to state to update
			* \param mean - Mean of the related normal distribution
			* \param stddev - Standard deviation of the related normal distribution
			*
			* \return Log-normally distributed float with mean \p mean and standard de
			viation \p stddev
			*/
			QUALIFIERS float curand_log_normal(curandStateSobol32_t *state, float mean,
			float stddev)
			{
			return exp(mean + (stddev * _curand_normal_icdf(curand(state))));
			}
			/**
			* \brief Return a log-normally distributed float from a scrambled Sobol32
			generator.
			*
			* Return a single log-normally distributed float derived from a normal
			* distribution with mean \p mean and standard deviation \p stddev
			* from the scrambled Sobol32 generator in \p state,
			* increment position of generator by one.
			*
			* The implementation uses the inverse cumulative distribution function
			* to generate a normally distributed result, then transforms the result
			* to log-normal.
			*
			* \param state - Pointer to state to update
			* \param mean - Mean of the related normal distribution
			* \param stddev - Standard deviation of the related normal distribution
			*
			* \return Log-normally distributed float with mean \p mean and standard de
			viation \p stddev
			*/
			QUALIFIERS float curand_log_normal(curandStateScrambledSobol32_t *state, fl
			oat mean, float stddev)
			{
			return exp(mean + (stddev * _curand_normal_icdf(curand(state))));
			}
			/**
			* \brief Return a log-normally distributed float from a Sobol64 generator.
			*
			* Return a single log-normally distributed float derived from a normal
			* distribution with mean \p mean and standard deviation \p stddev
			* from the Sobol64 generator in \p state,
			* increment position of generator by one.
			*
			* The implementation uses the inverse cumulative distribution function
			* to generate normally distributed results, then converts to log-normal
			* distribution.
			*
			* \param state - Pointer to state to update
			* \param mean - Mean of the related normal distribution
			* \param stddev - Standard deviation of the related normal distribution
			*
			* \return Log-normally distributed float with mean \p mean and standard de
			viation \p stddev
			*/
			QUALIFIERS float curand_log_normal(curandStateSobol64_t *state, float mean,
			float stddev)
			{
			return exp(mean + (stddev * _curand_normal_icdf(curand(state))));
			}
			/**
			* \brief Return a log-normally distributed float from a scrambled Sobol64
			generator.
			*
			* Return a single log-normally distributed float derived from a normal
			* distribution with mean \p mean and standard deviation \p stddev
			* from the scrambled Sobol64 generator in \p state,
			* increment position of generator by one.
			*
			* The implementation uses the inverse cumulative distribution function
			* to generate normally distributed results, then converts to log-normal
			* distribution.
			*
			* \param state - Pointer to state to update
			* \param mean - Mean of the related normal distribution
			* \param stddev - Standard deviation of the related normal distribution
			*
			* \return Log-normally distributed float with mean \p mean and standard de
			viation \p stddev
			*/
			QUALIFIERS float curand_log_normal(curandStateScrambledSobol64_t *state, fl
			oat mean, float stddev)
			{
			return exp(mean + (stddev * _curand_normal_icdf(curand(state))));
			}
			/**
			* \brief Return a log-normally distributed double from an XORWOW generator
			.
			*
			* Return a single normally distributed double derived from a normal
			* distribution with mean \p mean and standard deviation \p stddev
			* from the XORWOW generator in \p state,
			* increment position of generator.
			*
			* The implementation uses a Box-Muller transform to generate two
			* normally distributed results, transforms them to log-normal distribution
			,
			* then returns them one at a time.
			* See ::curand_log_normal2_double() for a more efficient version that retu
			rns
			* both results at once.
			*
			* \param state - Pointer to state to update
			* \param mean - Mean of the related normal distribution
			* \param stddev - Standard deviation of the related normal distribution
			*
			* \return Log-normally distributed double with mean \p mean and standard d
			eviation \p stddev
			*/
			QUALIFIERS double curand_log_normal_double(curandStateXORWOW_t *state, doub
			le mean, double stddev)
			{
			if(state->boxmuller_flag == 0) {
			unsigned int x0, x1, y0, y1;
			x0 = curand(state);
			x1 = curand(state);
			y0 = curand(state);
			y1 = curand(state);
			double2 v = _curand_box_muller_double(x0, x1, y0, y1);
			state->boxmuller_extra_double = exp(mean + (stddev * v.y));
			state->boxmuller_flag = 1;
			return exp(mean + (stddev * v.x));
			}
			state->boxmuller_flag = 0;
			return state->boxmuller_extra_double;
			}
			/**
			* \brief Return two log-normally distributed doubles from an XORWOW genera
			tor.
			*
			* Return two log-normally distributed doubles derived from a normal
			* distribution with mean \p mean and standard deviation \p stddev
			* from the XORWOW generator in \p state,
			* increment position of generator by two.
			*
			* The implementation uses a Box-Muller transform to generate two
			* normally distributed results, and transforms them to log-normal distribu
			tion,.
			*
			* \param state - Pointer to state to update
			* \param mean - Mean of the related normal distribution
			* \param stddev - Standard deviation of the related normal distribution
			*
			* \return Log-normally distributed double2 where each element is from a
			* distribution with mean \p mean and standard deviation \p stddev
			*/
			QUALIFIERS double2 curand_log_normal2_double(curandStateXORWOW_t *state, do
			uble mean, double stddev)
			{
			double2 v = curand_box_muller_double(state);
			v.x = exp(mean + (stddev * v.x));
			v.y = exp(mean + (stddev * v.y));
			return v;
			}
			/**
			* \brief Return a log-normally distributed double from a Sobol32 generator
			.
			*
			* Return a single log-normally distributed double derived from a normal
			* distribution with mean \p mean and standard deviation \p stddev
			* from the Sobol32 generator in \p state,
			* increment position of generator by one.
			*
			* The implementation uses the inverse cumulative distribution function
			* to generate normally distributed results, and transforms them into
			* log-normal distribution.
			*
			* \param state - Pointer to state to update
			* \param mean - Mean of the related normal distribution
			* \param stddev - Standard deviation of the related normal distribution
			*
			* \return Log-normally distributed double with mean \p mean and standard d
			eviation \p stddev
			*/
			QUALIFIERS double curand_log_normal_double(curandStateSobol32_t *state, dou
			ble mean, double stddev)
			{
			return exp(mean + (stddev * _curand_normal_icdf_double(curand(state))))
			;
			}
			/**
			* \brief Return a log-normally distributed double from a scrambled Sobol32
			generator.
			*
			* Return a single log-normally distributed double derived from a normal
			* distribution with mean \p mean and standard deviation \p stddev
			* from the scrambled Sobol32 generator in \p state,
			* increment position of generator by one.
			*
			* The implementation uses the inverse cumulative distribution function
			* to generate normally distributed results, and transforms them into
			* log-normal distribution.
			*
			* \param state - Pointer to state to update
			* \param mean - Mean of the related normal distribution
			* \param stddev - Standard deviation of the related normal distribution
			*
			* \return Log-normally distributed double with mean \p mean and standard d
			eviation \p stddev
			*/
			QUALIFIERS double curand_log_normal_double(curandStateScrambledSobol32_t *s
			tate, double mean, double stddev)
			{
			return exp(mean + (stddev * _curand_normal_icdf_double(curand(state))))
			;
			}
			/**
			* \brief Return a log-normally distributed double from a Sobol64 generator
			.
			*
			* Return a single normally distributed double derived from a normal
			* distribution with mean \p mean and standard deviation \p stddev
			* from the Sobol64 generator in \p state,
			* increment position of generator by one.
			*
			* The implementation uses the inverse cumulative distribution function
			* to generate normally distributed results.
			*
			* \param state - Pointer to state to update
			* \param mean - Mean of the related normal distribution
			* \param stddev - Standard deviation of the related normal distribution
			*
			* \return Log-normally distributed double with mean \p mean and standard d
			eviation \p stddev
			*/
			QUALIFIERS double curand_log_normal_double(curandStateSobol64_t *state, dou
			ble mean, double stddev)
			{
			return exp(mean + (stddev * _curand_normal_icdf_double(curand(state))))
			;
			}
			/**
			* \brief Return a log-normally distributed double from a scrambled Sobol64
			generator.
			*
			* Return a single normally distributed double derived from a normal
			* distribution with mean \p mean and standard deviation \p stddev
			* from the scrambled Sobol64 generator in \p state,
			* increment position of generator by one.
			*
			* The implementation uses the inverse cumulative distribution function
			* to generate normally distributed results.
			*
			* \param state - Pointer to state to update
			* \param mean - Mean of the related normal distribution
			* \param stddev - Standard deviation of the related normal distribution
			*
			* \return Log-normally distributed double with mean \p mean and standard d
			eviation \p stddev
			*/
			QUALIFIERS double curand_log_normal_double(curandStateScrambledSobol64_t *s
			tate, double mean, double stddev)
			{
			return exp(mean + (stddev * _curand_normal_icdf_double(curand(state))))
			;
			}
	__host__ __device__ static unsigned int *__get_precalculated_matrix(int n)		__host__ __device__ static unsigned int *__get_precalculated_matrix(int n)
	{		{
	if(n > 0) {		if(n == 0) {
	return precalc_xorwow_matrix[n];		return precalc_xorwow_matrix[n];
	} else {		}
			if(n == 1) {
	return precalc_xorwow_matrix_host[n];		return precalc_xorwow_matrix_host[n];
	}		}
			if(n == 2) {
			return precalc_xorwow_offset_matrix[n];
			}
			if(n == 3) {
			return precalc_xorwow_offset_matrix_host[n];
			}
			return precalc_xorwow_matrix[n];
	}		}
	#endif // !defined(CURAND_KERNEL_H_)		#endif // !defined(CURAND_KERNEL_H_)
End of changes. 41 change blocks.
	84 lines changed or deleted		1061 lines changed or added

	curand_precalc.h		curand_precalc.h
	skipping to change at line 17		skipping to change at line 17
	#define XORWOW_SEQUENCE_SPACING (67)		#define XORWOW_SEQUENCE_SPACING (67)
	static __constant__ unsigned int precalc_xorwow_matrix[8][800] = {		static __constant__ unsigned int precalc_xorwow_matrix[8][800] = {
	{850664906UL, 2293210629UL, 1517805917UL, 1215500405UL, 1612415445UL, 64538 8200UL, 824349799UL, 3517232886UL, 4075591755UL, 3089899292UL, 4249786064UL , 3811424903UL, 1100783479UL, 53649761UL, 2817264826UL, 3159462529UL, 16548 48550UL, 950025444UL, 3095510002UL, 4080567211UL, 4111078399UL, 3241719305U L, 2788212779UL, 4256963770UL, 2426893717UL, 4190211142UL, 1420776905UL, 37 80537969UL, 1102912875UL, 1657948873UL, 3354905256UL, 2519610308UL, 5157776 63UL, 3396785394UL, 1832603711UL, 1154211550UL, 1915690212UL, 1933919046UL, 789578337UL, 337961173UL, 1359089498UL, 2249086205UL, 3417955173UL, 862571 348UL, 528120760UL, 1265685672UL, 1970052076UL, 3585976752UL, 3645339918UL, 312171257UL, 1360991400UL, 1994321680UL, 2327168468UL, 2540437053UL, 11804 83641UL, 2217962701UL, 182726833UL, 590204372UL, 1904496495UL, 2545607041UL , 3697978033UL, 1084030545UL, 3397906968UL, 2192325323UL, 2704204176UL, 106 9092002UL, 2364406907UL, 1578647245UL, 3561974633UL, 3437665426UL, 14641273 05UL, 1616628807UL, 2243114101UL, 3639967880UL, 1702613633UL, 2437350057UL, 39991274UL, 2024323584UL, 3795072940UL, 3604530798UL, 443099203UL, 6435362 12UL, 1919517328UL, 3931285769UL, 427935569UL, 276421624UL, 2492081750UL, 2 62729512UL, 3088549877UL, 2922650665UL, 1816283755UL, 4246096489UL, 8425759 14UL, 1460435650UL, 3050522190UL, 2640849794UL, 3697925816UL, 3465779075UL, 3856929655UL, 1365559780UL, 2897029415UL, 2747033756UL, 3611830629UL, 1891 542518UL, 1897590206UL, 437451803UL, 677924906UL, 123809117UL, 3940574372UL , 687640291UL, 3488484529UL, 470218446UL, 1092571016UL, 1537938503UL, 10733 23937UL, 611300083UL, 3809285994UL, 3975678726UL, 925845389UL, 2514775760UL , 2859302390UL, 2761919483UL, 993285307UL, 164095287UL, 3736193671UL, 20789 46336UL, 1418537059UL, 1202525920UL, 4234029440UL, 1313593624UL, 2484428922 UL, 1833969372UL, 661495122UL, 2217907395UL, 2795045321UL, 2950835531UL, 14 02379354UL, 351314168UL, 1902476749UL, 1914974334UL, 2873973176UL, 13212036 03UL, 3316118265UL, 3282193947UL, 1342191737UL, 793441242UL, 3281524559UL, 296088733UL, 487851702UL, 712098215UL, 1388727135UL, 1705533557UL, 35578002 92UL, 399729516UL, 1355829467UL, 291276309UL, 421164833UL, 1318404599UL, 20 64519128UL, 1161612642UL, 2076623594UL, 850664906UL, 2293210629UL, 15178059 17UL, 1215500405UL, 3847487204UL, 645388200UL, 824349799UL, 3517232886UL, 4 075591755UL, 2755872609UL, 4249786064UL, 3811424903UL, 1100783479UL, 536497 61UL, 1417544262UL, 3159462529UL, 1654848550UL, 950025444UL, 3095510002UL, 1908900347UL, 4111078399UL, 3241719305UL, 2788212779UL, 4256963770UL, 37502 58343UL, 4190211142UL, 1420776905UL, 3780537969UL, 1102912875UL, 1690550UL, 3354905256UL, 2519610308UL, 515777663UL, 3396785394UL, 2658162202UL, 11542 11550UL, 1915690212UL, 1933919046UL, 789578337UL, 189880016UL, 1359089498UL , 2249086205UL, 3417955173UL, 862571348UL, 998719835UL, 1265685672UL, 19700 52076UL, 3585976752UL, 3645339918UL, 2973042959UL, 1360991400UL, 1994321680 UL, 2327168468UL, 2540437053UL, 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1418537059UL, 1202525920UL, 423 4029440UL, 704759480UL, 2484428922UL, 1833969372UL, 661495122UL, 2217907395 UL, 3287413716UL, 2950835531UL, 1402379354UL, 351314168UL, 1902476749UL, 20 33316109UL, 2873973176UL, 1321203603UL, 3316118265UL, 3282193947UL, 1316780 684UL, 793441242UL, 3281524559UL, 296088733UL, 487851702UL, 314311643UL, 13 88727135UL, 1705533557UL, 3557800292UL, 399729516UL, 1660074989UL, 29127630 9UL, 421164833UL, 1318404599UL, 2064519128UL, 3156334112UL, 2076623594UL, 8 50664906UL, 2293210629UL, 1517805917UL, 335452425UL, 3847487204UL, 64538820 0UL, 824349799UL, 3517232886UL, 954487767UL, 2755872609UL, 4249786064UL, 38 11424903UL, 1100783479UL, 3408594583UL, 1417544262UL, 3159462529UL, 1654848 550UL, 950025444UL, 324339737UL, 1908900347UL, 4111078399UL, 3241719305UL, 2788212779UL, 1890540205UL, 3750258343UL, 4190211142UL, 1420776905UL, 37805 37969UL, 3716648585UL, 1690550UL, 3354905256UL, 2519610308UL, 515777663UL, 3758156132UL, 2658162202UL, 1154211550UL, 1915690212UL, 1933919046UL, 84414 9171UL, 189880016UL, 1359089498UL, 2249086205UL, 3417955173UL, 1031812215UL , 998719835UL, 1265685672UL, 1970052076UL, 3585976752UL, 3174204115UL, 2973 042959UL, 1360991400UL, 1994321680UL, 2327168468UL, 714016907UL, 2283905032 UL, 2217962701UL, 182726833UL, 590204372UL, 2151450260UL, 110719262UL, 3697 978033UL, 1084030545UL, 3397906968UL, 767772303UL, 4133333579UL, 1069092002 UL, 2364406907UL, 1578647245UL, 42955292UL, 3629845331UL, 1464127305UL, 161 6628807UL, 2243114101UL, 3222189776UL, 3256744141UL, 2437350057UL, 39991274 UL, 2024323584UL, 3142424684UL, 1024703328UL, 443099203UL, 643536212UL, 191 9517328UL, 918511196UL, 2755167056UL, 276421624UL, 2492081750UL, 262729512U L, 4246877536UL, 2817867653UL, 1816283755UL, 4246096489UL, 842575914UL, 142 5765936UL, 2276077438UL, 2640849794UL, 3697925816UL, 3465779075UL, 14917025 26UL, 130551477UL, 2897029415UL, 2747033756UL, 3611830629UL, 1844578694UL, 804565809UL, 437451803UL, 677924906UL, 123809117UL, 3419189841UL, 244661074 9UL, 3488484529UL, 470218446UL, 1092571016UL, 3272535988UL, 1502147484UL, 6 11300083UL, 3809285994UL, 3975678726UL, 2853681168UL, 872826112UL, 28593023 90UL, 2761919483UL, 993285307UL, 1434560128UL, 3901654538UL, 2078946336UL, 1418537059UL, 1202525920UL, 2530097881UL, 704759480UL, 2484428922UL, 183396 9372UL, 661495122UL, 503878844UL, 3287413716UL, 2950835531UL, 1402379354UL, 351314168UL, 4131886119UL, 2033316109UL, 2873973176UL, 1321203603UL, 33161 18265UL, 237900321UL, 1316780684UL, 793441242UL, 3281524559UL, 296088733UL, 1730738847UL, 314311643UL, 1388727135UL, 1705533557UL, 3557800292UL, 15538 35665UL, 1660074989UL, 291276309UL, 421164833UL, 1318404599UL, 964731488UL, 3156334112UL, 2076623594UL, 850664906UL, 2293210629UL, 1105350579UL, 33545 2425UL, 3847487204UL, 645388200UL, 824349799UL, 2789953706UL, 954487767UL, 2755872609UL, 4249786064UL, 3811424903UL, 3937839949UL, 3408594583UL, 14175 44262UL, 3159462529UL, 1654848550UL, 624060530UL, 324339737UL, 1908900347UL , 4111078399UL, 3241719305UL, 2294919498UL, 1890540205UL, 3750258343UL, 419 0211142UL, 1420776905UL, 2279133729UL, 3716648585UL, 1690550UL, 3354905256U L, 2519610308UL, 3563975602UL, 3758156132UL, 2658162202UL, 1154211550UL, 19 15690212UL, 3505586122UL, 844149171UL, 189880016UL, 1359089498UL, 224908620 5UL, 2389487504UL, 1031812215UL, 998719835UL, 1265685672UL, 1970052076UL, 2 798611919UL, 3174204115UL, 2973042959UL, 1360991400UL, 1994321680UL, 168413 4678UL, 714016907UL, 2283905032UL, 2217962701UL, 182726833UL, 1734988742UL, 2151450260UL, 110719262UL, 3697978033UL, 1084030545UL, 159906818UL, 767772 303UL, 4133333579UL, 1069092002UL, 2364406907UL, 1290801202UL, 42955292UL, 3629845331UL, 1464127305UL, 1616628807UL, 987794861UL, 3222189776UL, 325674 4141UL, 2437350057UL, 39991274UL, 3644076751UL, 3142424684UL, 1024703328UL, 443099203UL, 643536212UL, 1487589384UL, 918511196UL, 2755167056UL, 2764216 24UL, 2492081750UL, 137688638UL, 4246877536UL, 2817867653UL, 1816283755UL, 4246096489UL, 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415739351UL, 817762972UL , 3768072560UL, 1931430949UL, 2698979439UL, 3481477932UL, 1994322914UL, 407 8299950UL, 1268233995UL, 3254069145UL, 91029129UL, 498234704UL, 1636613942U L, 3710087092UL, 3876816560UL, 3510446387UL, 3870169008UL, 1370156410UL, 24 42498047UL, 2324396523UL, 1258730334UL, 621954739UL, 1053015373UL, 49182071 7UL, 3386515432UL, 2203703266UL, 120167176UL, 2383669740UL, 1038666440UL, 2 927342870UL, 3583197824UL, 1236241846UL, 2474675929UL, 679052891UL, 2451259 584UL, 2177706146UL, 606842882UL, 3546980104UL, 2289281509UL, 353873434UL, 2041926837UL, 1238346748UL, 2729109726UL, 2843938395UL, 2938124210UL, 25544 43866UL, 1494477920UL, 693378319UL, 2020963566UL, 2000385949UL, 3744098787U L, 650307220UL, 2631327075UL, 1529128757UL, 595871428UL, 3206666562UL, 4580 62987UL, 875238192UL, 3729317374UL, 1368843921UL, 3478430230UL, 3234384578U L, 3232435428UL, 321359326UL, 994274524UL, 361184397UL, 4285497594UL, 91526 3578UL, 1486882838UL, 9988613UL, 829077170UL, 677216046UL, 4141828204UL, 16 5804609UL, 1086678519UL, 2933434608UL, 1351662802UL, 2640085040UL, 26115029 32UL, 2033698714UL, 2008873254UL, 3995557835UL, 1020873906UL, 67873555UL, 2 230337823UL, 1263800417UL, 1148712155UL, 3985159589UL, 2979503513UL, 285471 4997UL, 1539343345UL, 2751484352UL, 1569100732UL, 2020758949UL, 2126757134U L, 3426641899UL, 2808587825UL, 1953320148UL, 1096398464UL, 1502907172UL, 37 51230087UL, 765557661UL, 765290990UL, 3056075500UL, 2040620632UL, 422573751 UL, 3613558930UL, 1741145769UL, 273531216UL, 837238736UL, 494297893UL, 2903 251124UL, 1636782182UL, 4256592784UL, 3652746656UL, 4258393217UL, },
	};		};
	static unsigned int precalc_xorwow_matrix_host[8][800] = {		static unsigned int precalc_xorwow_matrix_host[8][800] = {
	{850664906UL, 2293210629UL, 1517805917UL, 1215500405UL, 1612415445UL, 64538 8200UL, 824349799UL, 3517232886UL, 4075591755UL, 3089899292UL, 4249786064UL , 3811424903UL, 1100783479UL, 53649761UL, 2817264826UL, 3159462529UL, 16548 48550UL, 950025444UL, 3095510002UL, 4080567211UL, 4111078399UL, 3241719305U L, 2788212779UL, 4256963770UL, 2426893717UL, 4190211142UL, 1420776905UL, 37 80537969UL, 1102912875UL, 1657948873UL, 3354905256UL, 2519610308UL, 5157776 63UL, 3396785394UL, 1832603711UL, 1154211550UL, 1915690212UL, 1933919046UL, 789578337UL, 337961173UL, 1359089498UL, 2249086205UL, 3417955173UL, 862571 348UL, 528120760UL, 1265685672UL, 1970052076UL, 3585976752UL, 3645339918UL, 312171257UL, 1360991400UL, 1994321680UL, 2327168468UL, 2540437053UL, 11804 83641UL, 2217962701UL, 182726833UL, 590204372UL, 1904496495UL, 2545607041UL , 3697978033UL, 1084030545UL, 3397906968UL, 2192325323UL, 2704204176UL, 106 9092002UL, 2364406907UL, 1578647245UL, 3561974633UL, 3437665426UL, 14641273 05UL, 1616628807UL, 2243114101UL, 3639967880UL, 1702613633UL, 2437350057UL, 39991274UL, 2024323584UL, 3795072940UL, 3604530798UL, 443099203UL, 6435362 12UL, 1919517328UL, 3931285769UL, 427935569UL, 276421624UL, 2492081750UL, 2 62729512UL, 3088549877UL, 2922650665UL, 1816283755UL, 4246096489UL, 8425759 14UL, 1460435650UL, 3050522190UL, 2640849794UL, 3697925816UL, 3465779075UL, 3856929655UL, 1365559780UL, 2897029415UL, 2747033756UL, 3611830629UL, 1891 542518UL, 1897590206UL, 437451803UL, 677924906UL, 123809117UL, 3940574372UL , 687640291UL, 3488484529UL, 470218446UL, 1092571016UL, 1537938503UL, 10733 23937UL, 611300083UL, 3809285994UL, 3975678726UL, 925845389UL, 2514775760UL , 2859302390UL, 2761919483UL, 993285307UL, 164095287UL, 3736193671UL, 20789 46336UL, 1418537059UL, 1202525920UL, 4234029440UL, 1313593624UL, 2484428922 UL, 1833969372UL, 661495122UL, 2217907395UL, 2795045321UL, 2950835531UL, 14 02379354UL, 351314168UL, 1902476749UL, 1914974334UL, 2873973176UL, 13212036 03UL, 3316118265UL, 3282193947UL, 1342191737UL, 793441242UL, 3281524559UL, 296088733UL, 487851702UL, 712098215UL, 1388727135UL, 1705533557UL, 35578002 92UL, 399729516UL, 1355829467UL, 291276309UL, 421164833UL, 1318404599UL, 20 64519128UL, 1161612642UL, 2076623594UL, 850664906UL, 2293210629UL, 15178059 17UL, 1215500405UL, 3847487204UL, 645388200UL, 824349799UL, 3517232886UL, 4 075591755UL, 2755872609UL, 4249786064UL, 3811424903UL, 1100783479UL, 536497 61UL, 1417544262UL, 3159462529UL, 1654848550UL, 950025444UL, 3095510002UL, 1908900347UL, 4111078399UL, 3241719305UL, 2788212779UL, 4256963770UL, 37502 58343UL, 4190211142UL, 1420776905UL, 3780537969UL, 1102912875UL, 1690550UL, 3354905256UL, 2519610308UL, 515777663UL, 3396785394UL, 2658162202UL, 11542 11550UL, 1915690212UL, 1933919046UL, 789578337UL, 189880016UL, 1359089498UL , 2249086205UL, 3417955173UL, 862571348UL, 998719835UL, 1265685672UL, 19700 52076UL, 3585976752UL, 3645339918UL, 2973042959UL, 1360991400UL, 1994321680 UL, 2327168468UL, 2540437053UL, 2283905032UL, 2217962701UL, 182726833UL, 59 0204372UL, 1904496495UL, 110719262UL, 3697978033UL, 1084030545UL, 339790696 8UL, 2192325323UL, 4133333579UL, 1069092002UL, 2364406907UL, 1578647245UL, 3561974633UL, 3629845331UL, 1464127305UL, 1616628807UL, 2243114101UL, 36399 67880UL, 3256744141UL, 2437350057UL, 39991274UL, 2024323584UL, 3795072940UL , 1024703328UL, 443099203UL, 643536212UL, 1919517328UL, 3931285769UL, 27551 67056UL, 276421624UL, 2492081750UL, 262729512UL, 3088549877UL, 2817867653UL , 1816283755UL, 4246096489UL, 842575914UL, 1460435650UL, 2276077438UL, 2640 849794UL, 3697925816UL, 3465779075UL, 3856929655UL, 130551477UL, 2897029415 UL, 2747033756UL, 3611830629UL, 1891542518UL, 804565809UL, 437451803UL, 677 924906UL, 123809117UL, 3940574372UL, 2446610749UL, 3488484529UL, 470218446U L, 1092571016UL, 1537938503UL, 1502147484UL, 611300083UL, 3809285994UL, 397 5678726UL, 925845389UL, 872826112UL, 2859302390UL, 2761919483UL, 993285307U L, 164095287UL, 3901654538UL, 2078946336UL, 1418537059UL, 1202525920UL, 423 4029440UL, 704759480UL, 2484428922UL, 1833969372UL, 661495122UL, 2217907395 UL, 3287413716UL, 2950835531UL, 1402379354UL, 351314168UL, 1902476749UL, 20 33316109UL, 2873973176UL, 1321203603UL, 3316118265UL, 3282193947UL, 1316780 684UL, 793441242UL, 3281524559UL, 296088733UL, 487851702UL, 314311643UL, 13 88727135UL, 1705533557UL, 3557800292UL, 399729516UL, 1660074989UL, 29127630 9UL, 421164833UL, 1318404599UL, 2064519128UL, 3156334112UL, 2076623594UL, 8 50664906UL, 2293210629UL, 1517805917UL, 335452425UL, 3847487204UL, 64538820 0UL, 824349799UL, 3517232886UL, 954487767UL, 2755872609UL, 4249786064UL, 38 11424903UL, 1100783479UL, 3408594583UL, 1417544262UL, 3159462529UL, 1654848 550UL, 950025444UL, 324339737UL, 1908900347UL, 4111078399UL, 3241719305UL, 2788212779UL, 1890540205UL, 3750258343UL, 4190211142UL, 1420776905UL, 37805 37969UL, 3716648585UL, 1690550UL, 3354905256UL, 2519610308UL, 515777663UL, 3758156132UL, 2658162202UL, 1154211550UL, 1915690212UL, 1933919046UL, 84414 9171UL, 189880016UL, 1359089498UL, 2249086205UL, 3417955173UL, 1031812215UL , 998719835UL, 1265685672UL, 1970052076UL, 3585976752UL, 3174204115UL, 2973 042959UL, 1360991400UL, 1994321680UL, 2327168468UL, 714016907UL, 2283905032 UL, 2217962701UL, 182726833UL, 590204372UL, 2151450260UL, 110719262UL, 3697 978033UL, 1084030545UL, 3397906968UL, 767772303UL, 4133333579UL, 1069092002 UL, 2364406907UL, 1578647245UL, 42955292UL, 3629845331UL, 1464127305UL, 161 6628807UL, 2243114101UL, 3222189776UL, 3256744141UL, 2437350057UL, 39991274 UL, 2024323584UL, 3142424684UL, 1024703328UL, 443099203UL, 643536212UL, 191 9517328UL, 918511196UL, 2755167056UL, 276421624UL, 2492081750UL, 262729512U L, 4246877536UL, 2817867653UL, 1816283755UL, 4246096489UL, 842575914UL, 142 5765936UL, 2276077438UL, 2640849794UL, 3697925816UL, 3465779075UL, 14917025 26UL, 130551477UL, 2897029415UL, 2747033756UL, 3611830629UL, 1844578694UL, 804565809UL, 437451803UL, 677924906UL, 123809117UL, 3419189841UL, 244661074 9UL, 3488484529UL, 470218446UL, 1092571016UL, 3272535988UL, 1502147484UL, 6 11300083UL, 3809285994UL, 3975678726UL, 2853681168UL, 872826112UL, 28593023 90UL, 2761919483UL, 993285307UL, 1434560128UL, 3901654538UL, 2078946336UL, 1418537059UL, 1202525920UL, 2530097881UL, 704759480UL, 2484428922UL, 183396 9372UL, 661495122UL, 503878844UL, 3287413716UL, 2950835531UL, 1402379354UL, 351314168UL, 4131886119UL, 2033316109UL, 2873973176UL, 1321203603UL, 33161 18265UL, 237900321UL, 1316780684UL, 793441242UL, 3281524559UL, 296088733UL, 1730738847UL, 314311643UL, 1388727135UL, 1705533557UL, 3557800292UL, 15538 35665UL, 1660074989UL, 291276309UL, 421164833UL, 1318404599UL, 964731488UL, 3156334112UL, 2076623594UL, 850664906UL, 2293210629UL, 1105350579UL, 33545 2425UL, 3847487204UL, 645388200UL, 824349799UL, 2789953706UL, 954487767UL, 2755872609UL, 4249786064UL, 3811424903UL, 3937839949UL, 3408594583UL, 14175 44262UL, 3159462529UL, 1654848550UL, 624060530UL, 324339737UL, 1908900347UL , 4111078399UL, 3241719305UL, 2294919498UL, 1890540205UL, 3750258343UL, 419 0211142UL, 1420776905UL, 2279133729UL, 3716648585UL, 1690550UL, 3354905256U L, 2519610308UL, 3563975602UL, 3758156132UL, 2658162202UL, 1154211550UL, 19 15690212UL, 3505586122UL, 844149171UL, 189880016UL, 1359089498UL, 224908620 5UL, 2389487504UL, 1031812215UL, 998719835UL, 1265685672UL, 1970052076UL, 2 798611919UL, 3174204115UL, 2973042959UL, 1360991400UL, 1994321680UL, 168413 4678UL, 714016907UL, 2283905032UL, 2217962701UL, 182726833UL, 1734988742UL, 2151450260UL, 110719262UL, 3697978033UL, 1084030545UL, 159906818UL, 767772 303UL, 4133333579UL, 1069092002UL, 2364406907UL, 1290801202UL, 42955292UL, 3629845331UL, 1464127305UL, 1616628807UL, 987794861UL, 3222189776UL, 325674 4141UL, 2437350057UL, 39991274UL, 3644076751UL, 3142424684UL, 1024703328UL, 443099203UL, 643536212UL, 1487589384UL, 918511196UL, 2755167056UL, 2764216 24UL, 2492081750UL, 137688638UL, 4246877536UL, 2817867653UL, 1816283755UL, 4246096489UL, 1518475380UL, 1425765936UL, 2276077438UL, 2640849794UL, 36979 25816UL, 4226506771UL, 1491702526UL, 130551477UL, 2897029415UL, 2747033756U L, 2033599579UL, 1844578694UL, 804565809UL, 437451803UL, 677924906UL, 27490 65512UL, 3419189841UL, 2446610749UL, 3488484529UL, 470218446UL, 290444026UL , 3272535988UL, 1502147484UL, 611300083UL, 3809285994UL, 2546040767UL, 2853 681168UL, 872826112UL, 2859302390UL, 2761919483UL, 4097961150UL, 1434560128 UL, 3901654538UL, 2078946336UL, 1418537059UL, 2725734455UL, 2530097881UL, 7 04759480UL, 2484428922UL, 1833969372UL, 3999408333UL, 503878844UL, 32874137 16UL, 2950835531UL, 1402379354UL, 3861442503UL, 4131886119UL, 2033316109UL, 2873973176UL, 1321203603UL, 1267331405UL, 237900321UL, 1316780684UL, 79344 1242UL, 3281524559UL, 1273427916UL, 1730738847UL, 314311643UL, 1388727135UL , 1705533557UL, 1474310231UL, 1553835665UL, 1660074989UL, 291276309UL, 4211 64833UL, 3884815658UL, 3088049345UL, 3307042227UL, 3228948601UL, 1717605083 UL, 1864502063UL, 3799516572UL, 2372822470UL, 2691586476UL, 1172840854UL, 1 577099080UL, 870101866UL, 2139291021UL, 406996656UL, 255568268UL, 897760202 UL, 674745664UL, 885214361UL, 3753233375UL, 3015215223UL, 1711461259UL, 324 1363282UL, 2125360928UL, 2493601640UL, 2350228245UL, 3434627328UL, 20956429 63UL, 3360932494UL, 3287396242UL, 4070512427UL, 3415702664UL, 1958354224UL, 3280206940UL, 3929504236UL, 3390499817UL, 4144225735UL, 3621750606UL, 3205 006592UL, 3495743785UL, 269239326UL, 2181299371UL, 2898796651UL, 2613623219 UL, 3988711298UL, 2162437858UL, 949553433UL, 3289670000UL, 3559525307UL, 33 66925567UL, 2112148665UL, 955626393UL, 1790865381UL, 699223558UL, 388958430 1UL, 1020750250UL, 4105283899UL, 2295851818UL, 4045668915UL, 2224770025UL, 766386910UL, 4265157386UL, 89139307UL, 2099710177UL, 1012450874UL, 18754924 46UL, 1927399417UL, 767450812UL, 654474783UL, 4265293038UL, 4041215389UL, 4 102336947UL, 4263617328UL, 2135826340UL, 2317231535UL, 3773895729UL, 403151 111UL, 1400693138UL, 4255050194UL, 755369466UL, 2325764302UL, 2617301159UL, 4165707294UL, 1206304709UL, 2415645397UL, 4276004841UL, 1457022279UL, 6626 60652UL, 795140282UL, 828519889UL, 805830562UL, 1179976369UL, 2212548232UL, 755708248UL, 1034682071UL, 899950902UL, 1906046264UL, 1861009040UL, 310711 525UL, 920739741UL, 2322414272UL, 3179236470UL, 81822135UL, 4111390320UL, 1 800166783UL, 112253014UL, 688771939UL, 1050990794UL, 3124647483UL, 28705217 1UL, 1363630156UL, 3447798279UL, 1405733552UL, 3075862538UL, 1682808202UL, 1595154222UL, 1173705692UL, 680713285UL, 2748212230UL, 568610527UL, 3434965 538UL, 1114942930UL, 2835858745UL, 2575992250UL, 3243355150UL, 2127580225UL , 1855934450UL, 3915941751UL, 2228679809UL, 1514780124UL, 1506688039UL, 103 3083295UL, 793807083UL, 1120681149UL, 4105670165UL, 3999570340UL, 208302013 1UL, 1213356023UL, 3684882757UL, 3375797774UL, 3577986103UL, 2092046164UL, 2593847443UL, 1826450612UL, 367828409UL, 3198272513UL, 1941316667UL, 943707 510UL, 907134807UL, 2020457947UL, 1462193665UL, 2964617539UL, 4216491663UL, 2625270800UL, 2395371467UL, 3691003028UL, 3659016793UL, 2381847054UL, 3513 105567UL, 3013019506UL, 2731245927UL, }, {1680024716UL, 2112340059UL, 33874 75367UL, 2080916186UL, 1431532386UL, 3907378472UL, 2636491350UL, 2176128529 UL, 2236616671UL, 3736851460UL, 2604001339UL, 3893075234UL, 3495918635UL, 4 116370522UL, 1384310379UL, 3660102574UL, 2030233939UL, 2759207091UL, 493479 23UL, 97526506UL, 2566932710UL, 1566181275UL, 3127827248UL, 578401670UL, 14 99229308UL, 2581732444UL, 279715551UL, 809690877UL, 1438444015UL, 878935323 UL, 1495277039UL, 3417305339UL, 2858903785UL, 3074075088UL, 603749086UL, 23 70669734UL, 391683868UL, 3933465331UL, 2884128106UL, 1478317876UL, 18649883 35UL, 2925823809UL, 4133578805UL, 218104493UL, 368652174UL, 1998600344UL, 1 109346044UL, 1716435313UL, 415435111UL, 91393686UL, 2536620737UL, 144006857 3UL, 481874870UL, 142128108UL, 988825519UL, 2077118779UL, 2858045339UL, 406 8162251UL, 115593872UL, 1364244587UL, 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1738833632UL, 973022 696UL, 519684794UL, 2291130512UL, 503032614UL, 3658059597UL, 1500301452UL, 2400186105UL, 3498787788UL, 422428426UL, 3879256913UL, 3923611748UL, 412070 4752UL, 298972869UL, 1718342816UL, 2541691685UL, 2323881484UL, 1686027891UL , 4223212804UL, 1812334632UL, 3717655725UL, 2109094458UL, 642431972UL, 2621 896355UL, 2572404999UL, 3590094954UL, 1837882537UL, 2949609717UL, 139235303 2UL, 1733137169UL, 2674005018UL, 3252348987UL, 1526077846UL, 451078217UL, 2 591688848UL, 12635611UL, 3971261781UL, 2417951415UL, 1789383090UL, 11401838 90UL, 1720695967UL, 2906966040UL, 3465605863UL, 2793804971UL, 1054433135UL, 2345087879UL, 915518921UL, 3079297626UL, 1793223686UL, 1055357758UL, 91427 1617UL, 791633499UL, 306802527UL, 2754998083UL, 3179053982UL, 598442002UL, 324402573UL, 2473418737UL, 3566888111UL, 2832258993UL, 4260365359UL, 216804 6398UL, 2097776414UL, 3852730296UL, 3643260763UL, 2163607277UL, 2595175979U L, 3957721904UL, 215187467UL, 2369406909UL, 1929000706UL, 657446369UL, 3779 486126UL, 810692262UL, 1337974799UL, 4030350704UL, 1865557469UL, 1127012865 UL, 1026825278UL, 593628480UL, 1817383139UL, 3414354529UL, 361018423UL, 203 612980UL, 2102950440UL, 3407143936UL, 1739372987UL, 1475218277UL, 282758053 9UL, 305467658UL, 3292706746UL, 825045562UL, 1900092336UL, 3697343089UL, 76 0470915UL, 587414402UL, 2000637694UL, 343303227UL, 3891626781UL, 2757016765 UL, 1090707384UL, 4015377800UL, 745490961UL, 2855539022UL, 36229159UL, 1591 415533UL, 2208656873UL, 3920625546UL, 2614469314UL, 213338525UL, 886212578U L, 2729976209UL, 827771411UL, 2430496777UL, 1198164420UL, 2644841698UL, 192 2667440UL, 2747674190UL, 1461695896UL, 1770331341UL, 1641501876UL, 35753531 1UL, 919857376UL, 3053795110UL, 3533531372UL, 3134337355UL, 1004072597UL, 2 01138876UL, 3221275220UL, 1589659138UL, 294382235UL, 1148950143UL, 42115797 07UL, 1799726917UL, 2838977761UL, 4172949119UL, 892664404UL, 416103844UL, 2 248389027UL, 1790015671UL, 3925738275UL, 2612357890UL, 3481887924UL, 161995 5951UL, 4188275966UL, 1672572975UL, 2005534713UL, 564854400UL, 747724021UL, 4037561738UL, 1605940213UL, 2620990454UL, 604900912UL, 610898209UL, 147324 4091UL, 735444769UL, 3571225334UL, 3036173307UL, 1032410949UL, 3672467167UL , 68684525UL, 3327351604UL, 973022696UL, 519684794UL, 2291130512UL, 5030326 14UL, 3814902238UL, 1500301452UL, 2400186105UL, 3498787788UL, 422428426UL, 1756753750UL, 3923611748UL, 4120704752UL, 298972869UL, 1718342816UL, 652903 081UL, 2323881484UL, 1686027891UL, 4223212804UL, 1812334632UL, 1599640566UL , 2109094458UL, 642431972UL, 2621896355UL, 2572404999UL, 1668409355UL, 1837 882537UL, 2949609717UL, 1392353032UL, 1733137169UL, 3691709793UL, 325234898 7UL, 1526077846UL, 451078217UL, 2591688848UL, 3353622601UL, 3971261781UL, 2 417951415UL, 1789383090UL, 1140183890UL, 4113853791UL, 2906966040UL, 346560 5863UL, 2793804971UL, 1054433135UL, 2195882948UL, 915518921UL, 3079297626UL , 1793223686UL, 1055357758UL, 898713552UL, 791633499UL, 306802527UL, 275499 8083UL, 3179053982UL, 2469350088UL, 324402573UL, 2473418737UL, 3566888111UL , 2832258993UL, 1377718274UL, 2168046398UL, 2097776414UL, 3852730296UL, 364 3260763UL, 3492388484UL, 2595175979UL, 3957721904UL, 215187467UL, 236940690 9UL, 4243449339UL, 657446369UL, 3779486126UL, 810692262UL, 1337974799UL, 39 60230785UL, 1865557469UL, 1127012865UL, 1026825278UL, 593628480UL, 73279331 2UL, 3414354529UL, 361018423UL, 203612980UL, 2102950440UL, 2401792405UL, 17 39372987UL, 1475218277UL, 2827580539UL, 305467658UL, 2454275289UL, 82504556 2UL, 1900092336UL, 3697343089UL, 760470915UL, 2146882409UL, 2000637694UL, 3 43303227UL, 3891626781UL, 2757016765UL, 3997473261UL, 4015377800UL, 7454909 61UL, 2855539022UL, 36229159UL, 2375394427UL, 2208656873UL, 3920625546UL, 2 614469314UL, 213338525UL, 2055366274UL, 2729976209UL, 827771411UL, 24304967 77UL, 1198164420UL, 1789631187UL, 1922667440UL, 2747674190UL, 1461695896UL, 1770331341UL, 4284442852UL, 357535311UL, 919857376UL, 3053795110UL, 353353 1372UL, 2124270060UL, 1004072597UL, 201138876UL, 3221275220UL, 1589659138UL , 1418386120UL, 1148950143UL, 4211579707UL, 1799726917UL, 2838977761UL, 354 0708069UL, 892664404UL, 416103844UL, 2248389027UL, 1790015671UL, 3936883UL, 2612357890UL, 3481887924UL, 1619955951UL, 4188275966UL, 2963623483UL, 2005 534713UL, 564854400UL, 747724021UL, 4037561738UL, 3431155922UL, 2620990454U L, 604900912UL, 610898209UL, 1473244091UL, 3880001339UL, 2879060316UL, 3300 897679UL, 3960972039UL, 3201086624UL, 3814462934UL, 3426650044UL, 193088163 2UL, 1981178788UL, 2956279691UL, 4272406256UL, 372705521UL, 1359389771UL, 1 590302979UL, 3940206208UL, 3817999127UL, 2527835456UL, 2739078164UL, 716997 849UL, 3235607043UL, 2550297745UL, 3688700200UL, 354502605UL, 2285793656UL, 2339138034UL, 3912354142UL, 2262255668UL, 469322622UL, 1319943359UL, 19161 01235UL, 200441823UL, 509436982UL, 2160284593UL, 1687919695UL, 4153615582UL , 495735041UL, 3694469424UL, 2086893117UL, 4223008799UL, 105344742UL, 16980 33424UL, 1149223145UL, 4183918790UL, 4176151950UL, 415739351UL, 817762972UL , 3768072560UL, 1931430949UL, 2698979439UL, 3481477932UL, 1994322914UL, 407 8299950UL, 1268233995UL, 3254069145UL, 91029129UL, 498234704UL, 1636613942U L, 3710087092UL, 3876816560UL, 3510446387UL, 3870169008UL, 1370156410UL, 24 42498047UL, 2324396523UL, 1258730334UL, 621954739UL, 1053015373UL, 49182071 7UL, 3386515432UL, 2203703266UL, 120167176UL, 2383669740UL, 1038666440UL, 2 927342870UL, 3583197824UL, 1236241846UL, 2474675929UL, 679052891UL, 2451259 584UL, 2177706146UL, 606842882UL, 3546980104UL, 2289281509UL, 353873434UL, 2041926837UL, 1238346748UL, 2729109726UL, 2843938395UL, 2938124210UL, 25544 43866UL, 1494477920UL, 693378319UL, 2020963566UL, 2000385949UL, 3744098787U L, 650307220UL, 2631327075UL, 1529128757UL, 595871428UL, 3206666562UL, 4580 62987UL, 875238192UL, 3729317374UL, 1368843921UL, 3478430230UL, 3234384578U L, 3232435428UL, 321359326UL, 994274524UL, 361184397UL, 4285497594UL, 91526 3578UL, 1486882838UL, 9988613UL, 829077170UL, 677216046UL, 4141828204UL, 16 5804609UL, 1086678519UL, 2933434608UL, 1351662802UL, 2640085040UL, 26115029 32UL, 2033698714UL, 2008873254UL, 3995557835UL, 1020873906UL, 67873555UL, 2 230337823UL, 1263800417UL, 1148712155UL, 3985159589UL, 2979503513UL, 285471 4997UL, 1539343345UL, 2751484352UL, 1569100732UL, 2020758949UL, 2126757134U L, 3426641899UL, 2808587825UL, 1953320148UL, 1096398464UL, 1502907172UL, 37 51230087UL, 765557661UL, 765290990UL, 3056075500UL, 2040620632UL, 422573751 UL, 3613558930UL, 1741145769UL, 273531216UL, 837238736UL, 494297893UL, 2903 251124UL, 1636782182UL, 4256592784UL, 3652746656UL, 4258393217UL, },
	};		};
			static __constant__ unsigned int precalc_xorwow_offset_matrix[8][800] = {
			{0UL, 0UL, 0UL, 0UL, 3UL, 0UL, 0UL, 0UL, 0UL, 6UL, 0UL, 0UL, 0UL, 0UL, 15UL
			, 0UL, 0UL, 0UL, 0UL, 30UL, 0UL, 0UL, 0UL, 0UL, 60UL, 0UL, 0UL, 0UL, 0UL, 1
			20UL, 0UL, 0UL, 0UL, 0UL, 240UL, 0UL, 0UL, 0UL, 0UL, 480UL, 0UL, 0UL, 0UL,
			0UL, 960UL, 0UL, 0UL, 0UL, 0UL, 1920UL, 0UL, 0UL, 0UL, 0UL, 3840UL, 0UL, 0U
			L, 0UL, 0UL, 7680UL, 0UL, 0UL, 0UL, 0UL, 15360UL, 0UL, 0UL, 0UL, 0UL, 30720
			UL, 0UL, 0UL, 0UL, 0UL, 61440UL, 0UL, 0UL, 0UL, 0UL, 122880UL, 0UL, 0UL, 0U
			L, 0UL, 245760UL, 0UL, 0UL, 0UL, 0UL, 491520UL, 0UL, 0UL, 0UL, 0UL, 983040U
			L, 0UL, 0UL, 0UL, 0UL, 1966080UL, 0UL, 0UL, 0UL, 0UL, 3932160UL, 0UL, 0UL,
			0UL, 0UL, 7864320UL, 0UL, 0UL, 0UL, 0UL, 15728640UL, 0UL, 0UL, 0UL, 0UL, 31
			457280UL, 0UL, 0UL, 0UL, 0UL, 62914560UL, 0UL, 0UL, 0UL, 0UL, 125829120UL,
			0UL, 0UL, 0UL, 0UL, 251658240UL, 0UL, 0UL, 0UL, 0UL, 503316480UL, 0UL, 0UL,
			0UL, 0UL, 1006632960UL, 0UL, 0UL, 0UL, 0UL, 2013265920UL, 0UL, 0UL, 0UL, 0
			UL, 4026531840UL, 0UL, 0UL, 0UL, 0UL, 3758096384UL, 1UL, 0UL, 0UL, 0UL, 0UL
			, 2UL, 0UL, 0UL, 0UL, 0UL, 4UL, 0UL, 0UL, 0UL, 0UL, 8UL, 0UL, 0UL, 0UL, 0UL
			, 16UL, 0UL, 0UL, 0UL, 0UL, 32UL, 0UL, 0UL, 0UL, 0UL, 64UL, 0UL, 0UL, 0UL,
			0UL, 128UL, 0UL, 0UL, 0UL, 0UL, 256UL, 0UL, 0UL, 0UL, 0UL, 512UL, 0UL, 0UL,
			0UL, 0UL, 1024UL, 0UL, 0UL, 0UL, 0UL, 2048UL, 0UL, 0UL, 0UL, 0UL, 4096UL,
			0UL, 0UL, 0UL, 0UL, 8192UL, 0UL, 0UL, 0UL, 0UL, 16384UL, 0UL, 0UL, 0UL, 0UL
			, 32768UL, 0UL, 0UL, 0UL, 0UL, 65536UL, 0UL, 0UL, 0UL, 0UL, 131072UL, 0UL,
			0UL, 0UL, 0UL, 262144UL, 0UL, 0UL, 0UL, 0UL, 524288UL, 0UL, 0UL, 0UL, 0UL,
			1048576UL, 0UL, 0UL, 0UL, 0UL, 2097152UL, 0UL, 0UL, 0UL, 0UL, 4194304UL, 0U
			L, 0UL, 0UL, 0UL, 8388608UL, 0UL, 0UL, 0UL, 0UL, 16777216UL, 0UL, 0UL, 0UL,
			0UL, 33554432UL, 0UL, 0UL, 0UL, 0UL, 67108864UL, 0UL, 0UL, 0UL, 0UL, 13421
			7728UL, 0UL, 0UL, 0UL, 0UL, 268435456UL, 0UL, 0UL, 0UL, 0UL, 536870912UL, 0
			UL, 0UL, 0UL, 0UL, 1073741824UL, 0UL, 0UL, 0UL, 0UL, 2147483648UL, 0UL, 0UL
			, 0UL, 0UL, 0UL, 1UL, 0UL, 0UL, 0UL, 0UL, 2UL, 0UL, 0UL, 0UL, 0UL, 4UL, 0UL
			, 0UL, 0UL, 0UL, 8UL, 0UL, 0UL, 0UL, 0UL, 16UL, 0UL, 0UL, 0UL, 0UL, 32UL, 0
			UL, 0UL, 0UL, 0UL, 64UL, 0UL, 0UL, 0UL, 0UL, 128UL, 0UL, 0UL, 0UL, 0UL, 256
			UL, 0UL, 0UL, 0UL, 0UL, 512UL, 0UL, 0UL, 0UL, 0UL, 1024UL, 0UL, 0UL, 0UL, 0
			UL, 2048UL, 0UL, 0UL, 0UL, 0UL, 4096UL, 0UL, 0UL, 0UL, 0UL, 8192UL, 0UL, 0U
			L, 0UL, 0UL, 16384UL, 0UL, 0UL, 0UL, 0UL, 32768UL, 0UL, 0UL, 0UL, 0UL, 6553
			6UL, 0UL, 0UL, 0UL, 0UL, 131072UL, 0UL, 0UL, 0UL, 0UL, 262144UL, 0UL, 0UL,
			0UL, 0UL, 524288UL, 0UL, 0UL, 0UL, 0UL, 1048576UL, 0UL, 0UL, 0UL, 0UL, 2097
			152UL, 0UL, 0UL, 0UL, 0UL, 4194304UL, 0UL, 0UL, 0UL, 0UL, 8388608UL, 0UL, 0
			UL, 0UL, 0UL, 16777216UL, 0UL, 0UL, 0UL, 0UL, 33554432UL, 0UL, 0UL, 0UL, 0U
			L, 67108864UL, 0UL, 0UL, 0UL, 0UL, 134217728UL, 0UL, 0UL, 0UL, 0UL, 2684354
			56UL, 0UL, 0UL, 0UL, 0UL, 536870912UL, 0UL, 0UL, 0UL, 0UL, 1073741824UL, 0U
			L, 0UL, 0UL, 0UL, 2147483648UL, 0UL, 0UL, 0UL, 0UL, 0UL, 1UL, 0UL, 0UL, 0UL
			, 0UL, 2UL, 0UL, 0UL, 0UL, 0UL, 4UL, 0UL, 0UL, 0UL, 0UL, 8UL, 0UL, 0UL, 0UL
			, 0UL, 16UL, 0UL, 0UL, 0UL, 0UL, 32UL, 0UL, 0UL, 0UL, 0UL, 64UL, 0UL, 0UL,
			0UL, 0UL, 128UL, 0UL, 0UL, 0UL, 0UL, 256UL, 0UL, 0UL, 0UL, 0UL, 512UL, 0UL,
			0UL, 0UL, 0UL, 1024UL, 0UL, 0UL, 0UL, 0UL, 2048UL, 0UL, 0UL, 0UL, 0UL, 409
			6UL, 0UL, 0UL, 0UL, 0UL, 8192UL, 0UL, 0UL, 0UL, 0UL, 16384UL, 0UL, 0UL, 0UL
			, 0UL, 32768UL, 0UL, 0UL, 0UL, 0UL, 65536UL, 0UL, 0UL, 0UL, 0UL, 131072UL,
			0UL, 0UL, 0UL, 0UL, 262144UL, 0UL, 0UL, 0UL, 0UL, 524288UL, 0UL, 0UL, 0UL,
			0UL, 1048576UL, 0UL, 0UL, 0UL, 0UL, 2097152UL, 0UL, 0UL, 0UL, 0UL, 4194304U
			L, 0UL, 0UL, 0UL, 0UL, 8388608UL, 0UL, 0UL, 0UL, 0UL, 16777216UL, 0UL, 0UL,
			0UL, 0UL, 33554432UL, 0UL, 0UL, 0UL, 0UL, 67108864UL, 0UL, 0UL, 0UL, 0UL,
			134217728UL, 0UL, 0UL, 0UL, 0UL, 268435456UL, 0UL, 0UL, 0UL, 0UL, 536870912
			UL, 0UL, 0UL, 0UL, 0UL, 1073741824UL, 0UL, 0UL, 0UL, 0UL, 2147483648UL, 0UL
			, 0UL, 0UL, 0UL, 0UL, 1UL, 17UL, 0UL, 0UL, 0UL, 2UL, 34UL, 0UL, 0UL, 0UL, 4
			UL, 68UL, 0UL, 0UL, 0UL, 8UL, 136UL, 0UL, 0UL, 0UL, 16UL, 272UL, 0UL, 0UL,
			0UL, 32UL, 544UL, 0UL, 0UL, 0UL, 64UL, 1088UL, 0UL, 0UL, 0UL, 128UL, 2176UL
			, 0UL, 0UL, 0UL, 256UL, 4352UL, 0UL, 0UL, 0UL, 512UL, 8704UL, 0UL, 0UL, 0UL
			, 1024UL, 17408UL, 0UL, 0UL, 0UL, 2048UL, 34816UL, 0UL, 0UL, 0UL, 4096UL, 6
			9632UL, 0UL, 0UL, 0UL, 8192UL, 139264UL, 0UL, 0UL, 0UL, 16384UL, 278528UL,
			0UL, 0UL, 0UL, 32768UL, 557056UL, 0UL, 0UL, 0UL, 65536UL, 1114112UL, 0UL, 0
			UL, 0UL, 131072UL, 2228224UL, 0UL, 0UL, 0UL, 262144UL, 4456448UL, 0UL, 0UL,
			0UL, 524288UL, 8912896UL, 0UL, 0UL, 0UL, 1048576UL, 17825792UL, 0UL, 0UL,
			0UL, 2097152UL, 35651584UL, 0UL, 0UL, 0UL, 4194304UL, 71303168UL, 0UL, 0UL,
			0UL, 8388608UL, 142606336UL, 0UL, 0UL, 0UL, 16777216UL, 285212672UL, 0UL,
			0UL, 0UL, 33554432UL, 570425344UL, 0UL, 0UL, 0UL, 67108864UL, 1140850688UL,
			0UL, 0UL, 0UL, 134217728UL, 2281701376UL, 0UL, 0UL, 0UL, 268435456UL, 2684
			35456UL, 0UL, 0UL, 0UL, 536870912UL, 536870912UL, 0UL, 0UL, 0UL, 1073741824
			UL, 1073741824UL, 0UL, 0UL, 0UL, 2147483648UL, 2147483648UL, }, {0UL, 3UL,
			51UL, 771UL, 13107UL, 0UL, 6UL, 102UL, 1542UL, 26214UL, 0UL, 15UL, 255UL, 3
			855UL, 65535UL, 0UL, 30UL, 510UL, 7710UL, 131070UL, 0UL, 60UL, 1020UL, 1542
			0UL, 262140UL, 0UL, 120UL, 2040UL, 30840UL, 524280UL, 0UL, 240UL, 4080UL, 6
			1680UL, 1048560UL, 0UL, 480UL, 8160UL, 123360UL, 2097120UL, 0UL, 960UL, 163
			20UL, 246720UL, 4194240UL, 0UL, 1920UL, 32640UL, 493440UL, 8388480UL, 0UL,
			3840UL, 65280UL, 986880UL, 16776960UL, 0UL, 7680UL, 130560UL, 1973760UL, 33
			553920UL, 0UL, 15360UL, 261120UL, 3947520UL, 67107840UL, 0UL, 30720UL, 5222
			40UL, 7895040UL, 134215680UL, 0UL, 61440UL, 1044480UL, 15790080UL, 26843136
			0UL, 0UL, 122880UL, 2088960UL, 31580160UL, 536862720UL, 0UL, 245760UL, 4177
			920UL, 63160320UL, 1073725440UL, 0UL, 491520UL, 8355840UL, 126320640UL, 214
			7450880UL, 0UL, 983040UL, 16711680UL, 252641280UL, 4294901760UL, 0UL, 19660
			80UL, 33423360UL, 505282560UL, 4294836224UL, 0UL, 3932160UL, 66846720UL, 10
			10565120UL, 4294705152UL, 0UL, 7864320UL, 133693440UL, 2021130240UL, 429444
			3008UL, 0UL, 15728640UL, 267386880UL, 4042260480UL, 4293918720UL, 0UL, 3145
			7280UL, 534773760UL, 3789553664UL, 4292870144UL, 0UL, 62914560UL, 106954752
			0UL, 3284140032UL, 4290772992UL, 0UL, 125829120UL, 2139095040UL, 2273312768
			UL, 4286578688UL, 0UL, 251658240UL, 4278190080UL, 251658240UL, 4278190080UL
			, 0UL, 503316480UL, 4261412864UL, 503316480UL, 4261412864UL, 0UL, 100663296
			0UL, 4227858432UL, 1006632960UL, 4227858432UL, 0UL, 2013265920UL, 416074956
			8UL, 2013265920UL, 4160749568UL, 0UL, 4026531840UL, 4026531840UL, 402653184
			0UL, 4026531840UL, 0UL, 3758096384UL, 3758096384UL, 3758096384UL, 375809638
			4UL, 0UL, 0UL, 3UL, 51UL, 771UL, 0UL, 0UL, 6UL, 102UL, 1542UL, 0UL, 0UL, 15
			UL, 255UL, 3855UL, 0UL, 0UL, 30UL, 510UL, 7710UL, 0UL, 0UL, 60UL, 1020UL, 1
			5420UL, 0UL, 0UL, 120UL, 2040UL, 30840UL, 0UL, 0UL, 240UL, 4080UL, 61680UL,
			0UL, 0UL, 480UL, 8160UL, 123360UL, 0UL, 0UL, 960UL, 16320UL, 246720UL, 0UL
			, 0UL, 1920UL, 32640UL, 493440UL, 0UL, 0UL, 3840UL, 65280UL, 986880UL, 0UL,
			0UL, 7680UL, 130560UL, 1973760UL, 0UL, 0UL, 15360UL, 261120UL, 3947520UL,
			0UL, 0UL, 30720UL, 522240UL, 7895040UL, 0UL, 0UL, 61440UL, 1044480UL, 15790
			080UL, 0UL, 0UL, 122880UL, 2088960UL, 31580160UL, 0UL, 0UL, 245760UL, 41779
			20UL, 63160320UL, 0UL, 0UL, 491520UL, 8355840UL, 126320640UL, 0UL, 0UL, 983
			040UL, 16711680UL, 252641280UL, 0UL, 0UL, 1966080UL, 33423360UL, 505282560U
			L, 0UL, 0UL, 3932160UL, 66846720UL, 1010565120UL, 0UL, 0UL, 7864320UL, 1336
			93440UL, 2021130240UL, 0UL, 0UL, 15728640UL, 267386880UL, 4042260480UL, 0UL
			, 0UL, 31457280UL, 534773760UL, 3789553664UL, 0UL, 0UL, 62914560UL, 1069547
			520UL, 3284140032UL, 0UL, 0UL, 125829120UL, 2139095040UL, 2273312768UL, 0UL
			, 0UL, 251658240UL, 4278190080UL, 251658240UL, 0UL, 0UL, 503316480UL, 42614
			12864UL, 503316480UL, 0UL, 0UL, 1006632960UL, 4227858432UL, 1006632960UL, 0
			UL, 0UL, 2013265920UL, 4160749568UL, 2013265920UL, 0UL, 0UL, 4026531840UL,
			4026531840UL, 4026531840UL, 0UL, 0UL, 3758096384UL, 3758096384UL, 375809638
			4UL, 0UL, 0UL, 0UL, 3UL, 51UL, 0UL, 0UL, 0UL, 6UL, 102UL, 0UL, 0UL, 0UL, 15
			UL, 255UL, 0UL, 0UL, 0UL, 30UL, 510UL, 0UL, 0UL, 0UL, 60UL, 1020UL, 0UL, 0U
			L, 0UL, 120UL, 2040UL, 0UL, 0UL, 0UL, 240UL, 4080UL, 0UL, 0UL, 0UL, 480UL,
			8160UL, 0UL, 0UL, 0UL, 960UL, 16320UL, 0UL, 0UL, 0UL, 1920UL, 32640UL, 0UL,
			0UL, 0UL, 3840UL, 65280UL, 0UL, 0UL, 0UL, 7680UL, 130560UL, 0UL, 0UL, 0UL,
			15360UL, 261120UL, 0UL, 0UL, 0UL, 30720UL, 522240UL, 0UL, 0UL, 0UL, 61440U
			L, 1044480UL, 0UL, 0UL, 0UL, 122880UL, 2088960UL, 0UL, 0UL, 0UL, 245760UL,
			4177920UL, 0UL, 0UL, 0UL, 491520UL, 8355840UL, 0UL, 0UL, 0UL, 983040UL, 167
			11680UL, 0UL, 0UL, 0UL, 1966080UL, 33423360UL, 0UL, 0UL, 0UL, 3932160UL, 66
			846720UL, 0UL, 0UL, 0UL, 7864320UL, 133693440UL, 0UL, 0UL, 0UL, 15728640UL,
			267386880UL, 0UL, 0UL, 0UL, 31457280UL, 534773760UL, 0UL, 0UL, 0UL, 629145
			60UL, 1069547520UL, 0UL, 0UL, 0UL, 125829120UL, 2139095040UL, 0UL, 0UL, 0UL
			, 251658240UL, 4278190080UL, 0UL, 0UL, 0UL, 503316480UL, 4261412864UL, 0UL,
			0UL, 0UL, 1006632960UL, 4227858432UL, 0UL, 0UL, 0UL, 2013265920UL, 4160749
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			L, 3503319486UL, 647308222UL, 3901603996UL, 3753248472UL, 221887019UL, 4294
			594427UL, 4261179460UL, 3285764480UL, 1861431346UL, 3340918862UL, 212518363
			UL, 1883994950UL, 1506873376UL, 2695939612UL, 430180116UL, 2398655600UL, 23
			89066602UL, 1574939945UL, 2852159074UL, 1683639957UL, 2744363872UL, 2493238
			133UL, 2584034689UL, 1952065633UL, 752704472UL, 2787053497UL, 2031844862UL,
			1272380790UL, 3530505866UL, 1528220628UL, 2342953154UL, 311436877UL, 36277
			85554UL, 3410473245UL, 2576495467UL, 2026656505UL, 2756955968UL, 4289348777
			UL, 2856163034UL, 3778019638UL, 2097276163UL, 2273246349UL, 835377715UL, 31
			27280755UL, 1060067446UL, 4233399907UL, 639470242UL, 2856296318UL, 26157750
			11UL, 2351047932UL, 28260330UL, 202834023UL, 1876303820UL, 619308202UL, 309
			4305336UL, 2455670466UL, 4188621628UL, 928095314UL, 3764894047UL, 378538558
			3UL, 117581687UL, 4021419931UL, 2483628986UL, 3759839215UL, 1867816730UL, 4
			254101087UL, 3872575068UL, 1577956550UL, 1687107439UL, 3081878598UL, 400327
			9048UL, 4048067248UL, 2500767965UL, 2804044146UL, 928465955UL, 922020515UL,
			1875618971UL, 3865851141UL, 2359176389UL, 173810260UL, 625773097UL, 338468
			4457UL, 2540504961UL, 3665420733UL, 2645143254UL, 2592805114UL, 1392199429U
			L, 3370444585UL, 1604709429UL, 162781360UL, 3190958614UL, 2534538765UL, 365
			841057UL, 3843585067UL, 3105377832UL, 3778669305UL, 1247069485UL, 152395829
			3UL, 293374051UL, 800971948UL, 720895889UL, 2071968844UL, 324025193UL, 3342
			361801UL, 52577992UL, 3535536111UL, 3632553233UL, 352757977UL, 1386594581UL
			, 3305908059UL, 2649344603UL, 1501841977UL, 1751860798UL, 3160423601UL, 264
			880505UL, 778965559UL, 3232142321UL, 3087428595UL, 3814775120UL, 1127761012
			UL, 3880222002UL, 4163350364UL, 1721646249UL, 3640773034UL, 2368512339UL, 7
			02936770UL, 1705444176UL, 713642505UL, 1717761787UL, 197200752UL, 147785869
			4UL, 37117223UL, 1948295454UL, 896215772UL, 1588372042UL, 464173532UL, 5278
			99063UL, 4285770666UL, 3441409029UL, 2124039914UL, 2872629966UL, 171012646U
			L, 3515750082UL, 2216687886UL, 818571456UL, 1631555059UL, 2662104279UL, 203
			3464928UL, 369438400UL, 329003658UL, 1503365029UL, 4215790910UL, 3264377550
			UL, 733526983UL, 2935318632UL, 1792331479UL, 608347530UL, 392723097UL, 1330
			445854UL, 3473004271UL, 1267636682UL, 2150566972UL, 2664910943UL, 259186163
			7UL, 409769584UL, 2943326880UL, 3746302819UL, 3162268832UL, 1028663260UL, 3
			206607045UL, 832105292UL, 2119405275UL, 538318455UL, 2981192295UL, 86177541
			6UL, 609718403UL, 3531204230UL, 1904759571UL, 1262633751UL, 2375133081UL, 4
			60454984UL, 946700253UL, 3763898311UL, 1571175213UL, 3124410107UL, 24134202
			16UL, 2664177543UL, 3241803820UL, 3968067371UL, 1234860999UL, 1130471500UL,
			772727786UL, 247203117UL, 576455235UL, 246297007UL, 2027348597UL, 76493388
			7UL, 3812479771UL, 1825807084UL, 4072281412UL, 2156865781UL, 1286484847UL,
			1966749063UL, 2479269303UL, 423506843UL, 3070938758UL, 653091413UL, 2267423
			132UL, 2004263526UL, 1374490719UL, 3871990628UL, 841138314UL, 1260317857UL,
			3887432433UL, 4025147569UL, 764233331UL, 1794763428UL, 3005903468UL, 87792
			6770UL, 2466593927UL, 2971729561UL, 3203070565UL, 4198500026UL, 815665759UL
			, 2434508139UL, 1840456368UL, 2279000427UL, 17077200UL, 3178380570UL, 99030
			4199UL, 3578008580UL, 1965763660UL, 1640352477UL, 750159594UL, 2047409402UL
			, 3576308245UL, 544920564UL, 1730124869UL, 1194761386UL, 3280315505UL, 1473
			34027UL, 2870674244UL, 2076860776UL, 1100947675UL, 2482772161UL, 401966468U
			L, 1610650855UL, 193868446UL, 3808157106UL, 1509130117UL, 1324484736UL, 385
			2893217UL, 1059179497UL, 4053543778UL, 2557844172UL, 3282312002UL, 68255005
			8UL, 4281899173UL, 137171998UL, 3239159214UL, 2258610918UL, 426724741UL, 35
			02660993UL, 135977383UL, 429929363UL, 3984458137UL, 964026748UL, 2182019070
			UL, 3836562946UL, 515026869UL, 359030455UL, 1301694917UL, 2300414803UL, 236
			4654981UL, 3804876710UL, 171119249UL, 2646785698UL, 4283509387UL, 362808776
			3UL, 1748227044UL, 3037141234UL, 3000413256UL, 23007314UL, 3598880509UL, 41
			60517314UL, 112205578UL, 1677675411UL, 734881643UL, 2830770338UL, 347031714
			5UL, 3306806569UL, 2635040943UL, 2671367560UL, 3528996498UL, 3878886478UL,
			3114253828UL, 2721384408UL, 3175226991UL, 1393767271UL, 2651623266UL, 37679
			78376UL, 1269699398UL, 1100964192UL, 4169085845UL, 2086718107UL, 1286251099
			UL, 764751784UL, 3006878591UL, },
			};
			static unsigned int precalc_xorwow_offset_matrix_host[8][800] = {
			{0UL, 0UL, 0UL, 0UL, 3UL, 0UL, 0UL, 0UL, 0UL, 6UL, 0UL, 0UL, 0UL, 0UL, 15UL
			, 0UL, 0UL, 0UL, 0UL, 30UL, 0UL, 0UL, 0UL, 0UL, 60UL, 0UL, 0UL, 0UL, 0UL, 1
			20UL, 0UL, 0UL, 0UL, 0UL, 240UL, 0UL, 0UL, 0UL, 0UL, 480UL, 0UL, 0UL, 0UL,
			0UL, 960UL, 0UL, 0UL, 0UL, 0UL, 1920UL, 0UL, 0UL, 0UL, 0UL, 3840UL, 0UL, 0U
			L, 0UL, 0UL, 7680UL, 0UL, 0UL, 0UL, 0UL, 15360UL, 0UL, 0UL, 0UL, 0UL, 30720
			UL, 0UL, 0UL, 0UL, 0UL, 61440UL, 0UL, 0UL, 0UL, 0UL, 122880UL, 0UL, 0UL, 0U
			L, 0UL, 245760UL, 0UL, 0UL, 0UL, 0UL, 491520UL, 0UL, 0UL, 0UL, 0UL, 983040U
			L, 0UL, 0UL, 0UL, 0UL, 1966080UL, 0UL, 0UL, 0UL, 0UL, 3932160UL, 0UL, 0UL,
			0UL, 0UL, 7864320UL, 0UL, 0UL, 0UL, 0UL, 15728640UL, 0UL, 0UL, 0UL, 0UL, 31
			457280UL, 0UL, 0UL, 0UL, 0UL, 62914560UL, 0UL, 0UL, 0UL, 0UL, 125829120UL,
			0UL, 0UL, 0UL, 0UL, 251658240UL, 0UL, 0UL, 0UL, 0UL, 503316480UL, 0UL, 0UL,
			0UL, 0UL, 1006632960UL, 0UL, 0UL, 0UL, 0UL, 2013265920UL, 0UL, 0UL, 0UL, 0
			UL, 4026531840UL, 0UL, 0UL, 0UL, 0UL, 3758096384UL, 1UL, 0UL, 0UL, 0UL, 0UL
			, 2UL, 0UL, 0UL, 0UL, 0UL, 4UL, 0UL, 0UL, 0UL, 0UL, 8UL, 0UL, 0UL, 0UL, 0UL
			, 16UL, 0UL, 0UL, 0UL, 0UL, 32UL, 0UL, 0UL, 0UL, 0UL, 64UL, 0UL, 0UL, 0UL,
			0UL, 128UL, 0UL, 0UL, 0UL, 0UL, 256UL, 0UL, 0UL, 0UL, 0UL, 512UL, 0UL, 0UL,
			0UL, 0UL, 1024UL, 0UL, 0UL, 0UL, 0UL, 2048UL, 0UL, 0UL, 0UL, 0UL, 4096UL,
			0UL, 0UL, 0UL, 0UL, 8192UL, 0UL, 0UL, 0UL, 0UL, 16384UL, 0UL, 0UL, 0UL, 0UL
			, 32768UL, 0UL, 0UL, 0UL, 0UL, 65536UL, 0UL, 0UL, 0UL, 0UL, 131072UL, 0UL,
			0UL, 0UL, 0UL, 262144UL, 0UL, 0UL, 0UL, 0UL, 524288UL, 0UL, 0UL, 0UL, 0UL,
			1048576UL, 0UL, 0UL, 0UL, 0UL, 2097152UL, 0UL, 0UL, 0UL, 0UL, 4194304UL, 0U
			L, 0UL, 0UL, 0UL, 8388608UL, 0UL, 0UL, 0UL, 0UL, 16777216UL, 0UL, 0UL, 0UL,
			0UL, 33554432UL, 0UL, 0UL, 0UL, 0UL, 67108864UL, 0UL, 0UL, 0UL, 0UL, 13421
			7728UL, 0UL, 0UL, 0UL, 0UL, 268435456UL, 0UL, 0UL, 0UL, 0UL, 536870912UL, 0
			UL, 0UL, 0UL, 0UL, 1073741824UL, 0UL, 0UL, 0UL, 0UL, 2147483648UL, 0UL, 0UL
			, 0UL, 0UL, 0UL, 1UL, 0UL, 0UL, 0UL, 0UL, 2UL, 0UL, 0UL, 0UL, 0UL, 4UL, 0UL
			, 0UL, 0UL, 0UL, 8UL, 0UL, 0UL, 0UL, 0UL, 16UL, 0UL, 0UL, 0UL, 0UL, 32UL, 0
			UL, 0UL, 0UL, 0UL, 64UL, 0UL, 0UL, 0UL, 0UL, 128UL, 0UL, 0UL, 0UL, 0UL, 256
			UL, 0UL, 0UL, 0UL, 0UL, 512UL, 0UL, 0UL, 0UL, 0UL, 1024UL, 0UL, 0UL, 0UL, 0
			UL, 2048UL, 0UL, 0UL, 0UL, 0UL, 4096UL, 0UL, 0UL, 0UL, 0UL, 8192UL, 0UL, 0U
			L, 0UL, 0UL, 16384UL, 0UL, 0UL, 0UL, 0UL, 32768UL, 0UL, 0UL, 0UL, 0UL, 6553
			6UL, 0UL, 0UL, 0UL, 0UL, 131072UL, 0UL, 0UL, 0UL, 0UL, 262144UL, 0UL, 0UL,
			0UL, 0UL, 524288UL, 0UL, 0UL, 0UL, 0UL, 1048576UL, 0UL, 0UL, 0UL, 0UL, 2097
			152UL, 0UL, 0UL, 0UL, 0UL, 4194304UL, 0UL, 0UL, 0UL, 0UL, 8388608UL, 0UL, 0
			UL, 0UL, 0UL, 16777216UL, 0UL, 0UL, 0UL, 0UL, 33554432UL, 0UL, 0UL, 0UL, 0U
			L, 67108864UL, 0UL, 0UL, 0UL, 0UL, 134217728UL, 0UL, 0UL, 0UL, 0UL, 2684354
			56UL, 0UL, 0UL, 0UL, 0UL, 536870912UL, 0UL, 0UL, 0UL, 0UL, 1073741824UL, 0U
			L, 0UL, 0UL, 0UL, 2147483648UL, 0UL, 0UL, 0UL, 0UL, 0UL, 1UL, 0UL, 0UL, 0UL
			, 0UL, 2UL, 0UL, 0UL, 0UL, 0UL, 4UL, 0UL, 0UL, 0UL, 0UL, 8UL, 0UL, 0UL, 0UL
			, 0UL, 16UL, 0UL, 0UL, 0UL, 0UL, 32UL, 0UL, 0UL, 0UL, 0UL, 64UL, 0UL, 0UL,
			0UL, 0UL, 128UL, 0UL, 0UL, 0UL, 0UL, 256UL, 0UL, 0UL, 0UL, 0UL, 512UL, 0UL,
			0UL, 0UL, 0UL, 1024UL, 0UL, 0UL, 0UL, 0UL, 2048UL, 0UL, 0UL, 0UL, 0UL, 409
			6UL, 0UL, 0UL, 0UL, 0UL, 8192UL, 0UL, 0UL, 0UL, 0UL, 16384UL, 0UL, 0UL, 0UL
			, 0UL, 32768UL, 0UL, 0UL, 0UL, 0UL, 65536UL, 0UL, 0UL, 0UL, 0UL, 131072UL,
			0UL, 0UL, 0UL, 0UL, 262144UL, 0UL, 0UL, 0UL, 0UL, 524288UL, 0UL, 0UL, 0UL,
			0UL, 1048576UL, 0UL, 0UL, 0UL, 0UL, 2097152UL, 0UL, 0UL, 0UL, 0UL, 4194304U
			L, 0UL, 0UL, 0UL, 0UL, 8388608UL, 0UL, 0UL, 0UL, 0UL, 16777216UL, 0UL, 0UL,
			0UL, 0UL, 33554432UL, 0UL, 0UL, 0UL, 0UL, 67108864UL, 0UL, 0UL, 0UL, 0UL,
			134217728UL, 0UL, 0UL, 0UL, 0UL, 268435456UL, 0UL, 0UL, 0UL, 0UL, 536870912
			UL, 0UL, 0UL, 0UL, 0UL, 1073741824UL, 0UL, 0UL, 0UL, 0UL, 2147483648UL, 0UL
			, 0UL, 0UL, 0UL, 0UL, 1UL, 17UL, 0UL, 0UL, 0UL, 2UL, 34UL, 0UL, 0UL, 0UL, 4
			UL, 68UL, 0UL, 0UL, 0UL, 8UL, 136UL, 0UL, 0UL, 0UL, 16UL, 272UL, 0UL, 0UL,
			0UL, 32UL, 544UL, 0UL, 0UL, 0UL, 64UL, 1088UL, 0UL, 0UL, 0UL, 128UL, 2176UL
			, 0UL, 0UL, 0UL, 256UL, 4352UL, 0UL, 0UL, 0UL, 512UL, 8704UL, 0UL, 0UL, 0UL
			, 1024UL, 17408UL, 0UL, 0UL, 0UL, 2048UL, 34816UL, 0UL, 0UL, 0UL, 4096UL, 6
			9632UL, 0UL, 0UL, 0UL, 8192UL, 139264UL, 0UL, 0UL, 0UL, 16384UL, 278528UL,
			0UL, 0UL, 0UL, 32768UL, 557056UL, 0UL, 0UL, 0UL, 65536UL, 1114112UL, 0UL, 0
			UL, 0UL, 131072UL, 2228224UL, 0UL, 0UL, 0UL, 262144UL, 4456448UL, 0UL, 0UL,
			0UL, 524288UL, 8912896UL, 0UL, 0UL, 0UL, 1048576UL, 17825792UL, 0UL, 0UL,
			0UL, 2097152UL, 35651584UL, 0UL, 0UL, 0UL, 4194304UL, 71303168UL, 0UL, 0UL,
			0UL, 8388608UL, 142606336UL, 0UL, 0UL, 0UL, 16777216UL, 285212672UL, 0UL,
			0UL, 0UL, 33554432UL, 570425344UL, 0UL, 0UL, 0UL, 67108864UL, 1140850688UL,
			0UL, 0UL, 0UL, 134217728UL, 2281701376UL, 0UL, 0UL, 0UL, 268435456UL, 2684
			35456UL, 0UL, 0UL, 0UL, 536870912UL, 536870912UL, 0UL, 0UL, 0UL, 1073741824
			UL, 1073741824UL, 0UL, 0UL, 0UL, 2147483648UL, 2147483648UL, }, {0UL, 3UL,
			51UL, 771UL, 13107UL, 0UL, 6UL, 102UL, 1542UL, 26214UL, 0UL, 15UL, 255UL, 3
			855UL, 65535UL, 0UL, 30UL, 510UL, 7710UL, 131070UL, 0UL, 60UL, 1020UL, 1542
			0UL, 262140UL, 0UL, 120UL, 2040UL, 30840UL, 524280UL, 0UL, 240UL, 4080UL, 6
			1680UL, 1048560UL, 0UL, 480UL, 8160UL, 123360UL, 2097120UL, 0UL, 960UL, 163
			20UL, 246720UL, 4194240UL, 0UL, 1920UL, 32640UL, 493440UL, 8388480UL, 0UL,
			3840UL, 65280UL, 986880UL, 16776960UL, 0UL, 7680UL, 130560UL, 1973760UL, 33
			553920UL, 0UL, 15360UL, 261120UL, 3947520UL, 67107840UL, 0UL, 30720UL, 5222
			40UL, 7895040UL, 134215680UL, 0UL, 61440UL, 1044480UL, 15790080UL, 26843136
			0UL, 0UL, 122880UL, 2088960UL, 31580160UL, 536862720UL, 0UL, 245760UL, 4177
			920UL, 63160320UL, 1073725440UL, 0UL, 491520UL, 8355840UL, 126320640UL, 214
			7450880UL, 0UL, 983040UL, 16711680UL, 252641280UL, 4294901760UL, 0UL, 19660
			80UL, 33423360UL, 505282560UL, 4294836224UL, 0UL, 3932160UL, 66846720UL, 10
			10565120UL, 4294705152UL, 0UL, 7864320UL, 133693440UL, 2021130240UL, 429444
			3008UL, 0UL, 15728640UL, 267386880UL, 4042260480UL, 4293918720UL, 0UL, 3145
			7280UL, 534773760UL, 3789553664UL, 4292870144UL, 0UL, 62914560UL, 106954752
			0UL, 3284140032UL, 4290772992UL, 0UL, 125829120UL, 2139095040UL, 2273312768
			UL, 4286578688UL, 0UL, 251658240UL, 4278190080UL, 251658240UL, 4278190080UL
			, 0UL, 503316480UL, 4261412864UL, 503316480UL, 4261412864UL, 0UL, 100663296
			0UL, 4227858432UL, 1006632960UL, 4227858432UL, 0UL, 2013265920UL, 416074956
			8UL, 2013265920UL, 4160749568UL, 0UL, 4026531840UL, 4026531840UL, 402653184
			0UL, 4026531840UL, 0UL, 3758096384UL, 3758096384UL, 3758096384UL, 375809638
			4UL, 0UL, 0UL, 3UL, 51UL, 771UL, 0UL, 0UL, 6UL, 102UL, 1542UL, 0UL, 0UL, 15
			UL, 255UL, 3855UL, 0UL, 0UL, 30UL, 510UL, 7710UL, 0UL, 0UL, 60UL, 1020UL, 1
			5420UL, 0UL, 0UL, 120UL, 2040UL, 30840UL, 0UL, 0UL, 240UL, 4080UL, 61680UL,
			0UL, 0UL, 480UL, 8160UL, 123360UL, 0UL, 0UL, 960UL, 16320UL, 246720UL, 0UL
			, 0UL, 1920UL, 32640UL, 493440UL, 0UL, 0UL, 3840UL, 65280UL, 986880UL, 0UL,
			0UL, 7680UL, 130560UL, 1973760UL, 0UL, 0UL, 15360UL, 261120UL, 3947520UL,
			0UL, 0UL, 30720UL, 522240UL, 7895040UL, 0UL, 0UL, 61440UL, 1044480UL, 15790
			080UL, 0UL, 0UL, 122880UL, 2088960UL, 31580160UL, 0UL, 0UL, 245760UL, 41779
			20UL, 63160320UL, 0UL, 0UL, 491520UL, 8355840UL, 126320640UL, 0UL, 0UL, 983
			040UL, 16711680UL, 252641280UL, 0UL, 0UL, 1966080UL, 33423360UL, 505282560U
			L, 0UL, 0UL, 3932160UL, 66846720UL, 1010565120UL, 0UL, 0UL, 7864320UL, 1336
			93440UL, 2021130240UL, 0UL, 0UL, 15728640UL, 267386880UL, 4042260480UL, 0UL
			, 0UL, 31457280UL, 534773760UL, 3789553664UL, 0UL, 0UL, 62914560UL, 1069547
			520UL, 3284140032UL, 0UL, 0UL, 125829120UL, 2139095040UL, 2273312768UL, 0UL
			, 0UL, 251658240UL, 4278190080UL, 251658240UL, 0UL, 0UL, 503316480UL, 42614
			12864UL, 503316480UL, 0UL, 0UL, 1006632960UL, 4227858432UL, 1006632960UL, 0
			UL, 0UL, 2013265920UL, 4160749568UL, 2013265920UL, 0UL, 0UL, 4026531840UL,
			4026531840UL, 4026531840UL, 0UL, 0UL, 3758096384UL, 3758096384UL, 375809638
			4UL, 0UL, 0UL, 0UL, 3UL, 51UL, 0UL, 0UL, 0UL, 6UL, 102UL, 0UL, 0UL, 0UL, 15
			UL, 255UL, 0UL, 0UL, 0UL, 30UL, 510UL, 0UL, 0UL, 0UL, 60UL, 1020UL, 0UL, 0U
			L, 0UL, 120UL, 2040UL, 0UL, 0UL, 0UL, 240UL, 4080UL, 0UL, 0UL, 0UL, 480UL,
			8160UL, 0UL, 0UL, 0UL, 960UL, 16320UL, 0UL, 0UL, 0UL, 1920UL, 32640UL, 0UL,
			0UL, 0UL, 3840UL, 65280UL, 0UL, 0UL, 0UL, 7680UL, 130560UL, 0UL, 0UL, 0UL,
			15360UL, 261120UL, 0UL, 0UL, 0UL, 30720UL, 522240UL, 0UL, 0UL, 0UL, 61440U
			L, 1044480UL, 0UL, 0UL, 0UL, 122880UL, 2088960UL, 0UL, 0UL, 0UL, 245760UL,
			4177920UL, 0UL, 0UL, 0UL, 491520UL, 8355840UL, 0UL, 0UL, 0UL, 983040UL, 167
			11680UL, 0UL, 0UL, 0UL, 1966080UL, 33423360UL, 0UL, 0UL, 0UL, 3932160UL, 66
			846720UL, 0UL, 0UL, 0UL, 7864320UL, 133693440UL, 0UL, 0UL, 0UL, 15728640UL,
			267386880UL, 0UL, 0UL, 0UL, 31457280UL, 534773760UL, 0UL, 0UL, 0UL, 629145
			60UL, 1069547520UL, 0UL, 0UL, 0UL, 125829120UL, 2139095040UL, 0UL, 0UL, 0UL
			, 251658240UL, 4278190080UL, 0UL, 0UL, 0UL, 503316480UL, 4261412864UL, 0UL,
			0UL, 0UL, 1006632960UL, 4227858432UL, 0UL, 0UL, 0UL, 2013265920UL, 4160749
			568UL, 0UL, 0UL, 0UL, 4026531840UL, 4026531840UL, 0UL, 0UL, 0UL, 3758096384
			UL, 3758096384UL, 0UL, 0UL, 0UL, 0UL, 3UL, 0UL, 0UL, 0UL, 0UL, 6UL, 0UL, 0U
			L, 0UL, 0UL, 15UL, 0UL, 0UL, 0UL, 0UL, 30UL, 0UL, 0UL, 0UL, 0UL, 60UL, 0UL,
			0UL, 0UL, 0UL, 120UL, 0UL, 0UL, 0UL, 0UL, 240UL, 0UL, 0UL, 0UL, 0UL, 480UL
			, 0UL, 0UL, 0UL, 0UL, 960UL, 0UL, 0UL, 0UL, 0UL, 1920UL, 0UL, 0UL, 0UL, 0UL
			, 3840UL, 0UL, 0UL, 0UL, 0UL, 7680UL, 0UL, 0UL, 0UL, 0UL, 15360UL, 0UL, 0UL
			, 0UL, 0UL, 30720UL, 0UL, 0UL, 0UL, 0UL, 61440UL, 0UL, 0UL, 0UL, 0UL, 12288
			0UL, 0UL, 0UL, 0UL, 0UL, 245760UL, 0UL, 0UL, 0UL, 0UL, 491520UL, 0UL, 0UL,
			0UL, 0UL, 983040UL, 0UL, 0UL, 0UL, 0UL, 1966080UL, 0UL, 0UL, 0UL, 0UL, 3932
			160UL, 0UL, 0UL, 0UL, 0UL, 7864320UL, 0UL, 0UL, 0UL, 0UL, 15728640UL, 0UL,
			0UL, 0UL, 0UL, 31457280UL, 0UL, 0UL, 0UL, 0UL, 62914560UL, 0UL, 0UL, 0UL, 0
			UL, 125829120UL, 0UL, 0UL, 0UL, 0UL, 251658240UL, 0UL, 0UL, 0UL, 0UL, 50331
			6480UL, 0UL, 0UL, 0UL, 0UL, 1006632960UL, 0UL, 0UL, 0UL, 0UL, 2013265920UL,
			0UL, 0UL, 0UL, 0UL, 4026531840UL, 0UL, 0UL, 0UL, 0UL, 3758096384UL, 1UL, 1
			7UL, 257UL, 4369UL, 65537UL, 2UL, 34UL, 514UL, 8738UL, 131074UL, 4UL, 68UL,
			1028UL, 17476UL, 262148UL, 8UL, 136UL, 2056UL, 34952UL, 524296UL, 16UL, 27
			2UL, 4112UL, 69904UL, 1048592UL, 32UL, 544UL, 8224UL, 139808UL, 2097184UL,
			64UL, 1088UL, 16448UL, 279616UL, 4194368UL, 128UL, 2176UL, 32896UL, 559232U
			L, 8388736UL, 256UL, 4352UL, 65792UL, 1118464UL, 16777472UL, 512UL, 8704UL,
			131584UL, 2236928UL, 33554944UL, 1024UL, 17408UL, 263168UL, 4473856UL, 671
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			};
	#endif // CURAND_XORWOW_PRECALCULATED_H_		#endif // CURAND_XORWOW_PRECALCULATED_H_
End of changes. 1 change blocks.
	0 lines changed or deleted		2062 lines changed or added

	cusparse.h		cusparse.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
			* subject to NVIDIA intellectual property rights under U.S. and
	* international Copyright laws.		* international Copyright laws.
	*		*
	* This software and the information contained herein is being provided		* These Licensed Deliverables contained herein is PROPRIETARY and
	* under the terms and conditions of a Source Code License Agreement.		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
			* conditions of a form of NVIDIA software license agreement by and
			* between NVIDIA and Licensee ("License Agreement") or electronically
			* accepted by Licensee. Notwithstanding any terms or conditions to
			* the contrary in the License Agreement, reproduction or disclosure
			* of the Licensed Deliverables to any third party without the express
			* written consent of NVIDIA is prohibited.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	,		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE		* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE		* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
	* OR PERFORMANCE OF THIS SOURCE CODE.		* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* U.S. Government End Users. These Licensed Deliverables are a
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* "commercial computer software" and "commercial computer software		* 1995), consisting of "commercial computer software" and "commercial
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* computer software documentation" as such terms are used in 48
	* and is provided to the U.S. Government only as a commercial end item.		* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
	* source code with only those rights set forth herein.		* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(CUSPARSE_H_)		#if !defined(CUSPARSE_H_)
	#define CUSPARSE_H_		#define CUSPARSE_H_
	#ifndef CUSPARSEAPI		#ifndef CUSPARSEAPI
	#ifdef _WIN32		#ifdef _WIN32
	#define CUSPARSEAPI __stdcall		#define CUSPARSEAPI __stdcall
	#else		#else
	#define CUSPARSEAPI		#define CUSPARSEAPI
	skipping to change at line 64		skipping to change at line 81
	CUSPARSE_STATUS_NOT_INITIALIZED=1,		CUSPARSE_STATUS_NOT_INITIALIZED=1,
	CUSPARSE_STATUS_ALLOC_FAILED=2,		CUSPARSE_STATUS_ALLOC_FAILED=2,
	CUSPARSE_STATUS_INVALID_VALUE=3,		CUSPARSE_STATUS_INVALID_VALUE=3,
	CUSPARSE_STATUS_ARCH_MISMATCH=4,		CUSPARSE_STATUS_ARCH_MISMATCH=4,
	CUSPARSE_STATUS_MAPPING_ERROR=5,		CUSPARSE_STATUS_MAPPING_ERROR=5,
	CUSPARSE_STATUS_EXECUTION_FAILED=6,		CUSPARSE_STATUS_EXECUTION_FAILED=6,
	CUSPARSE_STATUS_INTERNAL_ERROR=7,		CUSPARSE_STATUS_INTERNAL_ERROR=7,
	CUSPARSE_STATUS_MATRIX_TYPE_NOT_SUPPORTED=8		CUSPARSE_STATUS_MATRIX_TYPE_NOT_SUPPORTED=8
	} cusparseStatus_t;		} cusparseStatus_t;
	/* Opaque structure */		/* Opaque structure holding CUSPARSE library context */
	struct cusparseContext;		struct cusparseContext;
	typedef struct cusparseContext *cusparseHandle_t;		typedef struct cusparseContext *cusparseHandle_t;
	cusparseStatus_t CUSPARSEAPI cusparseCreate (cusparseHandle_t *handle);		cusparseStatus_t CUSPARSEAPI cusparseCreate (cusparseHandle_t *handle);
	cusparseStatus_t CUSPARSEAPI cusparseDestroy (cusparseHandle_t handle);		cusparseStatus_t CUSPARSEAPI cusparseDestroy (cusparseHandle_t handle);
	cusparseStatus_t CUSPARSEAPI cusparseGetVersion(cusparseHandle_t handle, in t *version);		cusparseStatus_t CUSPARSEAPI cusparseGetVersion(cusparseHandle_t handle, in t *version);
	cusparseStatus_t CUSPARSEAPI cusparseSetKernelStream (cusparseHandle_t hand le, cudaStream_t streamId);		cusparseStatus_t CUSPARSEAPI cusparseSetKernelStream (cusparseHandle_t hand le, cudaStream_t streamId);
	/* In general (for details please refer to documentation of every function) :		/* In general (for details please refer to documentation of every function) :
	m,n,k represent the dimensions of the input matrix and vectors,		m,n,k represent the dimensions of the input matrix and vectors,
	skipping to change at line 89		skipping to change at line 106
	/* Types definitions */		/* Types definitions */
	//General matrix descriptor similar to other sparse APIs, where we currentl y		//General matrix descriptor similar to other sparse APIs, where we currentl y
	//support only a subset of the allowed values.		//support only a subset of the allowed values.
	typedef enum {		typedef enum {
	CUSPARSE_MATRIX_TYPE_GENERAL=0,		CUSPARSE_MATRIX_TYPE_GENERAL=0,
	CUSPARSE_MATRIX_TYPE_SYMMETRIC=1,		CUSPARSE_MATRIX_TYPE_SYMMETRIC=1,
	CUSPARSE_MATRIX_TYPE_HERMITIAN=2,		CUSPARSE_MATRIX_TYPE_HERMITIAN=2,
	CUSPARSE_MATRIX_TYPE_TRIANGULAR=3,		CUSPARSE_MATRIX_TYPE_TRIANGULAR=3
	} cusparseMatrixType_t;		} cusparseMatrixType_t;
	typedef enum {		typedef enum {
	CUSPARSE_FILL_MODE_LOWER=0,		CUSPARSE_FILL_MODE_LOWER=0,
	CUSPARSE_FILL_MODE_UPPER=1		CUSPARSE_FILL_MODE_UPPER=1
	} cusparseFillMode_t;		} cusparseFillMode_t;
	typedef enum {		typedef enum {
	CUSPARSE_DIAG_TYPE_NON_UNIT=0,		CUSPARSE_DIAG_TYPE_NON_UNIT=0,
	CUSPARSE_DIAG_TYPE_UNIT=1		CUSPARSE_DIAG_TYPE_UNIT=1
	skipping to change at line 140		skipping to change at line 157
	cusparseStatus_t CUSPARSEAPI cusparseSetMatFillMode(cusparseMatDescr_t desc rA, cusparseFillMode_t fillMode);		cusparseStatus_t CUSPARSEAPI cusparseSetMatFillMode(cusparseMatDescr_t desc rA, cusparseFillMode_t fillMode);
	cusparseFillMode_t CUSPARSEAPI cusparseGetMatFillMode(const cusparseMatDesc r_t descrA);		cusparseFillMode_t CUSPARSEAPI cusparseGetMatFillMode(const cusparseMatDesc r_t descrA);
	// type must be _TRIANGULAR		// type must be _TRIANGULAR
	cusparseStatus_t CUSPARSEAPI cusparseSetMatDiagType(cusparseMatDescr_t des crA, cusparseDiagType_t diagType);		cusparseStatus_t CUSPARSEAPI cusparseSetMatDiagType(cusparseMatDescr_t des crA, cusparseDiagType_t diagType);
	cusparseDiagType_t CUSPARSEAPI cusparseGetMatDiagType(const cusparseMatDesc r_t descrA);		cusparseDiagType_t CUSPARSEAPI cusparseGetMatDiagType(const cusparseMatDesc r_t descrA);
	cusparseStatus_t CUSPARSEAPI cusparseSetMatIndexBase(cusparseMatDescr_t des crA, cusparseIndexBase_t base);		cusparseStatus_t CUSPARSEAPI cusparseSetMatIndexBase(cusparseMatDescr_t des crA, cusparseIndexBase_t base);
	cusparseIndexBase_t CUSPARSEAPI cusparseGetMatIndexBase(const cusparseMatDe scr_t descrA);		cusparseIndexBase_t CUSPARSEAPI cusparseGetMatIndexBase(const cusparseMatDe scr_t descrA);
			/* Opaque structure holding csrsv analysis information */
			struct cusparseSolveAnalysisInfo;
			typedef struct cusparseSolveAnalysisInfo *cusparseSolveAnalysisInfo_t;
			cusparseStatus_t CUSPARSEAPI cusparseCreateSolveAnalysisInfo(cusparseSolveA
			nalysisInfo_t *info);
			cusparseStatus_t CUSPARSEAPI cusparseDestroySolveAnalysisInfo(cusparseSolve
			AnalysisInfo_t info);
	/* Sparse Level 1 functions */		/* Sparse Level 1 functions */
	//Adds a scalar multiple of compressed sparse vector to a		//Adds a scalar multiple of compressed sparse vector to a
	//full-storage vector		//full-storage vector
	cusparseStatus_t CUSPARSEAPI cusparseSaxpyi(cusparseHandle_t handle,		cusparseStatus_t CUSPARSEAPI cusparseSaxpyi(cusparseHandle_t handle,
	int nnz,		int nnz,
	float alpha,		float alpha,
	const float *xVal,		const float *xVal,
	const int *xInd,		const int *xInd,
	float *y,		float *y,
	skipping to change at line 394		skipping to change at line 419
	int n,		int n,
	cuDoubleComplex alpha,		cuDoubleComplex alpha,
	const cusparseMatDescr_t descrA ,		const cusparseMatDescr_t descrA ,
	const cuDoubleComplex *csrValA,		const cuDoubleComplex *csrValA,
	const int *csrRowPtrA,		const int *csrRowPtrA,
	const int *csrColIndA,		const int *csrColIndA,
	const cuDoubleComplex *x,		const cuDoubleComplex *x,
	cuDoubleComplex beta,		cuDoubleComplex beta,
	cuDoubleComplex *y);		cuDoubleComplex *y);
			//Solution of triangular linear system op(A) * y = alpha * x,
			//where A is a sparse matrix, x and y are dense vectors
			cusparseStatus_t CUSPARSEAPI cusparseScsrsv_analysis(cusparseHandle_t handl
			e,
			cusparseOperation_t tr
			ansA,
			int m,
			const cusparseMatDescr
			_t descrA,
			const float *csrValA,
			const int *csrRowPtrA,
			const int *csrColIndA,
			cusparseSolveAnalysisI
			nfo_t info);
			cusparseStatus_t CUSPARSEAPI cusparseDcsrsv_analysis(cusparseHandle_t handl
			e,
			cusparseOperation_t tr
			ansA,
			int m,
			const cusparseMatDescr
			_t descrA,
			const double *csrValA,
			const int *csrRowPtrA,
			const int *csrColIndA,
			cusparseSolveAnalysisI
			nfo_t info);
			cusparseStatus_t CUSPARSEAPI cusparseCcsrsv_analysis(cusparseHandle_t handl
			e,
			cusparseOperation_t tr
			ansA,
			int m,
			const cusparseMatDescr
			_t descrA,
			const cuComplex *csrVa
			lA,
			const int *csrRowPtrA,
			const int *csrColIndA,
			cusparseSolveAnalysisI
			nfo_t info);
			cusparseStatus_t CUSPARSEAPI cusparseZcsrsv_analysis(cusparseHandle_t handl
			e,
			cusparseOperation_t tr
			ansA,
			int m,
			const cusparseMatDescr
			_t descrA,
			const cuDoubleComplex
			*csrValA,
			const int *csrRowPtrA,
			const int *csrColIndA,
			cusparseSolveAnalysisI
			nfo_t info);
			cusparseStatus_t CUSPARSEAPI cusparseScsrsv_solve(cusparseHandle_t handle,
			cusparseOperation_t trans
			A,
			int m,
			float alpha,
			const cusparseMatDescr_t
			descrA,
			const float *csrValA,
			const int *csrRowPtrA,
			const int *csrColIndA,
			cusparseSolveAnalysisInfo
			_t info,
			const float *x,
			float *y);
			cusparseStatus_t CUSPARSEAPI cusparseDcsrsv_solve(cusparseHandle_t handle,
			cusparseOperation_t trans
			A,
			int m,
			double alpha,
			const cusparseMatDescr_t
			descrA,
			const double *csrValA,
			const int *csrRowPtrA,
			const int *csrColIndA,
			cusparseSolveAnalysisInfo
			_t info,
			const double *x,
			double *y);
			cusparseStatus_t CUSPARSEAPI cusparseCcsrsv_solve(cusparseHandle_t handle,
			cusparseOperation_t trans
			A,
			int m,
			cuComplex alpha,
			const cusparseMatDescr_t
			descrA,
			const cuComplex *csrValA,
			const int *csrRowPtrA,
			const int *csrColIndA,
			cusparseSolveAnalysisInfo
			_t info,
			const cuComplex *x,
			cuComplex *y);
			cusparseStatus_t CUSPARSEAPI cusparseZcsrsv_solve(cusparseHandle_t handle,
			cusparseOperation_t trans
			A,
			int m,
			cuDoubleComplex alpha,
			const cusparseMatDescr_t
			descrA,
			const cuDoubleComplex *cs
			rValA,
			const int *csrRowPtrA,
			const int *csrColIndA,
			cusparseSolveAnalysisInfo
			_t info,
			const cuDoubleComplex *x,
			cuDoubleComplex *y);
	/* Sparse Level 3 functions */		/* Sparse Level 3 functions */
	//Matrix-matrix multiplication C = alpha * op(A) * B + beta * C,		//Matrix-matrix multiplication C = alpha * op(A) * B + beta * C,
	//where A is a sparse matrix, B and C are dense matrices		//where A is a sparse matrix, B and C are dense matrices
	cusparseStatus_t CUSPARSEAPI cusparseScsrmm(cusparseHandle_t handle,		cusparseStatus_t CUSPARSEAPI cusparseScsrmm(cusparseHandle_t handle,
	cusparseOperation_t transA,		cusparseOperation_t transA,
	int m,		int m,
	int n,		int n,
	int k,		int k,
	float alpha,		float alpha,
End of changes. 10 change blocks.
	26 lines changed or deleted		169 lines changed or added

	cuviddec.h		cuviddec.h
	skipping to change at line 40		skipping to change at line 40
	*		*
	* Any use of this source code in individual and commercial software must		* Any use of this source code in individual and commercial software must
	* include, in the user documentation and internal comments to the code,		* include, in the user documentation and internal comments to the code,
	* the above Disclaimer and U.S. Government End Users Notice.		* the above Disclaimer and U.S. Government End Users Notice.
	*/		*/
	#if !defined(__CUDA_VIDEO_H__)		#if !defined(__CUDA_VIDEO_H__)
	#define __CUDA_VIDEO_H__		#define __CUDA_VIDEO_H__
	#ifndef __cuda_cuda_h__		#ifndef __cuda_cuda_h__
	#if !defined(CUDA_FORCE_API_VERSION)
	#define CUDA_FORCE_API_VERSION 3010 // Currently does not support the n
	ew 3.2 apis (64-bit CUdeviceptr)
	#endif
	#include <cuda.h>		#include <cuda.h>
	#endif // __cuda_cuda_h__		#endif // __cuda_cuda_h__
	#if defined(__x86_64) || defined(AMD64) || defined(_M_AMD64)		#if defined(__x86_64) || defined(AMD64) || defined(_M_AMD64)
	#if (CUDA_VERSION >= 3020) && (!defined(CUDA_FORCE_API_VERSION) || (CUDA_FO RCE_API_VERSION >= 3020))		#if (CUDA_VERSION >= 3020) && (!defined(CUDA_FORCE_API_VERSION) || (CUDA_FO RCE_API_VERSION >= 3020))
	#error "CUVID currently does not support cuda 3.2 64-bit apis"		#define __CUVID_DEVPTR64
	#endif		#endif
	#endif		#endif
	#if defined(__cplusplus)		#if defined(__cplusplus)
	extern "C" {		extern "C" {
	#endif /* __cplusplus */		#endif /* __cplusplus */
	typedef void *CUvideodecoder;		typedef void *CUvideodecoder;
	typedef struct _CUcontextlock_st *CUvideoctxlock;		typedef struct _CUcontextlock_st *CUvideoctxlock;
	typedef enum cudaVideoCodec_enum {		typedef enum cudaVideoCodec_enum {
	cudaVideoCodec_MPEG1=0,		cudaVideoCodec_MPEG1=0,
	cudaVideoCodec_MPEG2,		cudaVideoCodec_MPEG2,
	cudaVideoCodec_MPEG4,		cudaVideoCodec_MPEG4,
	cudaVideoCodec_VC1,		cudaVideoCodec_VC1,
	cudaVideoCodec_H264,		cudaVideoCodec_H264,
	cudaVideoCodec_JPEG,		cudaVideoCodec_JPEG,
			cudaVideoCodec_H264_SVC,
	cudaVideoCodec_NumCodecs,		cudaVideoCodec_NumCodecs,
	// Uncompressed YUV		// Uncompressed YUV
	cudaVideoCodec_YUV420 = (('I'<<24)|('Y'<<16)|('U'<<8)|('V')), // Y,U, V (4:2:0)		cudaVideoCodec_YUV420 = (('I'<<24)|('Y'<<16)|('U'<<8)|('V')), // Y,U, V (4:2:0)
	cudaVideoCodec_YV12 = (('Y'<<24)|('V'<<16)|('1'<<8)|('2')), // Y,V, U (4:2:0)		cudaVideoCodec_YV12 = (('Y'<<24)|('V'<<16)|('1'<<8)|('2')), // Y,V, U (4:2:0)
	cudaVideoCodec_NV12 = (('N'<<24)|('V'<<16)|('1'<<8)|('2')), // Y,UV (4:2:0)		cudaVideoCodec_NV12 = (('N'<<24)|('V'<<16)|('1'<<8)|('2')), // Y,UV (4:2:0)
	cudaVideoCodec_YUYV = (('Y'<<24)|('U'<<16)|('Y'<<8)|('V')), // YUYV /YUY2 (4:2:2)		cudaVideoCodec_YUYV = (('Y'<<24)|('U'<<16)|('Y'<<8)|('V')), // YUYV /YUY2 (4:2:2)
	cudaVideoCodec_UYVY = (('U'<<24)|('Y'<<16)|('V'<<8)|('Y')), // UYVY (4:2:2)		cudaVideoCodec_UYVY = (('U'<<24)|('Y'<<16)|('V'<<8)|('Y')), // UYVY (4:2:2)
	} cudaVideoCodec;		} cudaVideoCodec;
	typedef enum cudaVideoSurfaceFormat_enum {		typedef enum cudaVideoSurfaceFormat_enum {
	skipping to change at line 146		skipping to change at line 144
	typedef struct _CUVIDH264DPBENTRY		typedef struct _CUVIDH264DPBENTRY
	{		{
	int PicIdx; // picture index of reference frame		int PicIdx; // picture index of reference frame
	int FrameIdx; // frame_num(short-term) or LongTermFrameIdx(lo ng-term)		int FrameIdx; // frame_num(short-term) or LongTermFrameIdx(lo ng-term)
	int is_long_term; // 0=short term reference, 1=long term referenc e		int is_long_term; // 0=short term reference, 1=long term referenc e
	int not_existing; // non-existing reference frame (corresponding PicIdx should be set to -1)		int not_existing; // non-existing reference frame (corresponding PicIdx should be set to -1)
	int used_for_reference; // 0=unused, 1=top_field, 2=bottom_field, 3=bot h_fields		int used_for_reference; // 0=unused, 1=top_field, 2=bottom_field, 3=bot h_fields
	int FieldOrderCnt[2]; // field order count of top and bottom fields		int FieldOrderCnt[2]; // field order count of top and bottom fields
	} CUVIDH264DPBENTRY;		} CUVIDH264DPBENTRY;
			typedef struct _CUVIDH264MVCEXT
			{
			int num_views_minus1;
			int view_id;
			unsigned char inter_view_flag;
			unsigned char num_inter_view_refs_l0;
			unsigned char num_inter_view_refs_l1;
			unsigned char MVCReserved8Bits;
			int InterViewRefsL0[16];
			int InterViewRefsL1[16];
			} CUVIDH264MVCEXT;
			typedef struct _CUVIDH264SVCEXT
			{
			unsigned char profile_idc;
			unsigned char level_idc;
			unsigned char DQId;
			unsigned char DQIdMax;
			unsigned char disable_inter_layer_deblocking_filter_idc;
			unsigned char ref_layer_chroma_phase_y_plus1;
			char inter_layer_slice_alpha_c0_offset_div2;
			char inter_layer_slice_beta_offset_div2;
			unsigned short DPBEntryValidFlag;
			unsigned char inter_layer_deblocking_filter_control_present_flag;
			unsigned char extended_spatial_scalability_idc;
			unsigned char adaptive_tcoeff_level_prediction_flag;
			unsigned char slice_header_restriction_flag;
			unsigned char chroma_phase_x_plus1_flag;
			unsigned char chroma_phase_y_plus1;
			unsigned char tcoeff_level_prediction_flag;
			unsigned char constrained_intra_resampling_flag;
			unsigned char ref_layer_chroma_phase_x_plus1_flag;
			unsigned char store_ref_base_pic_flag;
			unsigned char Reserved8BitsA;
			unsigned char Reserved8BitsB;
			// For the 4 scaled_ref_layer_XX fields below,
			// if (extended_spatial_scalability_idc == 1), SPS field, G.7.3.2.1.4,
			add prefix "seq_"
			// if (extended_spatial_scalability_idc == 2), SLH field, G.7.3.3.4,
			short scaled_ref_layer_left_offset;
			short scaled_ref_layer_top_offset;
			short scaled_ref_layer_right_offset;
			short scaled_ref_layer_bottom_offset;
			struct _CUVIDPICPARAMS *pNextLayer; // Points to the picparams for the
			next layer to be decoded. Linked list ends at the target layer.
			int bRefBaseLayer; // whether to store ref base pic
			} CUVIDH264SVCEXT;
	typedef struct _CUVIDH264PICPARAMS		typedef struct _CUVIDH264PICPARAMS
	{		{
	// SPS		// SPS
	int log2_max_frame_num_minus4;		int log2_max_frame_num_minus4;
	int pic_order_cnt_type;		int pic_order_cnt_type;
	int log2_max_pic_order_cnt_lsb_minus4;		int log2_max_pic_order_cnt_lsb_minus4;
	int delta_pic_order_always_zero_flag;		int delta_pic_order_always_zero_flag;
	int frame_mbs_only_flag;		int frame_mbs_only_flag;
	int direct_8x8_inference_flag;		int direct_8x8_inference_flag;
	int num_ref_frames; // NOTE: shall meet level 4.1 restricti ons		int num_ref_frames; // NOTE: shall meet level 4.1 restricti ons
	skipping to change at line 183		skipping to change at line 227
	int chroma_qp_index_offset;		int chroma_qp_index_offset;
	int second_chroma_qp_index_offset;		int second_chroma_qp_index_offset;
	int ref_pic_flag;		int ref_pic_flag;
	int frame_num;		int frame_num;
	int CurrFieldOrderCnt[2];		int CurrFieldOrderCnt[2];
	// DPB		// DPB
	CUVIDH264DPBENTRY dpb[16]; // List of reference frames within the DPB		CUVIDH264DPBENTRY dpb[16]; // List of reference frames within the DPB
	// Quantization Matrices (raster-order)		// Quantization Matrices (raster-order)
	unsigned char WeightScale4x4[6][16];		unsigned char WeightScale4x4[6][16];
	unsigned char WeightScale8x8[2][64];		unsigned char WeightScale8x8[2][64];
			unsigned int Reserved[16];
			union
			{
			CUVIDH264MVCEXT mvcext;
			CUVIDH264SVCEXT svcext;
			};
	} CUVIDH264PICPARAMS;		} CUVIDH264PICPARAMS;
	/////////////////////////////////////////////////////////////////////////// /////////////////////		/////////////////////////////////////////////////////////////////////////// /////////////////////
	//		//
	// MPEG-2 Picture Parameters		// MPEG-2 Picture Parameters
	//		//
	typedef struct _CUVIDMPEG2PICPARAMS		typedef struct _CUVIDMPEG2PICPARAMS
	{		{
	int ForwardRefIdx; // Picture index of forward reference (P/B- frames)		int ForwardRefIdx; // Picture index of forward reference (P/B- frames)
	skipping to change at line 349		skipping to change at line 399
	//		//
	// Post-processing		// Post-processing
	//		//
	typedef struct _CUVIDPROCPARAMS		typedef struct _CUVIDPROCPARAMS
	{		{
	int progressive_frame; // Input is progressive (deinterlace_mode will be ignored)		int progressive_frame; // Input is progressive (deinterlace_mode will be ignored)
	int second_field; // Output the second field (ignored if deinterl ace mode is Weave)		int second_field; // Output the second field (ignored if deinterl ace mode is Weave)
	int top_field_first; // Input frame is top field first (1st field is top, 2nd field is bottom)		int top_field_first; // Input frame is top field first (1st field is top, 2nd field is bottom)
	int unpaired_field; // Input only contains one field (2nd field is invalid)		int unpaired_field; // Input only contains one field (2nd field is invalid)
	// The fields below are reserved for future use (set to zero)		// The fields below are used for raw YUV input
	unsigned int reserved_flags; // Reserved for future use (set to zero)		unsigned int reserved_flags; // Reserved for future use (set to
	CUdeviceptr raw_input_dptr; // Reserved for raw YUV extensions (set to		zero)
	zero)		unsigned int reserved_zero; // Reserved (set to zero)
	unsigned int reserved_zero; // Reserved for future use (set to zero)		unsigned long long raw_input_dptr; // Input CUdeviceptr for raw YUV ex
	unsigned int raw_input_pitch; // Reserved for raw YUV extensions (set t		tensions
	o zero)		unsigned int raw_input_pitch; // pitch in bytes of raw YUV input
	unsigned int Reserved[53];		(should be aligned appropriately)
			unsigned int raw_input_format; // Reserved for future use (set to
			zero)
			unsigned long long raw_output_dptr; // Reserved for future use (set to
			zero)
			unsigned int raw_output_pitch; // Reserved for future use (set to
			zero)
			unsigned int Reserved[48];
	void *Reserved3[3];		void *Reserved3[3];
	} CUVIDPROCPARAMS;		} CUVIDPROCPARAMS;
	/////////////////////////////////////////////////////////////////////////// /////////////////////		/////////////////////////////////////////////////////////////////////////// /////////////////////
	//		//
	// In order to maximize decode latencies, there should be always at least 2 pictures in the decode		// In order to maximize decode latencies, there should be always at least 2 pictures in the decode
	// queue at any time, in order to make sure that all decode engines are alw ays busy.		// queue at any time, in order to make sure that all decode engines are alw ays busy.
	//		//
	// Overall data flow:		// Overall data flow:
	// - cuvidCreateDecoder(...)		// - cuvidCreateDecoder(...)
	skipping to change at line 391		skipping to change at line 444
	//		//
	/////////////////////////////////////////////////////////////////////////// /////////////////////		/////////////////////////////////////////////////////////////////////////// /////////////////////
	// Create/Destroy the decoder object		// Create/Destroy the decoder object
	extern CUresult CUDAAPI cuvidCreateDecoder(CUvideodecoder *phDecoder, CUVID DECODECREATEINFO *pdci);		extern CUresult CUDAAPI cuvidCreateDecoder(CUvideodecoder *phDecoder, CUVID DECODECREATEINFO *pdci);
	extern CUresult CUDAAPI cuvidDestroyDecoder(CUvideodecoder hDecoder);		extern CUresult CUDAAPI cuvidDestroyDecoder(CUvideodecoder hDecoder);
	// Decode a single picture (field or frame)		// Decode a single picture (field or frame)
	extern CUresult CUDAAPI cuvidDecodePicture(CUvideodecoder hDecoder, CUVIDPI CPARAMS *pPicParams);		extern CUresult CUDAAPI cuvidDecodePicture(CUvideodecoder hDecoder, CUVIDPI CPARAMS *pPicParams);
			#if !defined(__CUVID_DEVPTR64) || defined(__CUVID_INTERNAL)
	// Post-process and map a video frame for use in cuda		// Post-process and map a video frame for use in cuda
	extern CUresult CUDAAPI cuvidMapVideoFrame(CUvideodecoder hDecoder, int nPi cIdx,		extern CUresult CUDAAPI cuvidMapVideoFrame(CUvideodecoder hDecoder, int nPi cIdx,
	CUdeviceptr *pDevPtr, unsigned i nt *pPitch,		unsigned int *pDevPtr, unsigned int *pPitch,
	CUVIDPROCPARAMS *pVPP);		CUVIDPROCPARAMS *pVPP);
	// Unmap a previously mapped video frame		// Unmap a previously mapped video frame
	extern CUresult CUDAAPI cuvidUnmapVideoFrame(CUvideodecoder hDecoder, CUdev		extern CUresult CUDAAPI cuvidUnmapVideoFrame(CUvideodecoder hDecoder, unsig
	iceptr DevPtr);		ned int DevPtr);
			#endif
			#if defined(__x86_64) || defined(AMD64) || defined(_M_AMD64)
			extern CUresult CUDAAPI cuvidMapVideoFrame64(CUvideodecoder hDecoder, int n
			PicIdx, unsigned long long *pDevPtr,
			unsigned int *pPitch, CUVIDPRO
			CPARAMS *pVPP);
			extern CUresult CUDAAPI cuvidUnmapVideoFrame64(CUvideodecoder hDecoder, uns
			igned long long DevPtr);
			#if defined(__CUVID_DEVPTR64) && !defined(__CUVID_INTERNAL)
			#define cuvidMapVideoFrame cuvidMapVideoFrame64
			#define cuvidUnmapVideoFrame cuvidUnmapVideoFrame64
			#endif
			#endif
	// Get the pointer to the d3d9 surface that is the decode RT		// Get the pointer to the d3d9 surface that is the decode RT
	extern CUresult CUDAAPI cuvidGetVideoFrameSurface(CUvideodecoder hDecoder,		extern CUresult CUDAAPI cuvidGetVideoFrameSurface(CUvideodecoder hDecoder,
	int nPicIdx,		int nPicIdx, void **pSrcSurface);
	void **pSrcSurface);
	/////////////////////////////////////////////////////////////////////////// /////////////////////		/////////////////////////////////////////////////////////////////////////// /////////////////////
	//		//
	// Context-locking: to facilitate multi-threaded implementations, the follo wing 4 functions		// Context-locking: to facilitate multi-threaded implementations, the follo wing 4 functions
	// provide a simple mutex-style host synchronization. If a non-NULL context is specified		// provide a simple mutex-style host synchronization. If a non-NULL context is specified
	// in CUVIDDECODECREATEINFO, the codec library will acquire the mutex assoc iated with the given		// in CUVIDDECODECREATEINFO, the codec library will acquire the mutex assoc iated with the given
	// context before making any cuda calls.		// context before making any cuda calls.
	// A multi-threaded application could create a lock associated with a conte xt handle so that		// A multi-threaded application could create a lock associated with a conte xt handle so that
	// multiple threads can safely share the same cuda context:		// multiple threads can safely share the same cuda context:
	// - use cuCtxPopCurrent immediately after context creation in order to cr eate a 'floating' context		// - use cuCtxPopCurrent immediately after context creation in order to cr eate a 'floating' context
End of changes. 10 change blocks.
	19 lines changed or deleted		91 lines changed or added

	device_functions.h		device_functions.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(__DEVICE_FUNCTIONS_H__)		#if !defined(__DEVICE_FUNCTIONS_H__)
	#define __DEVICE_FUNCTIONS_H__		#define __DEVICE_FUNCTIONS_H__
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	skipping to change at line 86		skipping to change at line 100
	extern __device__ void __syncthreads(void);		extern __device__ void __syncthreads(void);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __device__ void __prof_trigger(int);		extern __device__ void __prof_trigger(int);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __device__ void __threadfence(void);		extern __device__ void __threadfence(void);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __device__ void __threadfence_block(void);		extern __device__ void __threadfence_block(void);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __device__ void __trap(void);		extern __device__ void __trap(void);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __device__ void __brkpt(int);		extern __device__ void __brkpt(int c = 0);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __device__ float __saturatef(float);		extern __device__ float __saturatef(float);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __device__ unsigned int __sad(int, int, unsigned int);		extern __device__ unsigned int __sad(int, int, unsigned int);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __device__ unsigned int __usad(unsigned int, unsigned int, unsigned int);		extern __device__ unsigned int __usad(unsigned int, unsigned int, unsigned int);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	skipping to change at line 394		skipping to change at line 408
	static __inline__ __device__ unsigned int umul24(unsigned int a, unsigned i nt b)		static __inline__ __device__ unsigned int umul24(unsigned int a, unsigned i nt b)
	{		{
	return __umul24(a, b);		return __umul24(a, b);
	}		}
	static __inline__ __device__ void trap(void)		static __inline__ __device__ void trap(void)
	{		{
	__trap();		__trap();
	}		}
	static __inline__ __device__ void brkpt(int c)		/* argument is optional, value of 0 means no value */
			static __inline__ __device__ void brkpt(int c = 0)
	{		{
	__brkpt(c);		__brkpt(c);
	}		}
	static __inline__ __device__ void syncthreads(void)		static __inline__ __device__ void syncthreads(void)
	{		{
	__syncthreads();		__syncthreads();
	}		}
	static __inline__ __device__ void prof_trigger(int e)		static __inline__ __device__ void prof_trigger(int e)
End of changes. 8 change blocks.
	30 lines changed or deleted		44 lines changed or added

	device_launch_parameters.h		device_launch_parameters.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(__DEVICE_LAUNCH_PARAMETERS_H__)		#if !defined(__DEVICE_LAUNCH_PARAMETERS_H__)
	#define __DEVICE_LAUNCH_PARAMETERS_H__		#define __DEVICE_LAUNCH_PARAMETERS_H__
	#include "vector_types.h"		#include "vector_types.h"
	#if !defined(__STORAGE__)		#if !defined(__STORAGE__)
	#define __STORAGE__ \		#define __STORAGE__ \
End of changes. 6 change blocks.
	28 lines changed or deleted		41 lines changed or added

	device_runtime.h		device_runtime.h
	skipping to change at line 41		skipping to change at line 41
	* Any use of this source code in individual and commercial software must		* Any use of this source code in individual and commercial software must
	* include, in the user documentation and internal comments to the code,		* include, in the user documentation and internal comments to the code,
	* the above Disclaimer and U.S. Government End Users Notice.		* the above Disclaimer and U.S. Government End Users Notice.
	*/		*/
	#define __CUDA_INTERNAL_COMPILATION__		#define __CUDA_INTERNAL_COMPILATION__
	#include "host_defines.h"		#include "host_defines.h"
	#define __no_sc__		#define __no_sc__
	#define __pad__(f)
	/*TEXTURE_TYPE*/		/*TEXTURE_TYPE*/
	typedef const void *__texture_type__;		typedef const void *__texture_type__;
	/*SURFACE_TYPE*/		/*SURFACE_TYPE*/
	typedef const void *__surface_type__;		typedef const void *__surface_type__;
	#if defined(__CUDABE__) /* cudabe compiler */		#if defined(__CUDABE__) /* cudabe compiler */
	#if __CUDA_ARCH__ >= 200		#if __CUDA_ARCH__ >= 200
	skipping to change at line 76		skipping to change at line 75
	#define __val_param(name) \		#define __val_param(name) \
	__val_param##name		__val_param##name
	#define __copy_param(local_decl, param) \		#define __copy_param(local_decl, param) \
	local_decl = param		local_decl = param
	#define __var_used__ \		#define __var_used__ \
	__attribute__((__used__))		__attribute__((__used__))
	#define __storage_extern_unsized__shared__ \		#define __storage_extern_unsized__shared__ \
	extern		extern
	#define __cxa_vec_ctor(n, num, size, c, d) \		#define __cxa_vec_ctor(n, num, size, c, d) \
	({ int i; for (i = 0; i < num; i++) c((void*)n + i*size); (void)0; })		({ int i; for (i = 0; i < num; i++) c((void*)n + i*size); (void)0; })
			#define __cxa_vec_cctor(dest, src, num, size, c, d) \
			({ int i; for (i = 0; i < num; i++) \
			c((void*)dest + i*size, (void*)src + i*size); (void)0; })
	#define __cxa_vec_dtor(n, num, size, d) \		#define __cxa_vec_dtor(n, num, size, d) \
	{ int i; for (i = num-1; i >= 0; i--) d((void*)n + i*size); }		{ int i; for (i = num-1; i >= 0; i--) d((void*)n + i*size); }
			#define __cxa_vec_new2(num, size, pad, c, d, m, f) \
			({void *t = m(num*size + pad); *(size_t*)t = num; \
			(void)__cxa_vec_ctor(t+pad, num, size, c, d); t+pad;})
			#define __cxa_vec_new3(num, size, pad, c, d, m, f) \
			__cxa_vec_new2(num, size, pad, c, d, m, f)
			#define __cxa_vec_delete2(n, size, pad, d, f) \
			{ void *t = (void*)n - pad; size_t num = *(size_t*)t; \
			__cxa_vec_dtor(n, num, size, d); f(t); }
			#define __cxa_vec_delete(n, size, pad, d) \
			__cxa_vec_delete2(n, size, pad, d, free)
			#define __cxa_vec_delete3(n, size, pad, d, f) \
			{ void *t = (void*)n - pad; size_t num = *(size_t*)t; \
			size_t tsize = num*size+pad; \
			__cxa_vec_dtor(n, num, size, d); f(t, tsize); }
	#undef __cdecl		#undef __cdecl
	#define __cdecl		#define __cdecl
	#undef __w64		#undef __w64
	#define __w64		#define __w64
	#elif defined(__CUDACC__) /* cudafe compiler */		#elif defined(__CUDACC__) /* cudafe compiler */
	#define __loc_sc__(loc, size, sc) \		#define __loc_sc__(loc, size, sc) \
	sc loc		sc loc
	#define __text__		#define __text__
	#define __surf__		#define __surf__
	#define ___device__(sc) \		#define ___device__(sc) \
	sc __device__		sc __device__
	#define __val_param(name) \		#define __val_param(name) \
	name		name
	#define __copy_param(local_decl, param)		#define __copy_param(local_decl, param)
			#define _Znwm \
			malloc
			#define _Znwj \
			malloc
			#define _Znwy \
			malloc
			#define _Znam \
			malloc
			#define _Znaj \
			malloc
			#define _Znay \
			malloc
			#define _ZdlPv \
			free
			#define _ZdaPv \
			free
			#define __cxa_pure_virtual \
			0
			extern __device__ void* malloc(size_t);
			extern __device__ void free(void*);
	#endif /* __CUDABE__ */		#endif /* __CUDABE__ */
	#include "builtin_types.h"		#include "builtin_types.h"
	#include "device_launch_parameters.h"		#include "device_launch_parameters.h"
	#include "storage_class.h"		#include "storage_class.h"
End of changes. 4 change blocks.
	1 lines changed or deleted		38 lines changed or added

	device_types.h		device_types.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(__DEVICE_TYPES_H__)		#if !defined(__DEVICE_TYPES_H__)
	#define __DEVICE_TYPES_H__		#define __DEVICE_TYPES_H__
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
End of changes. 6 change blocks.
	28 lines changed or deleted		41 lines changed or added

	driver_functions.h		driver_functions.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(__DRIVER_FUNCTIONS_H__)		#if !defined(__DRIVER_FUNCTIONS_H__)
	#define __DRIVER_FUNCTIONS_H__		#define __DRIVER_FUNCTIONS_H__
	#include "builtin_types.h"		#include "builtin_types.h"
	#include "host_defines.h"		#include "host_defines.h"
	#include "driver_types.h"		#include "driver_types.h"
	/**		/**
End of changes. 6 change blocks.
	28 lines changed or deleted		41 lines changed or added

	driver_types.h		driver_types.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(__DRIVER_TYPES_H__)		#if !defined(__DRIVER_TYPES_H__)
	#define __DRIVER_TYPES_H__		#define __DRIVER_TYPES_H__
	/**		/**
	* \file		* \file
	* \name Data types used by CUDA Runtime		* \name Data types used by CUDA Runtime
	* \author NVIDIA Corporation		* \author NVIDIA Corporation
	* \brief Data types used by CUDA Runtime		* \brief Data types used by CUDA Runtime
	skipping to change at line 63		skipping to change at line 77
	* *		* *
	* TYPE DEFINITIONS USED BY RUNTIME API *		* TYPE DEFINITIONS USED BY RUNTIME API *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	#if !defined(__CUDA_INTERNAL_COMPILATION__)		#if !defined(__CUDA_INTERNAL_COMPILATION__)
	#include <limits.h>		#include <limits.h>
	#include <stddef.h>		#include <stddef.h>
	#define cudaHostAllocDefault 0 /**< Default page-locked allocation		#define cudaHostAllocDefault 0x00 /**< Default page-locked alloc
	flag */		ation flag */
	#define cudaHostAllocPortable 1 /**< Pinned memory accessible by al		#define cudaHostAllocPortable 0x01 /**< Pinned memory accessible
	l CUDA contexts */		by all CUDA contexts */
	#define cudaHostAllocMapped 2 /**< Map allocation into device spa		#define cudaHostAllocMapped 0x02 /**< Map allocation into devic
	ce */		e space */
	#define cudaHostAllocWriteCombined 4 /**< Write-combined memory */		#define cudaHostAllocWriteCombined 0x04 /**< Write-combined memory */
	#define cudaEventDefault 0 /**< Default event flag */		#define cudaHostRegisterDefault 0x00 /**< Default host memory regis
	#define cudaEventBlockingSync 1 /**< Event uses blocking synchroniz		tration flag */
	ation */		#define cudaHostRegisterPortable 0x01 /**< Pinned memory accessible
	#define cudaEventDisableTiming 2 /**< Event will not record timing d		by all CUDA contexts */
	ata */		#define cudaHostRegisterMapped 0x02 /**< Map registered memory int
			o device space */
	#define cudaDeviceScheduleAuto 0 /**< Device flag - Automatic schedu		#define cudaPeerAccessDefault 0x00 /**< Default peer addressing e
	ling */		nable flag */
	#define cudaDeviceScheduleSpin 1 /**< Device flag - Spin default sch
	eduling */
	#define cudaDeviceScheduleYield 2 /**< Device flag - Yield default sc
	heduling */
	#define cudaDeviceBlockingSync 4 /**< Device flag - Use blocking syn
	chronization */
	#define cudaDeviceMapHost 8 /**< Device flag - Support mapped p
	inned allocations */
	#define cudaDeviceLmemResizeToMax 16 /**< Device flag - Keep local memor
	y allocation after launch */
	#define cudaDeviceMask 0x1f /**< Device flags mask */
	#define cudaArrayDefault 0x00 /**< Default CUDA array allocation		#define cudaEventDefault 0x00 /**< Default event flag */
	flag */		#define cudaEventBlockingSync 0x01 /**< Event uses blocking synch
	#define cudaArraySurfaceLoadStore 0x02 /**< Must be set in cudaMallocArray		ronization */
	in order to bind surfaces to the CUDA array */		#define cudaEventDisableTiming 0x02 /**< Event will not record tim
			ing data */
			#define cudaDeviceScheduleAuto 0x00 /**< Device flag - Automatic s
			cheduling */
			#define cudaDeviceScheduleSpin 0x01 /**< Device flag - Spin defaul
			t scheduling */
			#define cudaDeviceScheduleYield 0x02 /**< Device flag - Yield defau
			lt scheduling */
			#define cudaDeviceScheduleBlockingSync 0x04 /**< Device flag - Use blockin
			g synchronization */
			#define cudaDeviceBlockingSync 0x04 /**< Device flag - Use blockin
			g synchronization \deprecated */
			#define cudaDeviceMapHost 0x08 /**< Device flag - Support map
			ped pinned allocations */
			#define cudaDeviceLmemResizeToMax 0x10 /**< Device flag - Keep local
			memory allocation after launch */
			#define cudaDeviceMask 0x1f /**< Device flags mask */
			#define cudaArrayDefault 0x00 /**< Default CUDA array alloc
			ation flag */
			#define cudaArrayLayered 0x01 /**< Must be set in cudaMallo
			c3DArray to create a layered texture */
			#define cudaArraySurfaceLoadStore 0x02 /**< Must be set in cudaMallo
			cArray in order to bind surfaces to the CUDA array */
	#endif /* !__CUDA_INTERNAL_COMPILATION__ */		#endif /* !__CUDA_INTERNAL_COMPILATION__ */
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	/**		/**
	skipping to change at line 421		skipping to change at line 443
	/**		/**
	* This indicates that multiple surfaces (across separate CUDA source		* This indicates that multiple surfaces (across separate CUDA source
	* files in the application) share the same string name.		* files in the application) share the same string name.
	*/		*/
	cudaErrorDuplicateSurfaceName = 45,		cudaErrorDuplicateSurfaceName = 45,
	/**		/**
	* This indicates that all CUDA devices are busy or unavailable at the cu rrent		* This indicates that all CUDA devices are busy or unavailable at the cu rrent
	* time. Devices are often busy/unavailable due to use of		* time. Devices are often busy/unavailable due to use of
	* ::cudaComputeModeExclusive or ::cudaComputeModeProhibited. They can al		* ::cudaComputeModeExclusive, ::cudaComputeModeProhibited or when long
	so		* running CUDA kernels have filled up the GPU and are blocking new work
	* be unavailable due to memory constraints on a device that already has		* from starting. They can also be unavailable due to memory constraints
	* active CUDA work being performed.		* on a device that already has active CUDA work being performed.
	*/		*/
	cudaErrorDevicesUnavailable = 46,		cudaErrorDevicesUnavailable = 46,
	/**		/**
	* This indicates that the device kernel image is invalid.		* This indicates that the device kernel image is invalid.
	*/		*/
	cudaErrorInvalidKernelImage = 47,		cudaErrorInvalidKernelImage = 47,
	/**		/**
	* This indicates that there is no kernel image available that is suitabl e		* This indicates that there is no kernel image available that is suitabl e
	* for the device. This can occur when a user specifies code generation		* for the device. This can occur when a user specifies code generation
	* options for a particular CUDA source file that do not include the		* options for a particular CUDA source file that do not include the
	* corresponding device configuration.		* corresponding device configuration.
	*/		*/
	cudaErrorNoKernelImageForDevice = 48,		cudaErrorNoKernelImageForDevice = 48,
	/**		/**
	* This indicates that the current context is not compatible with this		* This indicates that the current context is not compatible with this
	* version of the CUDA Runtime. This can only occur if you are using CUDA		* the CUDA Runtime. This can only occur if you are using CUDA
	* Runtime/Driver interoperability and have created an existing Driver		* Runtime/Driver interoperability and have created an existing Driver
	* context using an older API. Please see \ref CUDART_DRIVER		* context using the driver API. The Driver context may be incompatible
	* "Interactions with the CUDA Driver API" for more information.		* either because the Driver context was created using an older version
			* of the API, because the Runtime API call expects a primary driver
			* contextand the Driver context is not primary, or because the Driver
			* context has been destroyed. Please see \ref CUDART_DRIVER "Interaction
			s
			* with the CUDA Driver API" for more information.
	*/		*/
	cudaErrorIncompatibleDriverContext = 49,		cudaErrorIncompatibleDriverContext = 49,
	/**		/**
			* This error indicates that a call to ::cudaDeviceEnablePeerAccess() is
			* trying to re-enable peer addressing on from a context which has alread
			y
			* had peer addressing enabled.
			*/
			cudaErrorPeerAccessAlreadyEnabled = 50,
			/**
			* This error indicates that ::cudaDeviceDisablePeerAccess() is trying to
			* disable peer addressing which has not been enabled yet via
			* ::cudaDeviceEnablePeerAccess().
			*/
			cudaErrorPeerAccessNotEnabled = 51,
			/**
			* This indicates that a call tried to access an exclusive-thread device
			that
			* is already in use by a different thread.
			*/
			cudaErrorDeviceAlreadyInUse = 54,
			/**
			* This indicates profiler has been disabled for this run and thus runtim
			e
			* APIs cannot be used to profile subsets of the program. This can
			* happen when the application is running with external profiling tools
			* like visual profiler.
			*/
			cudaErrorProfilerDisabled = 55,
			/**
			* This indicates profiler has not been initialized yet. cudaProfilerInit
			ialize()
			* must be called before calling cudaProfilerStart and cudaProfilerStop t
			o
			* initialize profiler.
			*/
			cudaErrorProfilerNotInitialized = 56,
			/**
			* This indicates profiler is already started. This error can be returned
			if
			* cudaProfilerStart() is called multiple times without subsequent call
			* to cudaProfilerStop().
			*/
			cudaErrorProfilerAlreadyStarted = 57,
			/**
			* This indicates profiler is already stopped. This error can be returned
			if
			* cudaProfilerStop() is called without starting profiler using cudaProfi
			lerStart().
			*/
			cudaErrorProfilerAlreadyStopped = 58,
			/**
	* This indicates an internal startup failure in the CUDA runtime.		* This indicates an internal startup failure in the CUDA runtime.
	*/		*/
	cudaErrorStartupFailure = 0x7f,		cudaErrorStartupFailure = 0x7f,
	/**		/**
	* Any unhandled CUDA driver error is added to this value and returned vi a		* Any unhandled CUDA driver error is added to this value and returned vi a
	* the runtime. Production releases of CUDA should not return such errors .		* the runtime. Production releases of CUDA should not return such errors .
	*/		*/
	cudaErrorApiFailureBase = 10000		cudaErrorApiFailureBase = 10000
	};		};
	skipping to change at line 493		skipping to change at line 568
	enum cudaChannelFormatKind f; /**< Channel format kind */		enum cudaChannelFormatKind f; /**< Channel format kind */
	};		};
	/**		/**
	* CUDA array		* CUDA array
	*/		*/
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	struct cudaArray;		struct cudaArray;
	/**		/**
			* CUDA memory types
			*/
			/*DEVICE_BUILTIN*/
			enum cudaMemoryType
			{
			cudaMemoryTypeHost = 1, /**< Host memory */
			cudaMemoryTypeDevice = 2 /**< Device memory */
			};
			/**
	* CUDA memory copy types		* CUDA memory copy types
	*/		*/
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	enum cudaMemcpyKind		enum cudaMemcpyKind
	{		{
	cudaMemcpyHostToHost = 0, /**< Host -> Host */		cudaMemcpyHostToHost = 0, /**< Host -> Host */
	cudaMemcpyHostToDevice = 1, /**< Host -> Device */		cudaMemcpyHostToDevice = 1, /**< Host -> Device */
	cudaMemcpyDeviceToHost = 2, /**< Device -> Host */		cudaMemcpyDeviceToHost = 2, /**< Device -> Host */
	cudaMemcpyDeviceToDevice = 3 /**< Device -> Device */		cudaMemcpyDeviceToDevice = 3, /**< Device -> Device */
			cudaMemcpyDefault = 4 /**< Default based unified virt
			ual address space */
	};		};
	/**		/**
	* CUDA Pitched memory pointer		* CUDA Pitched memory pointer
	* \sa ::make_cudaPitchedPtr		* \sa ::make_cudaPitchedPtr
	*/		*/
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	struct cudaPitchedPtr		struct cudaPitchedPtr
	{		{
	void *ptr; /**< Pointer to allocated memory */		void *ptr; /**< Pointer to allocated memory */
	skipping to change at line 560		skipping to change at line 646
	struct cudaArray *dstArray; /**< Destination memory address */		struct cudaArray *dstArray; /**< Destination memory address */
	struct cudaPos dstPos; /**< Destination position offset */		struct cudaPos dstPos; /**< Destination position offset */
	struct cudaPitchedPtr dstPtr; /**< Pitched destination memory address */		struct cudaPitchedPtr dstPtr; /**< Pitched destination memory address */
	struct cudaExtent extent; /**< Requested memory copy size */		struct cudaExtent extent; /**< Requested memory copy size */
	enum cudaMemcpyKind kind; /**< Type of transfer */		enum cudaMemcpyKind kind; /**< Type of transfer */
	};		};
	/**		/**
			* CUDA 3D cross-device memory copying parameters
			*/
			/*DEVICE_BUILTIN*/
			struct cudaMemcpy3DPeerParms
			{
			struct cudaArray *srcArray; /**< Source memory address */
			struct cudaPos srcPos; /**< Source position offset */
			struct cudaPitchedPtr srcPtr; /**< Pitched source memory address */
			int srcDevice; /**< Source device */
			struct cudaArray *dstArray; /**< Destination memory address */
			struct cudaPos dstPos; /**< Destination position offset */
			struct cudaPitchedPtr dstPtr; /**< Pitched destination memory address
			*/
			int dstDevice; /**< Destination device */
			struct cudaExtent extent; /**< Requested memory copy size */
			};
			/**
	* CUDA graphics interop resource		* CUDA graphics interop resource
	*/		*/
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	struct cudaGraphicsResource;		struct cudaGraphicsResource;
	/**		/**
	* CUDA graphics interop register flags		* CUDA graphics interop register flags
	*/		*/
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	enum cudaGraphicsRegisterFlags		enum cudaGraphicsRegisterFlags
	{		{
	cudaGraphicsRegisterFlagsNone = 0 /**< Default */		cudaGraphicsRegisterFlagsNone = 0, /**< Default */
			cudaGraphicsRegisterFlagsReadOnly = 1, /**< CUDA will not write
			to this resource */
			cudaGraphicsRegisterFlagsWriteDiscard = 2, /**< CUDA will only write
			to and will not read from this resource */
			cudaGraphicsRegisterFlagsSurfaceLoadStore = 4 /**< CUDA will bind this
			resource to a surface reference */
	};		};
	/**		/**
	* CUDA graphics interop map flags		* CUDA graphics interop map flags
	*/		*/
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	enum cudaGraphicsMapFlags		enum cudaGraphicsMapFlags
	{		{
	cudaGraphicsMapFlagsNone = 0, /**< Default; Assume resource can be read/written */		cudaGraphicsMapFlagsNone = 0, /**< Default; Assume resource can be read/written */
	cudaGraphicsMapFlagsReadOnly = 1, /**< CUDA will not write to this r esource */		cudaGraphicsMapFlagsReadOnly = 1, /**< CUDA will not write to this r esource */
	skipping to change at line 599		skipping to change at line 707
	enum cudaGraphicsCubeFace {		enum cudaGraphicsCubeFace {
	cudaGraphicsCubeFacePositiveX = 0x00, /**< Positive X face of cubemap */		cudaGraphicsCubeFacePositiveX = 0x00, /**< Positive X face of cubemap */
	cudaGraphicsCubeFaceNegativeX = 0x01, /**< Negative X face of cubemap */		cudaGraphicsCubeFaceNegativeX = 0x01, /**< Negative X face of cubemap */
	cudaGraphicsCubeFacePositiveY = 0x02, /**< Positive Y face of cubemap */		cudaGraphicsCubeFacePositiveY = 0x02, /**< Positive Y face of cubemap */
	cudaGraphicsCubeFaceNegativeY = 0x03, /**< Negative Y face of cubemap */		cudaGraphicsCubeFaceNegativeY = 0x03, /**< Negative Y face of cubemap */
	cudaGraphicsCubeFacePositiveZ = 0x04, /**< Positive Z face of cubemap */		cudaGraphicsCubeFacePositiveZ = 0x04, /**< Positive Z face of cubemap */
	cudaGraphicsCubeFaceNegativeZ = 0x05 /**< Negative Z face of cubemap */		cudaGraphicsCubeFaceNegativeZ = 0x05 /**< Negative Z face of cubemap */
	};		};
	/**		/**
			* CUDA pointer attributes
			*/
			/*DEVICE_BUILTIN*/
			struct cudaPointerAttributes
			{
			/**
			* The physical location of the memory, ::cudaMemoryTypeHost or
			* ::cudaMemoryTypeDevice.
			*/
			enum cudaMemoryType memoryType;
			/**
			* The device against which the memory was allocated or registered.
			* If the memory type is ::cudaMemoryTypeDevice then this identifies
			* the device on which the memory referred physically resides. If
			* the memory type is ::cudaMemoryTypeHost then this identifies the
			* device which was current when the memory was allocated or registered
			* (and if that device is deinitialized then this allocation will vanis
			h
			* with that device's state).
			*/
			int device;
			/**
			* The address which may be dereferenced on the current device to acces
			s
			* the memory or NULL if no such address exists.
			*/
			void *devicePointer;
			/**
			* The address which may be dereferenced on the host to access the
			* memory or NULL if no such address exists.
			*/
			void *hostPointer;
			};
			/**
	* CUDA function attributes		* CUDA function attributes
	*/		*/
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	struct cudaFuncAttributes		struct cudaFuncAttributes
	{		{
	/**		/**
	* The size in bytes of statically-allocated shared memory per block		* The size in bytes of statically-allocated shared memory per block
	* required by this function. This does not include dynamically-allocate d		* required by this function. This does not include dynamically-allocate d
	* shared memory requested by the user at runtime.		* shared memory requested by the user at runtime.
	*/		*/
	skipping to change at line 647		skipping to change at line 791
	* version, so a PTX version 1.3 function would return the value 13.		* version, so a PTX version 1.3 function would return the value 13.
	*/		*/
	int ptxVersion;		int ptxVersion;
	/**		/**
	* The binary architecture version for which the function was compiled.		* The binary architecture version for which the function was compiled.
	* This value is the major binary version * 10 + the minor binary versio n,		* This value is the major binary version * 10 + the minor binary versio n,
	* so a binary version 1.3 function would return the value 13.		* so a binary version 1.3 function would return the value 13.
	*/		*/
	int binaryVersion;		int binaryVersion;
	int __cudaReserved[6];
	};		};
	/**		/**
	* CUDA function cache configurations		* CUDA function cache configurations
	*/		*/
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	enum cudaFuncCache		enum cudaFuncCache
	{		{
	cudaFuncCachePreferNone = 0, /**< Default function cache configurati on, no preference */		cudaFuncCachePreferNone = 0, /**< Default function cache configurati on, no preference */
	cudaFuncCachePreferShared = 1, /**< Prefer larger shared memory and sm aller L1 cache */		cudaFuncCachePreferShared = 1, /**< Prefer larger shared memory and sm aller L1 cache */
	cudaFuncCachePreferL1 = 2 /**< Prefer larger L1 cache and smaller shared memory */		cudaFuncCachePreferL1 = 2 /**< Prefer larger L1 cache and smaller shared memory */
	};		};
	/**		/**
	* CUDA device compute modes		* CUDA device compute modes
	*/		*/
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	enum cudaComputeMode		enum cudaComputeMode
	{		{
	cudaComputeModeDefault = 0, /**< Default compute mode (Multiple thr		cudaComputeModeDefault = 0, /**< Default compute mode (Multiple
	eads can use ::cudaSetDevice() with this device) */		threads can use ::cudaSetDevice() with this device) */
	cudaComputeModeExclusive = 1, /**< Compute-exclusive mode (Only one t		cudaComputeModeExclusive = 1, /**< Compute-exclusive-thread mode
	hread will be able to use ::cudaSetDevice() with this device) */		(Only one thread in one process will be able to use ::cudaSetDevice() with
	cudaComputeModeProhibited = 2 /**< Compute-prohibited mode (No thread		this device) */
	s can use ::cudaSetDevice() with this device) */		cudaComputeModeProhibited = 2, /**< Compute-prohibited mode (No th
			reads can use ::cudaSetDevice() with this device) */
			cudaComputeModeExclusiveProcess = 3 /**< Compute-exclusive-process mode
			(Many threads in one process will be able to use ::cudaSetDevice() with th
			is device) */
	};		};
	/**		/**
	* CUDA Limits		* CUDA Limits
	*/		*/
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	enum cudaLimit		enum cudaLimit
	{		{
	cudaLimitStackSize = 0x00, /**< GPU thread stack size */		cudaLimitStackSize = 0x00, /**< GPU thread stack size */
	cudaLimitPrintfFifoSize = 0x01, /**< GPU printf FIFO size */		cudaLimitPrintfFifoSize = 0x01, /**< GPU printf FIFO size */
	cudaLimitMallocHeapSize = 0x02 /**< GPU malloc heap size */		cudaLimitMallocHeapSize = 0x02 /**< GPU malloc heap size */
	};		};
	/**		/**
			* CUDA Profiler Output modes
			*/
			/*DEVICE_BUILTIN*/
			enum cudaOutputMode
			{
			cudaKeyValuePair = 0x00, /**< Output mode Key-Value pair format. */
			cudaCSV = 0x01 /**< Output mode Comma separated values for
			mat. */
			};
			/**
	* CUDA device properties		* CUDA device properties
	*/		*/
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	struct cudaDeviceProp		struct cudaDeviceProp
	{		{
	char name[256]; /**< ASCII string identifying device */		char name[256]; /**< ASCII string identifying device *
	size_t totalGlobalMem; /**< Global memory available on device		/
	in bytes */		size_t totalGlobalMem; /**< Global memory available on device
	size_t sharedMemPerBlock; /**< Shared memory available per block		in bytes */
	in bytes */		size_t sharedMemPerBlock; /**< Shared memory available per block
	int regsPerBlock; /**< 32-bit registers available per blo		in bytes */
	ck */		int regsPerBlock; /**< 32-bit registers available per bl
	int warpSize; /**< Warp size in threads */		ock */
	size_t memPitch; /**< Maximum pitch in bytes allowed by		int warpSize; /**< Warp size in threads */
	memory copies */		size_t memPitch; /**< Maximum pitch in bytes allowed by
	int maxThreadsPerBlock; /**< Maximum number of threads per bloc		memory copies */
	k */		int maxThreadsPerBlock; /**< Maximum number of threads per blo
	int maxThreadsDim[3]; /**< Maximum size of each dimension of		ck */
	a block */		int maxThreadsDim[3]; /**< Maximum size of each dimension of
	int maxGridSize[3]; /**< Maximum size of each dimension of		a block */
	a grid */		int maxGridSize[3]; /**< Maximum size of each dimension of
	int clockRate; /**< Clock frequency in kilohertz */		a grid */
	size_t totalConstMem; /**< Constant memory available on devic		int clockRate; /**< Clock frequency in kilohertz */
	e in bytes */		size_t totalConstMem; /**< Constant memory available on devi
	int major; /**< Major compute capability */		ce in bytes */
	int minor; /**< Minor compute capability */		int major; /**< Major compute capability */
	size_t textureAlignment; /**< Alignment requirement for textures		int minor; /**< Minor compute capability */
	*/		size_t textureAlignment; /**< Alignment requirement for texture
	int deviceOverlap; /**< Device can concurrently copy memor		s */
	y and execute a kernel */		int deviceOverlap; /**< Device can concurrently copy memo
	int multiProcessorCount; /**< Number of multiprocessors on devic		ry and execute a kernel. Deprecated. Use instead asyncEngineCount. */
	e */		int multiProcessorCount; /**< Number of multiprocessors on devi
	int kernelExecTimeoutEnabled; /**< Specified whether there is a run t		ce */
	ime limit on kernels */		int kernelExecTimeoutEnabled; /**< Specified whether there is a run
	int integrated; /**< Device is integrated as opposed to		time limit on kernels */
	discrete */		int integrated; /**< Device is integrated as opposed t
	int canMapHostMemory; /**< Device can map host memory with cu		o discrete */
	daHostAlloc/cudaHostGetDevicePointer */		int canMapHostMemory; /**< Device can map host memory with c
	int computeMode; /**< Compute mode (See ::cudaComputeMod		udaHostAlloc/cudaHostGetDevicePointer */
	e) */		int computeMode; /**< Compute mode (See ::cudaComputeMo
	int maxTexture1D; /**< Maximum 1D texture size */		de) */
	int maxTexture2D[2]; /**< Maximum 2D texture dimensions */		int maxTexture1D; /**< Maximum 1D texture size */
	int maxTexture3D[3]; /**< Maximum 3D texture dimensions */		int maxTexture2D[2]; /**< Maximum 2D texture dimensions */
	int maxTexture2DArray[3]; /**< Maximum 2D texture array dimension		int maxTexture3D[3]; /**< Maximum 3D texture dimensions */
	s */		int maxTexture1DLayered[2]; /**< Maximum 1D layered texture dimens
	size_t surfaceAlignment; /**< Alignment requirements for surface		ions */
	s */		int maxTexture2DLayered[3]; /**< Maximum 2D layered texture dimens
	int concurrentKernels; /**< Device can possibly execute multip		ions */
	le kernels concurrently */		size_t surfaceAlignment; /**< Alignment requirements for surfac
	int ECCEnabled; /**< Device has ECC support enabled */		es */
	int pciBusID; /**< PCI bus ID of the device */		int concurrentKernels; /**< Device can possibly execute multi
	int pciDeviceID; /**< PCI device ID of the device */		ple kernels concurrently */
	int tccDriver; /**< 1 if device is a Tesla device usin		int ECCEnabled; /**< Device has ECC support enabled */
	g TCC driver, 0 otherwise */		int pciBusID; /**< PCI bus ID of the device */
	int __cudaReserved[21];		int pciDeviceID; /**< PCI device ID of the device */
			int pciDomainID; /**< PCI domain ID of the device */
			int tccDriver; /**< 1 if device is a Tesla device usi
			ng TCC driver, 0 otherwise */
			int asyncEngineCount; /**< Number of asynchronous engines */
			int unifiedAddressing; /**< Device shares a unified address s
			pace with the host */
			int memoryClockRate; /**< Peak memory clock frequency in ki
			lohertz */
			int memoryBusWidth; /**< Global memory bus width in bits *
			/
			int l2CacheSize; /**< Size of L2 cache in bytes */
			int maxThreadsPerMultiProcessor;/**< Maximum resident threads per mult
			iprocessor */
	};		};
	#define cudaDevicePropDontCare \		#define cudaDevicePropDontCare \
	{ \		{ \
	{'\0'}, /* char name[256]; */ \		{'\0'}, /* char name[256]; */ \
	0, /* size_t totalGlobalMem; */ \		0, /* size_t totalGlobalMem; */ \
	0, /* size_t sharedMemPerBlock; */ \		0, /* size_t sharedMemPerBlock; */ \
	0, /* int regsPerBlock; */ \		0, /* int regsPerBlock; */ \
	0, /* int warpSize; */ \		0, /* int warpSize; */ \
	0, /* size_t memPitch; */ \		0, /* size_t memPitch; */ \
	skipping to change at line 748		skipping to change at line 908
	0, /* size_t textureAlignment; */ \		0, /* size_t textureAlignment; */ \
	-1, /* int deviceOverlap; */ \		-1, /* int deviceOverlap; */ \
	0, /* int multiProcessorCount; */ \		0, /* int multiProcessorCount; */ \
	0, /* int kernelExecTimeoutEnabled */ \		0, /* int kernelExecTimeoutEnabled */ \
	0, /* int integrated */ \		0, /* int integrated */ \
	0, /* int canMapHostMemory */ \		0, /* int canMapHostMemory */ \
	0, /* int computeMode */ \		0, /* int computeMode */ \
	0, /* int maxTexture1D */ \		0, /* int maxTexture1D */ \
	{0, 0}, /* int maxTexture2D[2] */ \		{0, 0}, /* int maxTexture2D[2] */ \
	{0, 0, 0}, /* int maxTexture3D[3] */ \		{0, 0, 0}, /* int maxTexture3D[3] */ \
	{0, 0, 0}, /* int maxTexture2DArray[3] */ \		{0, 0}, /* int maxTexture1DLayered[2] */ \
			{0, 0, 0}, /* int maxTexture2DLayered[3] */ \
	0, /* size_t surfaceAlignment */ \		0, /* size_t surfaceAlignment */ \
	0, /* int concurrentKernels */ \		0, /* int concurrentKernels */ \
	0, /* int ECCEnabled */ \		0, /* int ECCEnabled */ \
	0, /* int pciBusID */ \		0, /* int pciBusID */ \
	0, /* int pciDeviceID */ \		0, /* int pciDeviceID */ \
	0 /* int tccDriver */ \		0, /* int pciDomainID */ \
			0, /* int tccDriver */ \
			0, /* int asyncEngineCount */ \
			0, /* int unifiedAddressing */ \
			0, /* int memoryClockRate */ \
			0, /* int memoryBusWidth */ \
			0, /* int l2CacheSize */ \
			0 /* int maxThreadsPerMultiProcessor */ \
	} /**< Empty device properties */		} /**< Empty device properties */
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* SHORTHAND TYPE DEFINITION USED BY RUNTIME API *		* SHORTHAND TYPE DEFINITION USED BY RUNTIME API *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	/**		/**
	* CUDA Error types		* CUDA Error types
	skipping to change at line 793		skipping to change at line 961
	*/		*/
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	typedef struct cudaGraphicsResource *cudaGraphicsResource_t;		typedef struct cudaGraphicsResource *cudaGraphicsResource_t;
	/**		/**
	* CUDA UUID types		* CUDA UUID types
	*/		*/
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	typedef struct CUuuid_st cudaUUID_t;		typedef struct CUuuid_st cudaUUID_t;
			/**
			* CUDA output file modes
			*/
			/*DEVICE_BUILTIN*/
			typedef enum cudaOutputMode cudaOutputMode_t;
	/** @} */		/** @} */
	/** @} */ /* END CUDART_TYPES */		/** @} */ /* END CUDART_TYPES */
	#endif /* !__DRIVER_TYPES_H__ */		#endif /* !__DRIVER_TYPES_H__ */
End of changes. 26 change blocks.
	126 lines changed or deleted		330 lines changed or added

	func_macro.h		func_macro.h
	skipping to change at line 54		skipping to change at line 54
	___device__(static) decl		___device__(static) decl
	#else /* __CUDABE__ */		#else /* __CUDABE__ */
	#if !defined(__CUDA_INTERNAL_COMPILATION__)		#if !defined(__CUDA_INTERNAL_COMPILATION__)
	#error -- incorrect inclusion of a cudart header file		#error -- incorrect inclusion of a cudart header file
	#endif /* !__CUDA_INTERNAL_COMPILATION__ */		#endif /* !__CUDA_INTERNAL_COMPILATION__ */
	#if defined(__APPLE__)
	#define __func__(decl) \		#define __func__(decl) \
	extern __attribute__((__weak_import__, __weak__)) decl; decl		inline decl
	#define __device_func__(decl) \
	static __attribute__((__unused__)) decl
	#elif defined(__GNUC__)		#if defined(__GNUC__)
	#define __func__(decl) \
	extern __attribute__((__weak__)) decl; decl
	#define __device_func__(decl) \		#define __device_func__(decl) \
	static __attribute__((__unused__)) decl		static __attribute__((__unused__)) decl
	#elif defined(_WIN32)		#elif defined(_WIN32)
	#define __func__(decl) \
	static decl
	#define __device_func__(decl) \		#define __device_func__(decl) \
	static decl		static decl
	#endif /* __APPLE__ */		#endif /* __GNUC__ */
	#endif /* CUDABE */		#endif /* CUDABE */
	#endif /* __FUNC_MACRO_H__ */		#endif /* __FUNC_MACRO_H__ */
End of changes. 6 change blocks.
	11 lines changed or deleted		3 lines changed or added

	host_config.h		host_config.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(__HOST_CONFIG_H__)		#if !defined(__HOST_CONFIG_H__)
	#define __HOST_CONFIG_H__		#define __HOST_CONFIG_H__
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	#if defined(__CUDACC__)		#if defined(__CUDACC__)
			/* check for host compilers that are compatible with nvcc */
			#if !defined(__GNUC__) && !defined(_WIN32)
			#error --- !!! UNSUPPORTED COMPILER !!! ---
			#endif /* !__GNUC__ && !_WIN32 */
			#if defined(__ICC)
			#if __ICC != 1110 || !defined(__GNUC__) || !defined(__LP64__)
			//#error -- unsupported ICC configuration! Only ICC 11.1 on Linux x86_64 is
			supported!
			#endif /* __ICC != 1110 || !__GNUC__ || !__LP64__ */
			#endif /* __ICC */
			#if defined(__GNUC__)
			#if __GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ > 4)
			#error -- unsupported GNU version! gcc 4.5 and up are not supported!
			#endif /* __GNUC__> 4 || (__GNUC__ == 4 && __GNUC_MINOR__ > 4) */
			#endif /* __GNUC__ */
			#if defined(_WIN32)
			#if _MSC_VER < 1400 || _MSC_VER > 1600
			#error -- unsupported MSVC version! Only MSVC 8.0, 9.0, and 10.0 are suppor
			ted!
			#endif /* _MSC_VER < 1400 || _MSC_VER > 1500 */
			#endif /* _WIN32 */
			/* configure host compiler */
	#if defined(__APPLE__)		#if defined(__APPLE__)
	#define _CRTIMP		#define _CRTIMP
	#define __THROW		#define __THROW
	#if defined(__BLOCKS__) /* nvcc does not support closures */		#if defined(__BLOCKS__) /* nvcc does not support closures */
	#undef __BLOCKS__		#undef __BLOCKS__
	#endif /* __BLOCKS__ */		#endif /* __BLOCKS__ */
	#elif defined(__GNUC__)		#elif defined(__GNUC__)
	#define _CRTIMP		#define _CRTIMP
	#include <features.h> /* for __THROW */		#include <features.h> /* for __THROW */
	#elif defined(_WIN32)		#elif defined(_WIN32)
	#if _MSC_VER >= 1400
	#if _MSC_VER >= 1500		#if _MSC_VER >= 1500
	#undef _USE_DECLSPECS_FOR_SAL		#undef _USE_DECLSPECS_FOR_SAL
	#define _USE_DECLSPECS_FOR_SAL \		#define _USE_DECLSPECS_FOR_SAL \
	1		1
	#endif /* _MSC_VER >= 1500 */		#endif /* _MSC_VER >= 1500 */
	#if !defined(_CRT_NONSTDC_NO_WARNINGS)		#if !defined(_CRT_NONSTDC_NO_WARNINGS)
	#define _CRT_NONSTDC_NO_WARNINGS /* to suppress warnings */		#define _CRT_NONSTDC_NO_WARNINGS /* to suppress warnings */
	#endif /* !_CRT_NONSTDC_NO_WARNINGS */		#endif /* !_CRT_NONSTDC_NO_WARNINGS */
	#if !defined(_CRT_SECURE_NO_WARNINGS)		#if !defined(_CRT_SECURE_NO_WARNINGS)
	#define _CRT_SECURE_NO_WARNINGS /* to suppress warnings */		#define _CRT_SECURE_NO_WARNINGS /* to suppress warnings */
	#endif /* !_CRT_SECURE_NO_WARNINGS */		#endif /* !_CRT_SECURE_NO_WARNINGS */
	#endif /* _MSC_VER >= 1400 */
	#if !defined(NOMINMAX)		#if !defined(NOMINMAX)
	#define NOMINMAX /* min and max are part of cuda runtime */		#define NOMINMAX /* min and max are part of cuda runtime */
	#endif /* !NOMINMAX */		#endif /* !NOMINMAX */
	#include <crtdefs.h> /* for _CRTIMP */		#include <crtdefs.h> /* for _CRTIMP */
	#define __THROW		#define __THROW
	#endif /* __APPLE__ */		#endif /* __APPLE__ */
	#endif /* __CUDACC__ */		#endif /* __CUDACC__ */
	#if defined(__ICC)
	#if __ICC != 1110 || !defined(__GNUC__) || !defined(__LP64__)
	#error -- unsupported ICC configuration! Only ICC 11.1 on Linux x86_64 is s
	upported!
	#endif /* __ICC != 1110 || !__GNUC__ || !__LP64__ */
	#endif /* __ICC */
	#endif /* !__HOST_CONFIG_H__ */		#endif /* !__HOST_CONFIG_H__ */
End of changes. 10 change blocks.
	43 lines changed or deleted		81 lines changed or added

	host_defines.h		host_defines.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(__HOST_DEFINES_H__)		#if !defined(__HOST_DEFINES_H__)
	#define __HOST_DEFINES_H__		#define __HOST_DEFINES_H__
	#if !defined(__GNUC__) && !defined(_WIN32)		#if defined(__GNUC__)
	#error --- !!! UNSUPPORTED COMPILER !!! ---
	#elif defined(__GNUC__)
	#define __no_return__ \		#define __no_return__ \
	__attribute__((noreturn))		__attribute__((noreturn))
	#define __noinline__ \		#define __noinline__ \
	__attribute__((noinline))		__attribute__((noinline))
	#define __forceinline__ \		#define __forceinline__ \
	__inline__ __attribute__((always_inline))		__inline__ __attribute__((always_inline))
	#define __align__(n) \		#define __align__(n) \
	__attribute__((aligned(n)))		__attribute__((aligned(n)))
	#define __thread__ \		#define __thread__ \
	skipping to change at line 100		skipping to change at line 110
	__declspec(dllimport)		__declspec(dllimport)
	#define __export__ \		#define __export__ \
	__declspec(dllexport)		__declspec(dllexport)
	#define __annotate__(a) \		#define __annotate__(a) \
	__declspec(a)		__declspec(a)
	#define __location__(a) \		#define __location__(a) \
	__annotate__(__##a##__)		__annotate__(__##a##__)
	#define CUDARTAPI \		#define CUDARTAPI \
	__stdcall		__stdcall
	#endif /* !__GNUC__ && !_WIN32 */		#else /* __GNUC__ */
			#define __inline__
			#if !defined(__align__)
			#error --- !!! UNKNOWN COMPILER: please provide a CUDA compatible definitio
			n for '__align__' !!! ---
			#endif /* !__align__ */
			#if !defined(CUDARTAPI)
			#error --- !!! UNKNOWN COMPILER: please provide a CUDA compatible definitio
			n for 'CUDARTAPI' !!! ---
			#endif /* !CUDARTAPI */
			#endif /* !__GNUC__ */
	#if !defined(__GNUC__) || __GNUC__ < 4 || (__GNUC__ == 4 && __GNUC_MINOR__ < 3)		#if !defined(__GNUC__) || __GNUC__ < 4 || (__GNUC__ == 4 && __GNUC_MINOR__ < 3)
	#define __specialization_static \		#define __specialization_static \
	static		static
	#else /* !__GNUC__ || __GNUC__ < 4 || (__GNUC__ == 4 && __GNUC_MINOR__ < 3) */		#else /* !__GNUC__ || __GNUC__ < 4 || (__GNUC__ == 4 && __GNUC_MINOR__ < 3) */
	#define __specialization_static		#define __specialization_static
	skipping to change at line 137		skipping to change at line 163
	#define __builtin_align__(a) \		#define __builtin_align__(a) \
	__align__(a)		__align__(a)
	#else /* __CUDACC__ || __CUDABE__ || __GNUC__ || _WIN64 */		#else /* __CUDACC__ || __CUDABE__ || __GNUC__ || _WIN64 */
	#define __builtin_align__(a)		#define __builtin_align__(a)
	#endif /* __CUDACC__ || __CUDABE__ || __GNUC__ || _WIN64 */		#endif /* __CUDACC__ || __CUDABE__ || __GNUC__ || _WIN64 */
	#define __device__ \
	__location__(device)
	#define __host__ \		#define __host__ \
	__location__(host)		__location__(host)
			#define __device__ \
			__location__(device)
	#define __global__ \		#define __global__ \
	__location__(global)		__location__(global)
	#define __shared__ \		#define __shared__ \
	__location__(shared)		__location__(shared)
	#define __constant__ \		#define __constant__ \
	__location__(constant)		__location__(constant)
	#endif /* !__HOST_DEFINES_H__ */		#endif /* !__HOST_DEFINES_H__ */
End of changes. 10 change blocks.
	36 lines changed or deleted		63 lines changed or added

	host_runtime.h		host_runtime.h
	skipping to change at line 74		skipping to change at line 74
	#define __pad__(f)		#define __pad__(f)
	#endif /* _WIN32 && !_WIN64 */		#endif /* _WIN32 && !_WIN64 */
	#include "builtin_types.h"		#include "builtin_types.h"
	#include "storage_class.h"		#include "storage_class.h"
	#else /* !__CUDA_INTERNAL_COMPILATION__ */		#else /* !__CUDA_INTERNAL_COMPILATION__ */
			template <typename T>
			static inline void *__cudaAddressOf(T &val)
			{
			return (void *)(&(const_cast<char &>(reinterpret_cast<const volatile ch
			ar &>(val))));
			}
	#define __cudaRegisterBinary() \		#define __cudaRegisterBinary() \
	__cudaFatCubinHandle = __cudaRegisterFatBinary((void*)&__fatDeviceT ext); \		__cudaFatCubinHandle = __cudaRegisterFatBinary((void*)&__fatDeviceT ext); \
	atexit(__cudaUnregisterBinaryUtil)		atexit(__cudaUnregisterBinaryUtil)
	#define __cudaRegisterVariable(var, ext, size, constant, global) \		#define __cudaRegisterVariable(var, ext, size, constant, global) \
	__cudaRegisterVar(__cudaFatCubinHandle, (char*)&__host##var, (char* )__device##var, __name##var, ext, size, constant, global)		__cudaRegisterVar(__cudaFatCubinHandle, (char*)&__host##var, (char* )__device##var, __name##var, ext, size, constant, global)
	#define __cudaRegisterGlobalTexture(tex, dim, norm, ext) \		#define __cudaRegisterGlobalTexture(tex, dim, norm, ext) \
	__cudaRegisterTexture(__cudaFatCubinHandle, (const struct textureRe ference*)&tex, (const void**)__device##tex, __name##tex, dim, norm, ext)		__cudaRegisterTexture(__cudaFatCubinHandle, (const struct textureRe ference*)&tex, (const void**)__device##tex, __name##tex, dim, norm, ext)
	#define __cudaRegisterGlobalSurface(surf, dim, ext) \		#define __cudaRegisterGlobalSurface(surf, dim, ext) \
	__cudaRegisterSurface(__cudaFatCubinHandle, (const struct surfaceRe ference*)&surf, (const void**)__device##surf, __name##surf, dim, ext)		__cudaRegisterSurface(__cudaFatCubinHandle, (const struct surfaceRe ference*)&surf, (const void**)__device##surf, __name##surf, dim, ext)
	#define __cudaRegisterEntry(funptr, fun, thread_limit) \		#define __cudaRegisterEntry(funptr, fun, thread_limit) \
	skipping to change at line 85		skipping to change at line 91
	__cudaFatCubinHandle = __cudaRegisterFatBinary((void*)&__fatDeviceT ext); \		__cudaFatCubinHandle = __cudaRegisterFatBinary((void*)&__fatDeviceT ext); \
	atexit(__cudaUnregisterBinaryUtil)		atexit(__cudaUnregisterBinaryUtil)
	#define __cudaRegisterVariable(var, ext, size, constant, global) \		#define __cudaRegisterVariable(var, ext, size, constant, global) \
	__cudaRegisterVar(__cudaFatCubinHandle, (char*)&__host##var, (char* )__device##var, __name##var, ext, size, constant, global)		__cudaRegisterVar(__cudaFatCubinHandle, (char*)&__host##var, (char* )__device##var, __name##var, ext, size, constant, global)
	#define __cudaRegisterGlobalTexture(tex, dim, norm, ext) \		#define __cudaRegisterGlobalTexture(tex, dim, norm, ext) \
	__cudaRegisterTexture(__cudaFatCubinHandle, (const struct textureRe ference*)&tex, (const void**)__device##tex, __name##tex, dim, norm, ext)		__cudaRegisterTexture(__cudaFatCubinHandle, (const struct textureRe ference*)&tex, (const void**)__device##tex, __name##tex, dim, norm, ext)
	#define __cudaRegisterGlobalSurface(surf, dim, ext) \		#define __cudaRegisterGlobalSurface(surf, dim, ext) \
	__cudaRegisterSurface(__cudaFatCubinHandle, (const struct surfaceRe ference*)&surf, (const void**)__device##surf, __name##surf, dim, ext)		__cudaRegisterSurface(__cudaFatCubinHandle, (const struct surfaceRe ference*)&surf, (const void**)__device##surf, __name##surf, dim, ext)
	#define __cudaRegisterEntry(funptr, fun, thread_limit) \		#define __cudaRegisterEntry(funptr, fun, thread_limit) \
	__cudaRegisterFunction(__cudaFatCubinHandle, (const char*)funptr, ( char*)__device_fun(fun), #fun, -1, (uint3*)0, (uint3*)0, (dim3*)0, (dim3*)0 , (int*)0)		__cudaRegisterFunction(__cudaFatCubinHandle, (const char*)funptr, ( char*)__device_fun(fun), #fun, -1, (uint3*)0, (uint3*)0, (dim3*)0, (dim3*)0 , (int*)0)
	#define __cudaSetupArg(arg, offset) \		#define __cudaSetupArg(arg, offset) \
	if (cudaSetupArgument((void*)(char*)&arg, sizeof(arg), (size_t)&off set->arg) != cudaSuccess) \		if (cudaSetupArgument(__cudaAddressOf(arg), sizeof(arg), (size_t)of fset) != cudaSuccess) \
	return		return
			#define __cudaSetupArgSimple(arg, offset) \
			if (cudaSetupArgument((void *)(char *)&arg, sizeof(arg), (size_t)of
			fset) != cudaSuccess) \
			return
	#define __cudaLaunch(fun) \		#define __cudaLaunch(fun) \
	{ volatile static char *__f; __f = fun; (void)cudaLaunch(fun); }		{ volatile static char *__f; __f = fun; (void)cudaLaunch(fun); }
	extern "C" {		extern "C" {
	extern void** CUDARTAPI __cudaRegisterFatBinary(		extern void** CUDARTAPI __cudaRegisterFatBinary(
	void *fatCubin		void *fatCubin
	);		);
	extern void CUDARTAPI __cudaUnregisterFatBinary(		extern void CUDARTAPI __cudaUnregisterFatBinary(
End of changes. 4 change blocks.
	1 lines changed or deleted		15 lines changed or added

	math_constants.h		math_constants.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(__MATH_CONSTANTS_H__)		#if !defined(__MATH_CONSTANTS_H__)
	#define __MATH_CONSTANTS_H__		#define __MATH_CONSTANTS_H__
	/* single precision constants */		/* single precision constants */
	#define CUDART_INF_F __int_as_float(0x7f800000)		#define CUDART_INF_F __int_as_float(0x7f800000)
	#define CUDART_NAN_F __int_as_float(0x7fffffff)		#define CUDART_NAN_F __int_as_float(0x7fffffff)
	#define CUDART_MIN_DENORM_F __int_as_float(0x00000001)		#define CUDART_MIN_DENORM_F __int_as_float(0x00000001)
	#define CUDART_MAX_NORMAL_F __int_as_float(0x7f7fffff)		#define CUDART_MAX_NORMAL_F __int_as_float(0x7f7fffff)
	skipping to change at line 137		skipping to change at line 151
	#define CUDART_LNT_HI 2.3025850929940459e+0		#define CUDART_LNT_HI 2.3025850929940459e+0
	#define CUDART_LNT_LO (-2.1707562233822494e-16)		#define CUDART_LNT_LO (-2.1707562233822494e-16)
	#define CUDART_LNPI 1.1447298858494002e+0		#define CUDART_LNPI 1.1447298858494002e+0
	#define CUDART_LN2_X_1024 7.0978271289338397e+2		#define CUDART_LN2_X_1024 7.0978271289338397e+2
	#define CUDART_LN2_X_1025 7.1047586007394398e+2		#define CUDART_LN2_X_1025 7.1047586007394398e+2
	#define CUDART_LN2_X_1075 7.4513321910194122e+2		#define CUDART_LN2_X_1075 7.4513321910194122e+2
	#define CUDART_LG2_X_1024 3.0825471555991675e+2		#define CUDART_LG2_X_1024 3.0825471555991675e+2
	#define CUDART_LG2_X_1075 3.2360724533877976e+2		#define CUDART_LG2_X_1075 3.2360724533877976e+2
	#define CUDART_TWO_TO_23 8388608.0		#define CUDART_TWO_TO_23 8388608.0
	#define CUDART_TWO_TO_52 4503599627370496.0		#define CUDART_TWO_TO_52 4503599627370496.0
			#define CUDART_TWO_TO_53 9007199254740992.0
	#define CUDART_TWO_TO_54 18014398509481984.0		#define CUDART_TWO_TO_54 18014398509481984.0
	#define CUDART_TWO_TO_M54 5.5511151231257827e-17		#define CUDART_TWO_TO_M54 5.5511151231257827e-17
	#define CUDART_TWO_TO_M1022 2.22507385850720140e-308		#define CUDART_TWO_TO_M1022 2.22507385850720140e-308
	#define CUDART_TRIG_PLOSS 2147483648.0		#define CUDART_TRIG_PLOSS 2147483648.0
	#endif /* !__MATH_CONSTANTS_H__ */		#endif /* !__MATH_CONSTANTS_H__ */
End of changes. 7 change blocks.
	28 lines changed or deleted		42 lines changed or added

	math_functions.h		math_functions.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(__MATH_FUNCTIONS_H__)		#if !defined(__MATH_FUNCTIONS_H__)
	#define __MATH_FUNCTIONS_H__		#define __MATH_FUNCTIONS_H__
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	skipping to change at line 56		skipping to change at line 70
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	#include "builtin_types.h"		#include "builtin_types.h"
	#include "host_defines.h"		#include "host_defines.h"
			#include <math.h>
			#include <stdlib.h>
			#include <cmath>
			#include <cstdlib>
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	extern "C"		extern "C"
	{		{
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	skipping to change at line 104		skipping to change at line 123
	extern __host__ __device__ long long int llmax(long long int, long long int);		extern __host__ __device__ long long int llmax(long long int, long long int);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ unsigned long long int ullmax(unsigned long long int, unsigned long long int);		extern __host__ __device__ unsigned long long int ullmax(unsigned long long int, unsigned long long int);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ float fmaxf(float, float) __THR OW;		extern __host__ __device__ float fmaxf(float, float) __THR OW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double fmax(double, double) __TH ROW;		extern __host__ __device__ double fmax(double, double) __TH ROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double __cdecl sin(double) __THROW;		extern __host__ __device__ double __cdecl sin(double) __THROW;
	/*DEVICE_BUILTIN*/
	extern __host__ __device__ float sinf(float) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double __cdecl cos(double) __THROW;		extern __host__ __device__ double __cdecl cos(double) __THROW;
	/*DEVICE_BUILTIN*/
	extern __host__ __device__ float cosf(float) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ void sincos(double, double*, d ouble*) __THROW;		extern __host__ __device__ void sincos(double, double*, d ouble*) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ void sincosf(float, float*, fl oat*) __THROW;		extern __host__ __device__ void sincosf(float, float*, fl oat*) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double __cdecl tan(double) __THROW;		extern __host__ __device__ double __cdecl tan(double) __THROW;
	/*DEVICE_BUILTIN*/
	extern __host__ __device__ float tanf(float) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double __cdecl sqrt(double) __THROW;		extern __host__ __device__ double __cdecl sqrt(double) __THROW;
	/*DEVICE_BUILTIN*/
	extern __host__ __device__ float sqrtf(float) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double rsqrt(double);		extern __host__ __device__ double rsqrt(double);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ float rsqrtf(float);		extern __host__ __device__ float rsqrtf(float);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double exp2(double) __THROW;		extern __host__ __device__ double exp2(double) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ float exp2f(float) __THROW;		extern __host__ __device__ float exp2f(float) __THROW;
	skipping to change at line 154		skipping to change at line 165
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ float expm1f(float) __THROW;		extern __host__ __device__ float expm1f(float) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double log2(double) __THROW;		extern __host__ __device__ double log2(double) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ float log2f(float) __THROW;		extern __host__ __device__ float log2f(float) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double __cdecl log10(double) __THROW;		extern __host__ __device__ double __cdecl log10(double) __THROW;
	/*DEVICE_BUILTIN*/
	extern __host__ __device__ float log10f(float) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double __cdecl log(double) __THROW;		extern __host__ __device__ double __cdecl log(double) __THROW;
	/*DEVICE_BUILTIN*/
	extern __host__ __device__ float logf(float) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double log1p(double) __THROW;		extern __host__ __device__ double log1p(double) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ float log1pf(float) __THROW;		extern __host__ __device__ float log1pf(float) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double __cdecl floor(double) __THROW;		extern __host__ __device__ _CRTIMP double __cdecl floor(double) __THROW;
	/*DEVICE_BUILTIN*/
	extern __host__ __device__ float floorf(float) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double __cdecl exp(double) __THROW;		extern __host__ __device__ double __cdecl exp(double) __THROW;
	/*DEVICE_BUILTIN*/
	extern __host__ __device__ float expf(float) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double __cdecl cosh(double) __THROW;		extern __host__ __device__ double __cdecl cosh(double) __THROW;
	/*DEVICE_BUILTIN*/
	extern __host__ __device__ float coshf(float) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double __cdecl sinh(double) __THROW;		extern __host__ __device__ double __cdecl sinh(double) __THROW;
	/*DEVICE_BUILTIN*/
	extern __host__ __device__ float sinhf(float) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double __cdecl tanh(double) __THROW;		extern __host__ __device__ double __cdecl tanh(double) __THROW;
	/*DEVICE_BUILTIN*/
	extern __host__ __device__ float tanhf(float) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double acosh(double) __THROW;		extern __host__ __device__ double acosh(double) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ float acoshf(float) __THROW;		extern __host__ __device__ float acoshf(float) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double asinh(double) __THROW;		extern __host__ __device__ double asinh(double) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ float asinhf(float) __THROW;		extern __host__ __device__ float asinhf(float) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double atanh(double) __THROW;		extern __host__ __device__ double atanh(double) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ float atanhf(float) __THROW;		extern __host__ __device__ float atanhf(float) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double __cdecl ldexp(double, int) __THRO W;		extern __host__ __device__ _CRTIMP double __cdecl ldexp(double, int) __THRO W;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ float ldexpf(float, int) __THRO W;		extern __host__ __device__ float ldexpf(float, int) __THRO W;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double logb(double) __THROW;		extern __host__ __device__ double logb(double) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ float logbf(float) __THROW;		extern __host__ __device__ float logbf(float) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ int ilogb(double) __THROW;		extern __host__ __device__ int ilogb(double) __THROW;
	skipping to change at line 233		skipping to change at line 230
	extern __host__ __device__ double scalbn(double, int) __THR OW;		extern __host__ __device__ double scalbn(double, int) __THR OW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ float scalbnf(float, int) __THR OW;		extern __host__ __device__ float scalbnf(float, int) __THR OW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double scalbln(double, long int) __THROW;		extern __host__ __device__ double scalbln(double, long int) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ float scalblnf(float, long int) __THROW;		extern __host__ __device__ float scalblnf(float, long int) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double __cdecl frexp(double, int*) __THR OW;		extern __host__ __device__ _CRTIMP double __cdecl frexp(double, int*) __THR OW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ float frexpf(float, int*) __THR OW;		extern __host__ __device__ float frexpf(float, int*) __THR OW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double round(double) __THROW;		extern __host__ __device__ double round(double) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ float roundf(float) __THROW;		extern __host__ __device__ float roundf(float) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ long int lround(double) __THROW;		extern __host__ __device__ long int lround(double) __THROW;
	skipping to change at line 273		skipping to change at line 270
	extern __host__ __device__ long long int llrint(double) __THROW;		extern __host__ __device__ long long int llrint(double) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ long long int llrintf(float) __THROW;		extern __host__ __device__ long long int llrintf(float) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double nearbyint(double) __THROW ;		extern __host__ __device__ double nearbyint(double) __THROW ;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ float nearbyintf(float) __THROW ;		extern __host__ __device__ float nearbyintf(float) __THROW ;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double __cdecl ceil(double) __THROW;		extern __host__ __device__ _CRTIMP double __cdecl ceil(double) __THROW;
	/*DEVICE_BUILTIN*/
	extern __host__ __device__ float ceilf(float) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double trunc(double) __THROW;		extern __host__ __device__ double trunc(double) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ float truncf(float) __THROW;		extern __host__ __device__ float truncf(float) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double fdim(double, double) __TH ROW;		extern __host__ __device__ double fdim(double, double) __TH ROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ float fdimf(float, float) __THR OW;		extern __host__ __device__ float fdimf(float, float) __THR OW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double __cdecl atan2(double, double) __T HROW;		extern __host__ __device__ double __cdecl atan2(double, double) __T HROW;
	/*DEVICE_BUILTIN*/
	extern __host__ __device__ float atan2f(float, float) __TH
	ROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double __cdecl atan(double) __THROW;		extern __host__ __device__ double __cdecl atan(double) __THROW;
	/*DEVICE_BUILTIN*/
	extern __host__ __device__ float atanf(float) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double __cdecl asin(double) __THROW;		extern __host__ __device__ double __cdecl asin(double) __THROW;
	/*DEVICE_BUILTIN*/
	extern __host__ __device__ float asinf(float) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double __cdecl acos(double) __THROW;		extern __host__ __device__ double __cdecl acos(double) __THROW;
	/*DEVICE_BUILTIN*/
	extern __host__ __device__ float acosf(float) __THROW;
			#if _MSC_VER < 1600
			/*DEVICE_BUILTIN*/
			extern __host__ __device__ _CRTIMP double __cdecl hypot(double, double) __T
			HROW;
			#else
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double __cdecl hypot(double, double) __T HROW;		extern __host__ __device__ double __cdecl hypot(double, double) __T HROW;
			#endif /* _MSC_VER < 1600 */
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ float hypotf(float, float) __TH ROW;		extern __host__ __device__ float hypotf(float, float) __TH ROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double cbrt(double) __THROW;		extern __host__ __device__ double cbrt(double) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ float cbrtf(float) __THROW;		extern __host__ __device__ float cbrtf(float) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double rcbrt(double);		extern __host__ __device__ double rcbrt(double);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ float rcbrtf(float);		extern __host__ __device__ float rcbrtf(float);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double sinpi(double);		extern __host__ __device__ double sinpi(double);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ float sinpif(float);		extern __host__ __device__ float sinpif(float);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double __cdecl pow(double, double) __THR OW;		extern __host__ __device__ double cospi(double);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ float powf(float, float) __THRO W;		extern __host__ __device__ float cospif(float);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double __cdecl modf(double, double*) __T		extern __host__ __device__ double __cdecl pow(double, double) __THR
	HROW;		OW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ float modff(float, float*) __TH ROW;		extern __host__ __device__ _CRTIMP double __cdecl modf(double, double*) __T HROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double __cdecl fmod(double, double) __TH ROW;		extern __host__ __device__ double __cdecl fmod(double, double) __TH ROW;
	/*DEVICE_BUILTIN*/
	extern __host__ __device__ float fmodf(float, float) __THR
	OW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double remainder(double, double) __THROW;		extern __host__ __device__ double remainder(double, double) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ float remainderf(float, float) __THROW;		extern __host__ __device__ float remainderf(float, float) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ double remquo(double, double, in t*) __THROW;		extern __host__ __device__ double remquo(double, double, in t*) __THROW;
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ float remquof(float, float, int *) __THROW;		extern __host__ __device__ float remquof(float, float, int *) __THROW;
	skipping to change at line 460		skipping to change at line 454
	#else /* __APPLE__ */		#else /* __APPLE__ */
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __host__ __device__ int __finitel(long double) __ THROW;		extern __host__ __device__ int __finitel(long double) __ THROW;
	#endif /* __APPLE__ */		#endif /* __APPLE__ */
	#endif /* __GNUC__ */		#endif /* __GNUC__ */
	}		#if defined(_WIN32) && defined(_M_AMD64)
			/*DEVICE_BUILTIN*/
			extern _CRTIMP __host__ __device__ float __cdecl acosf(float) __THROW;
	#include <math.h>		/*DEVICE_BUILTIN*/
	#include <stdlib.h>		extern _CRTIMP __host__ __device__ float __cdecl asinf(float) __THROW;
	#include <cmath>
	#include <cstdlib>		/*DEVICE_BUILTIN*/
			extern _CRTIMP __host__ __device__ float __cdecl atanf(float) __THROW;
			/*DEVICE_BUILTIN*/
			extern _CRTIMP __host__ __device__ float __cdecl atan2f(float, float) __THR
			OW;
			/*DEVICE_BUILTIN*/
			extern _CRTIMP __host__ __device__ float __cdecl cosf(float) __THROW;
			/*DEVICE_BUILTIN*/
			extern _CRTIMP __host__ __device__ float __cdecl sinf(float) __THROW;
			/*DEVICE_BUILTIN*/
			extern _CRTIMP __host__ __device__ float __cdecl tanf(float) __THROW;
			/*DEVICE_BUILTIN*/
			extern _CRTIMP __host__ __device__ float __cdecl coshf(float) __THROW;
			/*DEVICE_BUILTIN*/
			extern _CRTIMP __host__ __device__ float __cdecl sinhf(float) __THROW;
			/*DEVICE_BUILTIN*/
			extern _CRTIMP __host__ __device__ float __cdecl tanhf(float) __THROW;
			/*DEVICE_BUILTIN*/
			extern _CRTIMP __host__ __device__ float __cdecl expf(float) __THROW;
			/*DEVICE_BUILTIN*/
			extern _CRTIMP __host__ __device__ float __cdecl logf(float) __THROW;
			/*DEVICE_BUILTIN*/
			extern _CRTIMP __host__ __device__ float __cdecl log10f(float) __THROW;
			/*DEVICE_BUILTIN*/
			extern _CRTIMP __host__ __device__ float __cdecl modff(float, float*) __THR
			OW;
			/*DEVICE_BUILTIN*/
			extern _CRTIMP __host__ __device__ float __cdecl powf(float, float) __THROW
			;
			/*DEVICE_BUILTIN*/
			extern _CRTIMP __host__ __device__ float __cdecl sqrtf(float) __THROW;
			/*DEVICE_BUILTIN*/
			extern _CRTIMP __host__ __device__ float __cdecl ceilf(float) __THROW;
			/*DEVICE_BUILTIN*/
			extern _CRTIMP __host__ __device__ float __cdecl floorf(float) __THROW;
			/*DEVICE_BUILTIN*/
			extern _CRTIMP __host__ __device__ float __cdecl fmodf(float, float) __THRO
			W;
			#else /* _WIN32 && _M_AMD64 */
			/*DEVICE_BUILTIN*/
			extern __host__ __device__ float acosf(float) __THROW;
			/*DEVICE_BUILTIN*/
			extern __host__ __device__ float asinf(float) __THROW;
			/*DEVICE_BUILTIN*/
			extern __host__ __device__ float atanf(float) __THROW;
			/*DEVICE_BUILTIN*/
			extern __host__ __device__ float atan2f(float, float) __TH
			ROW;
			/*DEVICE_BUILTIN*/
			extern __host__ __device__ float cosf(float) __THROW;
			/*DEVICE_BUILTIN*/
			extern __host__ __device__ float sinf(float) __THROW;
			/*DEVICE_BUILTIN*/
			extern __host__ __device__ float tanf(float) __THROW;
			/*DEVICE_BUILTIN*/
			extern __host__ __device__ float coshf(float) __THROW;
			/*DEVICE_BUILTIN*/
			extern __host__ __device__ float sinhf(float) __THROW;
			/*DEVICE_BUILTIN*/
			extern __host__ __device__ float tanhf(float) __THROW;
			/*DEVICE_BUILTIN*/
			extern __host__ __device__ float expf(float) __THROW;
			/*DEVICE_BUILTIN*/
			extern __host__ __device__ float logf(float) __THROW;
			/*DEVICE_BUILTIN*/
			extern __host__ __device__ float log10f(float) __THROW;
			/*DEVICE_BUILTIN*/
			extern __host__ __device__ float modff(float, float*) __TH
			ROW;
			/*DEVICE_BUILTIN*/
			extern __host__ __device__ float powf(float, float) __THRO
			W;
			/*DEVICE_BUILTIN*/
			extern __host__ __device__ float sqrtf(float) __THROW;
			/*DEVICE_BUILTIN*/
			extern __host__ __device__ float ceilf(float) __THROW;
			/*DEVICE_BUILTIN*/
			extern __host__ __device__ float floorf(float) __THROW;
			/*DEVICE_BUILTIN*/
			extern __host__ __device__ float fmodf(float, float) __THR
			OW;
			#endif /* _WIN32 && _M_AMD64 */
			}
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	#if defined(__GNUC__)		#if defined(__GNUC__)
	#if defined(__APPLE__)		#if defined(__APPLE__)
	skipping to change at line 533		skipping to change at line 640
	using std::acos;		using std::acos;
	using std::cos;		using std::cos;
	using std::cosh;		using std::cosh;
	using std::atan;		using std::atan;
	using std::atan2;		using std::atan2;
	using std::tan;		using std::tan;
	using std::tanh;		using std::tanh;
	#elif defined(_WIN32)		#elif defined(_WIN32)
			#if _MSC_VER < 1600
	static __inline__ __host__ __device__ long long int abs(long long int a)		static __inline__ __host__ __device__ long long int abs(long long int a)
	{		{
	return llabs(a);		return llabs(a);
	}		}
			#endif /* _MSC_VER < 1600 */
	static __inline__ __host__ __device__ int signbit(double a)		static __inline__ __host__ __device__ int signbit(double a)
	{		{
	return __signbit(a);		return __signbit(a);
	}		}
	static __inline__ __host__ __device__ int signbit(float a)		static __inline__ __host__ __device__ int signbit(float a)
	{		{
	return __signbitf(a);		return __signbitf(a);
	}		}
	skipping to change at line 579		skipping to change at line 688
	{		{
	return __finite(a);		return __finite(a);
	}		}
	static __inline__ __host__ __device__ int isfinite(float a)		static __inline__ __host__ __device__ int isfinite(float a)
	{		{
	return __finitef(a);		return __finitef(a);
	}		}
	template<class T> extern __host__ __device__ T _Pow_int(T, int);		template<class T> extern __host__ __device__ T _Pow_int(T, int);
			extern __host__ __device__ long long int abs(long long int);
	#endif /* __GNUC__ */		#endif /* __GNUC__ */
	#if defined(__GNUC__)		#if defined(__GNUC__)
	namespace std {		namespace std {
	#endif /* __GNUC__ */		#endif /* __GNUC__ */
	extern __host__ __device__ long int __cdecl abs(long int);		extern __host__ __device__ long int __cdecl abs(long int);
	extern __host__ __device__ float __cdecl abs(float);		extern __host__ __device__ float __cdecl abs(float);
	extern __host__ __device__ double __cdecl abs(double);		extern __host__ __device__ double __cdecl abs(double);
	skipping to change at line 703		skipping to change at line 813
	static __inline__ __host__ __device__ float rcbrt(float a)		static __inline__ __host__ __device__ float rcbrt(float a)
	{		{
	return rcbrtf(a);		return rcbrtf(a);
	}		}
	static __inline__ __host__ __device__ float sinpi(float a)		static __inline__ __host__ __device__ float sinpi(float a)
	{		{
	return sinpif(a);		return sinpif(a);
	}		}
			static __inline__ __host__ __device__ float cospi(float a)
			{
			return cospif(a);
			}
	static __inline__ __host__ __device__ void sincos(float a, float *sptr, flo at *cptr)		static __inline__ __host__ __device__ void sincos(float a, float *sptr, flo at *cptr)
	{		{
	sincosf(a, sptr, cptr);		sincosf(a, sptr, cptr);
	}		}
	static __inline__ __host__ __device__ float erf(float a)		static __inline__ __host__ __device__ float erf(float a)
	{		{
	return erff(a);		return erff(a);
	}		}
	skipping to change at line 1430		skipping to change at line 1545
	lo = result[idx+1];		lo = result[idx+1];
	if (e) {		if (e) {
	q = 32 - e;		q = 32 - e;
	hi = (hi << e) + (lo >> q);		hi = (hi << e) + (lo >> q);
	lo = (lo << e) + (result[idx] >> q);		lo = (lo << e) + (result[idx] >> q);
	}		}
	q = hi >> 30;		q = hi >> 30;
	/* fraction */		/* fraction */
	hi = (hi << 2) + (lo >> 30);		hi = (hi << 2) + (lo >> 30);
	lo = (lo << 2);		lo = (lo << 2);
	e = (hi + (lo > 0)) > 0x80000000; /* fraction >= 0.5 */		e = hi >> 31; /* fraction >= 0.5 */
	q += e;		q += e;
	if (s) q = -q;		if (s) q = -q;
	if (e) {		if (e) {
	unsigned int t;		unsigned int t;
	hi = ~hi;		hi = ~hi;
	lo = -(int)lo;		lo = -(int)lo;
	t = (lo == 0);		t = (lo == 0);
	hi += t;		hi += t;
	s = s ^ 0x80000000;		s = s ^ 0x80000000;
	}		}
	*quadrant = q;		*quadrant = q;
	/* normalize fraction */		/* normalize fraction */
	e = 0;		e = __clz(hi);
	while ((int)hi > 0) {		if (e) {
	hi = (hi << 1) + (lo >> 31);		hi = (hi << e) + (lo >> (32 - e));
	lo = (lo << 1);
	e--;
	}		}
	lo = hi * 0xc90fdaa2;		lo = hi * 0xc90fdaa2;
	hi = __umulhi(hi, 0xc90fdaa2);		hi = __umulhi(hi, 0xc90fdaa2);
	if ((int)hi > 0) {		if ((int)hi > 0) {
	hi = (hi << 1) + (lo >> 31);		hi = (hi << 1) + (lo >> 31);
	lo = (lo << 1);		e++;
	e--;
	}		}
	hi = hi + (lo > 0);		ia = s | (((126 - e) << 23) + ((((hi + 1) >> 7) + 1) >> 1));
	ia = s | (((e + 126) << 23) + (hi >> 8) + ((hi << 24) >= 0x80000000));
	return __int_as_float(ia);		return __int_as_float(ia);
	}		}
	q = __float2int_rn (a * CUDART_2_OVER_PI_F);		q = __float2int_rn (a * CUDART_2_OVER_PI_F);
	j = (float)q;		j = (float)q;
	a = __internal_fmad (-j, 1.5703125000000000e+000f, a);
	#if __CUDA_ARCH__ >= 200		#if __CUDA_ARCH__ >= 200
	/* take advantage of FMA by combining two constants */		/* take advantage of FMA */
	a = __fmaf_rn (-j, 4.8382673412561417e-004f, a);		a = __fmaf_rn (-j, 1.5707962512969971e+000f, a);
			a = __fmaf_rn (-j, 7.5497894158615964e-008f, a);
			a = __fmaf_rn (-j, 5.3903029534742384e-015f, a);
	#else /* __CUDA_ARCH__ >= 200 */		#else /* __CUDA_ARCH__ >= 200 */
			a = __internal_fmad (-j, 1.5703125000000000e+000f, a);
	a = __internal_fmad (-j, 4.8351287841796875e-004f, a);		a = __internal_fmad (-j, 4.8351287841796875e-004f, a);
	a = __internal_fmad (-j, 3.1385570764541626e-007f, a);		a = __internal_fmad (-j, 3.1385570764541626e-007f, a);
	#endif /* __CUDA_ARCH__ >= 200 */
	a = __internal_fmad (-j, 6.0771005065061922e-011f, a);		a = __internal_fmad (-j, 6.0771005065061922e-011f, a);
			#endif /* __CUDA_ARCH__ >= 200 */
	*quadrant = q;		*quadrant = q;
	return a;		return a;
	}		}
	/* High quality implementation of expf(). A naive implementation, expf(x) =		/* High quality implementation of expf(). A naive implementation, expf(x) =
	* exp2f (x * log2(e)), loses significant accuracy for large arguments, and		* exp2f (x * log2(e)), loses significant accuracy for large arguments, and
	* may return results with only 15 to 16 good bits (out of 24). The present		* may return results with only 15 to 16 good bits (out of 24). The present
	* implementation limits the error to about 2 ulps across the entire argume nt		* implementation limits the error to about 2 ulps across the entire argume nt
	* range. It does so by employing an extended precision representation for		* range. It does so by employing an extended precision representation for
	* ln(2) which is composited from ln2_hi = 0.6931457519f which provides the		* ln(2) which is composited from ln2_hi = 0.6931457519f which provides the
	skipping to change at line 1637		skipping to change at line 1750
	z = __internal_fmad (z, x2, -5.00000000e-1f);		z = __internal_fmad (z, x2, -5.00000000e-1f);
	z = __internal_fmad (z, x2, 1.00000000e+0f);		z = __internal_fmad (z, x2, 1.00000000e+0f);
	return z;		return z;
	}		}
	static __forceinline__ float __internal_accurate_sinf(float a)		static __forceinline__ float __internal_accurate_sinf(float a)
	{		{
	float z;		float z;
	int i;		int i;
	if ((__isinff(a)) || (a == CUDART_ZERO_F)) {		if (__isinff(a)) {
	return __fmul_rn (a, CUDART_ZERO_F);		a = __fmul_rn (a, CUDART_ZERO_F); /* return NaN */
	}		}
	z = __internal_trig_reduction_kernel(a, &i);		a = __internal_trig_reduction_kernel(a, &i);
	/* here, abs(z) <= pi/4, and i has the quadrant */		/* here, abs(z) <= pi/4, and i has the quadrant */
	if (i & 1) {		if (i & 1) {
	z = __internal_cos_kernel(z);		z = __internal_cos_kernel(a);
	} else {		} else {
	z = __internal_sin_kernel(z);		z = __internal_sin_kernel(a);
	}		}
	if (i & 2) {		if (i & 2) {
	z = -z;		z = -z;
	}		}
			if (a == CUDART_ZERO_F) {
			z = __fmul_rn (a, CUDART_ZERO_F);
			}
	return z;		return z;
	}		}
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	static __forceinline__ float sinf(float a)		static __forceinline__ float sinf(float a)
	{		{
	#if defined(__USE_FAST_MATH__)		#if defined(__USE_FAST_MATH__)
	return __sinf(a);		return __sinf(a);
	#else /* __USE_FAST_MATH__ */		#else /* __USE_FAST_MATH__ */
	return __internal_accurate_sinf(a);		return __internal_accurate_sinf(a);
	#endif /* __USE_FAST_MATH__ */		#endif /* __USE_FAST_MATH__ */
	}		}
	static __forceinline__ float sinpif(float a)		static __forceinline__ float sinpif(float a)
	{		{
	float z;		volatile float z; /* #588812 */
	int i;		int i;
	if ((__isinff(a)) || (a == CUDART_ZERO_F)) {		if (__isinff(a)) {
	return __fmul_rn (a, CUDART_ZERO_F);		a = __fmul_rn (a, CUDART_ZERO_F); /* return NaN */
	}		}
	if (a == floorf(a)) {		if (a == truncf(a)) {
	return __int_as_float(__float_as_int(a) & 0x80000000);		a = __int_as_float(__float_as_int(a) & 0x80000000);
	}		}
	i = (int)rintf (a * 2.0f);		i = __float2int_rn (a * 2.0f);
	z = __internal_fmad ((float)i, -0.5f, a);		z = (float)i;
			z = __internal_fmad (-z, 0.5f, a);
			#if __CUDA_ARCH__ >= 200
			z = __fmaf_rn (z, 3.141592503e+00f, z * 1.509958025e-07f);
			#else /* __CUDA_ARCH__ >= 200 */
	z = z * CUDART_PI_F;		z = z * CUDART_PI_F;
			#endif /* __CUDA_ARCH__ >= 200 */
	if (i & 1) {		if (i & 1) {
	z = __internal_cos_kernel(z);		z = __internal_cos_kernel(z);
	} else {		} else {
	z = __internal_sin_kernel(z);		z = __internal_sin_kernel(z);
	}		}
	if (i & 2) {		if (i & 2) {
	z = -z;		z = -z;
	}		}
			if (a == CUDART_ZERO_F) {
			z = __fmul_rn (a, CUDART_ZERO_F);
			}
			return z;
			}
			static __forceinline__ float cospif(float a)
			{
			volatile float z;
			int i;
			if (fabsf(a) > CUDART_TWO_TO_24_F) {
			a = __fmul_rn (a, CUDART_ZERO_F);
			}
			i = __float2int_rn (a * 2.0f);
			z = (float)i;
			z = __internal_fmad (-z, 0.5f, a);
			#if __CUDA_ARCH__ >= 200
			z = __fmaf_rn (z, 3.141592503e+00f, z * 1.509958025e-07f);
			#else /* __CUDA_ARCH__ >= 200 */
			z = z * CUDART_PI_F;
			#endif /* __CUDA_ARCH__ >= 200 */
			i++;
			if (i & 1) {
			z = __internal_cos_kernel(z);
			} else {
			z = __internal_sin_kernel(z);
			}
			if (i & 2) {
			z = -z;
			}
			if (z == CUDART_ZERO_F) {
			z = fabsf (z);
			}
	return z;		return z;
	}		}
	static __forceinline__ float cosf(float a)		static __forceinline__ float cosf(float a)
	{		{
	#if defined(__USE_FAST_MATH__)		#if defined(__USE_FAST_MATH__)
	return __cosf(a);		return __cosf(a);
	#else /* __USE_FAST_MATH__ */		#else /* __USE_FAST_MATH__ */
	float z;		float z;
	int i;		int i;
	if (__isinff(a)) {		if (__isinff(a)) {
	return __fadd_rn (a, -a); /* return NaN */		a = __fmul_rn (a, CUDART_ZERO_F); /* return NaN */
	}		}
	z = __internal_trig_reduction_kernel(a, &i);		z = __internal_trig_reduction_kernel(a, &i);
	/* here, abs(z) <= pi/4, and i has the quadrant */		/* here, abs(z) <= pi/4, and i has the quadrant */
	i++;		i++;
	if (i & 1) {		if (i & 1) {
	z = __internal_cos_kernel(z);		z = __internal_cos_kernel(z);
	} else {		} else {
	z = __internal_sin_kernel(z);		z = __internal_sin_kernel(z);
	}		}
	if (i & 2) {		if (i & 2) {
	skipping to change at line 1728		skipping to change at line 1883
	static __forceinline__ float tanf(float a)		static __forceinline__ float tanf(float a)
	{		{
	#if defined(__USE_FAST_MATH__)		#if defined(__USE_FAST_MATH__)
	return __tanf(a);		return __tanf(a);
	#else /* __USE_FAST_MATH__ */		#else /* __USE_FAST_MATH__ */
	float z;		float z;
	int i;		int i;
	if (__isinff(a)) {		if (__isinff(a)) {
	return __fadd_rn (a, -a); /* return NaN */		a = __fmul_rn (a, CUDART_ZERO_F); /* return NaN */
	}		}
	z = __internal_trig_reduction_kernel(a, &i);		z = __internal_trig_reduction_kernel(a, &i);
	/* here, abs(z) <= pi/4, and i has the quadrant */		/* here, abs(z) <= pi/4, and i has the quadrant */
	z = __internal_tan_kernel(z);		z = __internal_tan_kernel(z);
	if (i & 1) {		if (i & 1) {
	z = - (1.0f / z);		z = - (1.0f / z);
	}		}
	return z;		return z;
	#endif /* __USE_FAST_MATH__ */		#endif /* __USE_FAST_MATH__ */
	}		}
	skipping to change at line 2931		skipping to change at line 3086
	}		}
	}		}
	return res;		return res;
	}		}
	static __forceinline__ void sincosf(float a, float *sptr, float *cptr)		static __forceinline__ void sincosf(float a, float *sptr, float *cptr)
	{		{
	#if defined(__USE_FAST_MATH__)		#if defined(__USE_FAST_MATH__)
	__sincosf(a, sptr, cptr);		__sincosf(a, sptr, cptr);
	#else /* __USE_FAST_MATH__ */		#else /* __USE_FAST_MATH__ */
	float t, s, c;		volatile float s, c; /* #588812 */
	volatile float t2, u2;		float t;
	int quadrant;		int quadrant;
	t = fabsf(a);
	if ((t == CUDART_INF_F) || (t == CUDART_ZERO_F)) {		if (__isinff(a)) {
	s = __fmul_rn (a, CUDART_ZERO_F); /* generate NaN, zero */		a = __fmul_rn (a, CUDART_ZERO_F); /* return NaN */
	c = 1.0f + s; /* generate NaN, one */
	*sptr = s;
	*cptr = c;
	return;
	}		}
	t = __internal_trig_reduction_kernel(a, &quadrant);		a = __internal_trig_reduction_kernel(a, &quadrant);
	u2 = __internal_cos_kernel(t);		c = __internal_cos_kernel(a);
	t2 = __internal_sin_kernel(t);		s = __internal_sin_kernel(a);
			t = s;
	if (quadrant & 1) {		if (quadrant & 1) {
	s = u2;		s = c;
	c = t2;		c = t;
	} else {
	s = t2;
	c = u2;
	}		}
	if (quadrant & 2) {		if (quadrant & 2) {
	s = -s;		s = -s;
	}		}
	quadrant++;		quadrant++;
	if (quadrant & 2) {		if (quadrant & 2) {
	c = -c;		c = -c;
	}		}
			if (a == CUDART_ZERO_F) {
			s = __fmul_rn (a, CUDART_ZERO_F);
			}
	*sptr = s;		*sptr = s;
	*cptr = c;		*cptr = c;
	#endif /* __USE_FAST_MATH__ */		#endif /* __USE_FAST_MATH__ */
	}		}
			#if defined(_WIN32)
			#if _MSC_VER >= 1600
			static __forceinline__ float _hypotf(float a, float b)
			{
			return hypotf(a, b);
			}
			#endif /* _MSC_VER >= 1600 */
			#endif /* defined(_WIN32) */
	#else /* __CUDABE__ */		#else /* __CUDABE__ */
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* ONLY FOR HOST CODE! NOT FOR DEVICE EXECUTION *		* ONLY FOR HOST CODE! NOT FOR DEVICE EXECUTION *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	#include <crt/func_macro.h>		#include <crt/func_macro.h>
	skipping to change at line 3035		skipping to change at line 3196
	cvt.d = a;		cvt.d = a;
	return cvt.l << 1 == 0xffe0000000000000ull;		return cvt.l << 1 == 0xffe0000000000000ull;
	}		}
	#endif /* _WIN32 || __APPLE__ */		#endif /* _WIN32 || __APPLE__ */
	#if defined(_WIN32)		#if defined(_WIN32)
			#if _MSC_VER < 1600
	__func__(long long int llabs(long long int a))		__func__(long long int llabs(long long int a))
	{		{
	return a < 0ll ? -a : a;		return a < 0ll ? -a : a;
	}		}
			#endif /* _MSC_VER < 1600 */
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* HOST IMPLEMENTATION FOR DOUBLE ROUTINES FOR WINDOWS PLATFORM *		* HOST IMPLEMENTATION FOR DOUBLE ROUTINES FOR WINDOWS PLATFORM *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	__func__(int __signbit(double a))		__func__(int __signbit(double a))
	{		{
	volatile union {		volatile union {
	skipping to change at line 3507		skipping to change at line 3670
	int quo;		int quo;
	return remquo (a, b, &quo);		return remquo (a, b, &quo);
	}		}
	__func__(double fma (double a, double b, double c))		__func__(double fma (double a, double b, double c))
	{		{
	volatile union {		volatile union {
	struct {		struct {
	unsigned int lo;		unsigned int lo;
	unsigned int hi;		unsigned int hi;
	};		} part;
	double d;		double d;
	} xx, yy, zz, ww;		} xx, yy, zz, ww;
	unsigned int s, t, u, prod0, prod1, prod2, prod3, expo_x, expo_y, expo_z;		unsigned int s, t, u, prod0, prod1, prod2, prod3, expo_x, expo_y, expo_z;
	xx.d = a;		xx.d = a;
	yy.d = b;		yy.d = b;
	zz.d = c;		zz.d = c;
	expo_z = 0x7FF;		expo_z = 0x7FF;
	t = xx.hi >> 20;		t = xx.part.hi >> 20;
	expo_x = expo_z & t;		expo_x = expo_z & t;
	expo_x = expo_x - 1; /* expo(x) - 1 */		expo_x = expo_x - 1; /* expo(x) - 1 */
	t = yy.hi >> 20;		t = yy.part.hi >> 20;
	expo_y = expo_z & t;		expo_y = expo_z & t;
	expo_y = expo_y - 1; /* expo(y) - 1 */		expo_y = expo_y - 1; /* expo(y) - 1 */
	t = zz.hi >> 20;		t = zz.part.hi >> 20;
	expo_z = expo_z & t;		expo_z = expo_z & t;
	expo_z = expo_z - 1; /* expo(z) - 1 */		expo_z = expo_z - 1; /* expo(z) - 1 */
	if (!((expo_x <= 0x7FD) &&		if (!((expo_x <= 0x7FD) &&
	(expo_y <= 0x7FD) &&		(expo_y <= 0x7FD) &&
	(expo_z <= 0x7FD))) {		(expo_z <= 0x7FD))) {
	/* fma (nan, y, z) --> nan		/* fma (nan, y, z) --> nan
	fma (x, nan, z) --> nan		fma (x, nan, z) --> nan
	fma (x, y, nan) --> nan		fma (x, y, nan) --> nan
	*/		*/
	if (((yy.hi << 1) | (yy.lo != 0)) > 0xffe00000) {		if (((yy.part.hi << 1) | (yy.part.lo != 0)) > 0xffe00000) {
	yy.hi |= 0x00080000;		yy.part.hi |= 0x00080000;
	return yy.d;		return yy.d;
	}		}
	if (((zz.hi << 1) | (zz.lo != 0)) > 0xffe00000) {		if (((zz.part.hi << 1) | (zz.part.lo != 0)) > 0xffe00000) {
	zz.hi |= 0x00080000;		zz.part.hi |= 0x00080000;
	return zz.d;		return zz.d;
	}		}
	if (((xx.hi << 1) | (xx.lo != 0)) > 0xffe00000) {		if (((xx.part.hi << 1) | (xx.part.lo != 0)) > 0xffe00000) {
	xx.hi |= 0x00080000;		xx.part.hi |= 0x00080000;
	return xx.d;		return xx.d;
	}		}
	/* fma (0, inf, z) --> INDEFINITE		/* fma (0, inf, z) --> INDEFINITE
	fma (inf, 0, z) --> INDEFINITE		fma (inf, 0, z) --> INDEFINITE
	fma (-inf,+y,+inf) --> INDEFINITE		fma (-inf,+y,+inf) --> INDEFINITE
	fma (+x,-inf,+inf) --> INDEFINITE		fma (+x,-inf,+inf) --> INDEFINITE
	fma (+inf,-y,+inf) --> INDEFINITE		fma (+inf,-y,+inf) --> INDEFINITE
	fma (-x,+inf,+inf) --> INDEFINITE		fma (-x,+inf,+inf) --> INDEFINITE
	fma (-inf,-y,-inf) --> INDEFINITE		fma (-inf,-y,-inf) --> INDEFINITE
	fma (-x,-inf,-inf) --> INDEFINITE		fma (-x,-inf,-inf) --> INDEFINITE
	fma (+inf,+y,-inf) --> INDEFINITE		fma (+inf,+y,-inf) --> INDEFINITE
	fma (+x,+inf,-inf) --> INDEFINITE		fma (+x,+inf,-inf) --> INDEFINITE
	*/		*/
	if (((((xx.hi << 1) | xx.lo) == 0) &&		if (((((xx.part.hi << 1) | xx.part.lo) == 0) &&
	(((yy.hi << 1) | (yy.lo != 0)) == 0xffe00000)) ||		(((yy.part.hi << 1) | (yy.part.lo != 0)) == 0xffe00000)) ||
	((((yy.hi << 1) | yy.lo) == 0) &&		((((yy.part.hi << 1) | yy.part.lo) == 0) &&
	(((xx.hi << 1) | (xx.lo != 0)) == 0xffe00000))) {		(((xx.part.hi << 1) | (xx.part.lo != 0)) == 0xffe00000))) {
	xx.hi = 0xfff80000;		xx.part.hi = 0xfff80000;
	xx.lo = 0x00000000;		xx.part.lo = 0x00000000;
	return xx.d;		return xx.d;
	}		}
	if (((zz.hi << 1) | (zz.lo != 0)) == 0xffe00000) {		if (((zz.part.hi << 1) | (zz.part.lo != 0)) == 0xffe00000) {
	if ((((yy.hi << 1) | (yy.lo != 0)) == 0xffe00000) ||		if ((((yy.part.hi << 1) | (yy.part.lo != 0)) == 0xffe00000) ||
	(((xx.hi << 1) | (xx.lo != 0)) == 0xffe00000)) {		(((xx.part.hi << 1) | (xx.part.lo != 0)) == 0xffe00000)) {
	if ((int)(xx.hi ^ yy.hi ^ zz.hi) < 0) {		if ((int)(xx.part.hi ^ yy.part.hi ^ zz.part.hi) < 0) {
	xx.hi = 0xfff80000;		xx.part.hi = 0xfff80000;
	xx.lo = 0x00000000;		xx.part.lo = 0x00000000;
	return xx.d;		return xx.d;
	}		}
	}		}
	}		}
	/* fma (inf, y, z) --> inf		/* fma (inf, y, z) --> inf
	fma (x, inf, z) --> inf		fma (x, inf, z) --> inf
	fma (x, y, inf) --> inf		fma (x, y, inf) --> inf
	*/		*/
	if (((xx.hi << 1) | (xx.lo != 0)) == 0xffe00000) {		if (((xx.part.hi << 1) | (xx.part.lo != 0)) == 0xffe00000) {
	xx.hi = xx.hi ^ (yy.hi & 0x80000000);		xx.part.hi = xx.part.hi ^ (yy.part.hi & 0x80000000);
	return xx.d;		return xx.d;
	}		}
	if (((yy.hi << 1) | (yy.lo != 0)) == 0xffe00000) {		if (((yy.part.hi << 1) | (yy.part.lo != 0)) == 0xffe00000) {
	yy.hi = yy.hi ^ (xx.hi & 0x80000000);		yy.part.hi = yy.part.hi ^ (xx.part.hi & 0x80000000);
	return yy.d;		return yy.d;
	}		}
	if (((zz.hi << 1) | (zz.lo != 0)) == 0xffe00000) {		if (((zz.part.hi << 1) | (zz.part.lo != 0)) == 0xffe00000) {
	return zz.d;		return zz.d;
	}		}
	/* fma (+0, -y, -0) --> -0		/* fma (+0, -y, -0) --> -0
	fma (-0, +y, -0) --> -0		fma (-0, +y, -0) --> -0
	fma (+x, -0, -0) --> -0		fma (+x, -0, -0) --> -0
	fma (-x, +0, -0) --> -0		fma (-x, +0, -0) --> -0
	*/		*/
	if ((zz.hi == 0x80000000) && (zz.lo == 0)) {		if ((zz.part.hi == 0x80000000) && (zz.part.lo == 0)) {
	if ((((xx.hi << 1) | xx.lo) == 0) ||		if ((((xx.part.hi << 1) | xx.part.lo) == 0) ||
	(((yy.hi << 1) | yy.lo) == 0)) {		(((yy.part.hi << 1) | yy.part.lo) == 0)) {
	if ((int)(xx.hi ^ yy.hi) < 0) {		if ((int)(xx.part.hi ^ yy.part.hi) < 0) {
	return zz.d;		return zz.d;
	}		}
	}		}
	}		}
	/* fma (0, y, 0) --> +0 (-0 if round down and signs of addend differ)		/* fma (0, y, 0) --> +0 (-0 if round down and signs of addend differ)
	fma (x, 0, 0) --> +0 (-0 if round down and signs of addend differ)		fma (x, 0, 0) --> +0 (-0 if round down and signs of addend differ)
	*/		*/
	if ((((zz.hi << 1) | zz.lo) == 0) &&		if ((((zz.part.hi << 1) | zz.part.lo) == 0) &&
	((((xx.hi << 1) | xx.lo) == 0) ||		((((xx.part.hi << 1) | xx.part.lo) == 0) ||
	(((yy.hi << 1) | yy.lo) == 0))) {		(((yy.part.hi << 1) | yy.part.lo) == 0))) {
	zz.hi &= 0x7fffffff;		zz.part.hi &= 0x7fffffff;
	return zz.d;		return zz.d;
	}		}
	/* fma (0, y, z) --> z		/* fma (0, y, z) --> z
	fma (x, 0, z) --> z		fma (x, 0, z) --> z
	*/		*/
	if ((((xx.hi << 1) | xx.lo) == 0) ||		if ((((xx.part.hi << 1) | xx.part.lo) == 0) ||
	(((yy.hi << 1) | yy.lo) == 0)) {		(((yy.part.hi << 1) | yy.part.lo) == 0)) {
	return zz.d;		return zz.d;
	}		}
	if (expo_x == 0xffffffff) {		if (expo_x == 0xffffffff) {
	expo_x++;		expo_x++;
	t = xx.hi & 0x80000000;		t = xx.part.hi & 0x80000000;
	s = xx.lo >> 21;		s = xx.part.lo >> 21;
	xx.lo = xx.lo << 11;		xx.part.lo = xx.part.lo << 11;
	xx.hi = xx.hi << 11;		xx.part.hi = xx.part.hi << 11;
	xx.hi = xx.hi | s;		xx.part.hi = xx.part.hi | s;
	if (!xx.hi) {		if (!xx.part.hi) {
	xx.hi = xx.lo;		xx.part.hi = xx.part.lo;
	xx.lo = 0;		xx.part.lo = 0;
	expo_x -= 32;		expo_x -= 32;
	}		}
	while ((int)xx.hi > 0) {		while ((int)xx.part.hi > 0) {
	s = xx.lo >> 31;		s = xx.part.lo >> 31;
	xx.lo = xx.lo + xx.lo;		xx.part.lo = xx.part.lo + xx.part.lo;
	xx.hi = xx.hi + xx.hi;		xx.part.hi = xx.part.hi + xx.part.hi;
	xx.hi = xx.hi | s;		xx.part.hi = xx.part.hi | s;
	expo_x--;		expo_x--;
	}		}
	xx.lo = (xx.lo >> 11);		xx.part.lo = (xx.part.lo >> 11);
	xx.lo |= (xx.hi << 21);		xx.part.lo |= (xx.part.hi << 21);
	xx.hi = (xx.hi >> 11) | t;		xx.part.hi = (xx.part.hi >> 11) | t;
	}		}
	if (expo_y == 0xffffffff) {		if (expo_y == 0xffffffff) {
	expo_y++;		expo_y++;
	t = yy.hi & 0x80000000;		t = yy.part.hi & 0x80000000;
	s = yy.lo >> 21;		s = yy.part.lo >> 21;
	yy.lo = yy.lo << 11;		yy.part.lo = yy.part.lo << 11;
	yy.hi = yy.hi << 11;		yy.part.hi = yy.part.hi << 11;
	yy.hi = yy.hi | s;		yy.part.hi = yy.part.hi | s;
	if (!yy.hi) {		if (!yy.part.hi) {
	yy.hi = yy.lo;		yy.part.hi = yy.part.lo;
	yy.lo = 0;		yy.part.lo = 0;
	expo_y -= 32;		expo_y -= 32;
	}		}
	while ((int)yy.hi > 0) {		while ((int)yy.part.hi > 0) {
	s = yy.lo >> 31;		s = yy.part.lo >> 31;
	yy.lo = yy.lo + yy.lo;		yy.part.lo = yy.part.lo + yy.part.lo;
	yy.hi = yy.hi + yy.hi;		yy.part.hi = yy.part.hi + yy.part.hi;
	yy.hi = yy.hi | s;		yy.part.hi = yy.part.hi | s;
	expo_y--;		expo_y--;
	}		}
	yy.lo = (yy.lo >> 11);		yy.part.lo = (yy.part.lo >> 11);
	yy.lo |= (yy.hi << 21);		yy.part.lo |= (yy.part.hi << 21);
	yy.hi = (yy.hi >> 11) | t;		yy.part.hi = (yy.part.hi >> 11) | t;
	}		}
	if (expo_z == 0xffffffff) {		if (expo_z == 0xffffffff) {
	expo_z++;		expo_z++;
	t = zz.hi & 0x80000000;		t = zz.part.hi & 0x80000000;
	s = zz.lo >> 21;		s = zz.part.lo >> 21;
	zz.lo = zz.lo << 11;		zz.part.lo = zz.part.lo << 11;
	zz.hi = zz.hi << 11;		zz.part.hi = zz.part.hi << 11;
	zz.hi = zz.hi | s;		zz.part.hi = zz.part.hi | s;
	if (!zz.hi) {		if (!zz.part.hi) {
	zz.hi = zz.lo;		zz.part.hi = zz.part.lo;
	zz.lo = 0;		zz.part.lo = 0;
	expo_z -= 32;		expo_z -= 32;
	}		}
	while ((int)zz.hi > 0) {		while ((int)zz.part.hi > 0) {
	s = zz.lo >> 31;		s = zz.part.lo >> 31;
	zz.lo = zz.lo + zz.lo;		zz.part.lo = zz.part.lo + zz.part.lo;
	zz.hi = zz.hi + zz.hi;		zz.part.hi = zz.part.hi + zz.part.hi;
	zz.hi = zz.hi | s;		zz.part.hi = zz.part.hi | s;
	expo_z--;		expo_z--;
	}		}
	zz.lo = (zz.lo >> 11);		zz.part.lo = (zz.part.lo >> 11);
	zz.lo |= (zz.hi << 21);		zz.part.lo |= (zz.part.hi << 21);
	zz.hi = (zz.hi >> 11) | t;		zz.part.hi = (zz.part.hi >> 11) | t;
	}		}
	}		}
	expo_x = expo_x + expo_y;		expo_x = expo_x + expo_y;
	expo_y = xx.hi ^ yy.hi;		expo_y = xx.part.hi ^ yy.part.hi;
	t = xx.lo >> 21;		t = xx.part.lo >> 21;
	xx.lo = xx.lo << 11;		xx.part.lo = xx.part.lo << 11;
	xx.hi = xx.hi << 11;		xx.part.hi = xx.part.hi << 11;
	xx.hi = xx.hi | t;		xx.part.hi = xx.part.hi | t;
	yy.hi = yy.hi & 0x000fffff;		yy.part.hi = yy.part.hi & 0x000fffff;
	xx.hi = xx.hi | 0x80000000; /* set mantissa hidden bit */		xx.part.hi = xx.part.hi | 0x80000000; /* set mantissa hidden bit */
	yy.hi = yy.hi | 0x00100000; /* set mantissa hidden bit */		yy.part.hi = yy.part.hi | 0x00100000; /* set mantissa hidden bit */
	prod0 = xx.lo * yy.lo;		prod0 = xx.part.lo * yy.part.lo;
	prod1 =(unsigned)(((unsigned long long)xx.lo*(unsigned long long)yy.lo)>>		prod1 =(unsigned)(((unsigned long long)xx.part.lo*(unsigned long long)yy.
	32);		part.lo)>>32);
	prod2 = xx.hi * yy.lo;		prod2 = xx.part.hi * yy.part.lo;
	prod3 = xx.lo * yy.hi;		prod3 = xx.part.lo * yy.part.hi;
	prod1 += prod2;		prod1 += prod2;
	t = prod1 < prod2;		t = (unsigned)(prod1 < prod2);
	prod1 += prod3;		prod1 += prod3;
	t += prod1 < prod3;		t += prod1 < prod3;
	prod2 =(unsigned)(((unsigned long long)xx.hi*(unsigned long long)yy.lo)>>		prod2 =(unsigned)(((unsigned long long)xx.part.hi*(unsigned long long)yy.
	32);		part.lo)>>32);
	prod3 =(unsigned)(((unsigned long long)xx.lo*(unsigned long long)yy.hi)>>		prod3 =(unsigned)(((unsigned long long)xx.part.lo*(unsigned long long)yy.
	32);		part.hi)>>32);
	prod2 += prod3;		prod2 += prod3;
	s = prod2 < prod3;		s = (unsigned)(prod2 < prod3);
	prod3 = xx.hi * yy.hi;		prod3 = xx.part.hi * yy.part.hi;
	prod2 += prod3;		prod2 += prod3;
	s += prod2 < prod3;		s += prod2 < prod3;
	prod2 += t;		prod2 += t;
	s += prod2 < t;		s += prod2 < t;
	prod3 =(unsigned)(((unsigned long long)xx.hi*(unsigned long long)yy.hi)>> 32);		prod3 =(unsigned)(((unsigned long long)xx.part.hi*(unsigned long long)yy. part.hi)>>32);
	prod3 = prod3 + s;		prod3 = prod3 + s;
	yy.lo = prod0; /* mantissa */		yy.part.lo = prod0; /* mantissa */
	yy.hi = prod1; /* mantissa */		yy.part.hi = prod1; /* mantissa */
	xx.lo = prod2; /* mantissa */		xx.part.lo = prod2; /* mantissa */
	xx.hi = prod3; /* mantissa */		xx.part.hi = prod3; /* mantissa */
	expo_x = expo_x - (1023 - 2); /* expo-1 */		expo_x = expo_x - (1023 - 2); /* expo-1 */
	expo_y = expo_y & 0x80000000; /* sign */		expo_y = expo_y & 0x80000000; /* sign */
	if (xx.hi < 0x00100000) {		if (xx.part.hi < 0x00100000) {
	s = xx.lo >> 31;		s = xx.part.lo >> 31;
	s = (xx.hi << 1) + s;		s = (xx.part.hi << 1) + s;
	xx.hi = s;		xx.part.hi = s;
	s = yy.hi >> 31;		s = yy.part.hi >> 31;
	s = (xx.lo << 1) + s;		s = (xx.part.lo << 1) + s;
	xx.lo = s;		xx.part.lo = s;
	s = yy.lo >> 31;		s = yy.part.lo >> 31;
	s = (yy.hi << 1) + s;		s = (yy.part.hi << 1) + s;
	yy.hi = s;		yy.part.hi = s;
	s = yy.lo << 1;		s = yy.part.lo << 1;
	yy.lo = s;		yy.part.lo = s;
	expo_x--;		expo_x--;
	}		}
	t = 0;		t = 0;
	if (((zz.hi << 1) | zz.lo) != 0) { /* z is not zero */		if (((zz.part.hi << 1) | zz.part.lo) != 0) { /* z is not zero */
	s = zz.hi & 0x80000000;		s = zz.part.hi & 0x80000000;
	zz.hi &= 0x000fffff;		zz.part.hi &= 0x000fffff;
	zz.hi |= 0x00100000;		zz.part.hi |= 0x00100000;
	ww.hi = 0;		ww.part.hi = 0;
	ww.lo = 0;		ww.part.lo = 0;
	/* compare and swap. put augend into xx:yy */		/* compare and swap. put augend into xx:yy */
	if ((int)expo_z > (int)expo_x) {		if ((int)expo_z > (int)expo_x) {
	t = expo_z;		t = expo_z;
	expo_z = expo_x;		expo_z = expo_x;
	expo_x = t;		expo_x = t;
	t = zz.hi;		t = zz.part.hi;
	zz.hi = xx.hi;		zz.part.hi = xx.part.hi;
	xx.hi = t;		xx.part.hi = t;
	t = zz.lo;		t = zz.part.lo;
	zz.lo = xx.lo;		zz.part.lo = xx.part.lo;
	xx.lo = t;		xx.part.lo = t;
	t = ww.hi;		t = ww.part.hi;
	ww.hi = yy.hi;		ww.part.hi = yy.part.hi;
	yy.hi = t;		yy.part.hi = t;
	t = ww.lo;		t = ww.part.lo;
	ww.lo = yy.lo;		ww.part.lo = yy.part.lo;
	yy.lo = t;		yy.part.lo = t;
	t = expo_y;		t = expo_y;
	expo_y = s;		expo_y = s;
	s = t;		s = t;
	}		}
	/* augend_sign = expo_y, augend_mant = xx:yy, augend_expo = expo_x */		/* augend_sign = expo_y, augend_mant = xx:yy, augend_expo = expo_x */
	/* addend_sign = s, addend_mant = zz:ww, addend_expo = expo_z */		/* addend_sign = s, addend_mant = zz:ww, addend_expo = expo_z */
	expo_z = expo_x - expo_z;		expo_z = expo_x - expo_z;
	u = expo_y ^ s;		u = expo_y ^ s;
	if (expo_z <= 107) {		if (expo_z <= 107) {
	/* denormalize addend */		/* denormalize addend */
	t = 0;		t = 0;
	while (expo_z >= 32) {		while (expo_z >= 32) {
	t = ww.lo | (t != 0);		t = ww.part.lo | (t != 0);
	ww.lo = ww.hi;		ww.part.lo = ww.part.hi;
	ww.hi = zz.lo;		ww.part.hi = zz.part.lo;
	zz.lo = zz.hi;		zz.part.lo = zz.part.hi;
	zz.hi = 0;		zz.part.hi = 0;
	expo_z -= 32;		expo_z -= 32;
	}		}
	if (expo_z) {		if (expo_z) {
	t = (t >> expo_z) | (ww.lo << (32 - expo_z)) |		t = (t >> expo_z) | (ww.part.lo << (32 - expo_z)) |
	((t << (32 - expo_z)) != 0);		((t << (32 - expo_z)) != 0);
	ww.lo = (ww.lo >> expo_z) | (ww.hi << (32 - expo_z));		ww.part.lo = (ww.part.lo >> expo_z) | (ww.part.hi << (32 - expo_z))
	ww.hi = (ww.hi >> expo_z) | (zz.lo << (32 - expo_z));		;
	zz.lo = (zz.lo >> expo_z) | (zz.hi << (32 - expo_z));		ww.part.hi = (ww.part.hi >> expo_z) | (zz.part.lo << (32 - expo_z))
	zz.hi = (zz.hi >> expo_z);		;
			zz.part.lo = (zz.part.lo >> expo_z) | (zz.part.hi << (32 - expo_z))
			;
			zz.part.hi = (zz.part.hi >> expo_z);
	}		}
	} else {		} else {
	t = 1;		t = 1;
	ww.lo = 0;		ww.part.lo = 0;
	ww.hi = 0;		ww.part.hi = 0;
	zz.lo = 0;		zz.part.lo = 0;
	zz.hi = 0;		zz.part.hi = 0;
	}		}
	if ((int)u < 0) {		if ((int)u < 0) {
	/* signs differ, effective subtraction */		/* signs differ, effective subtraction */
	t = (unsigned)(-(int)t);		t = (unsigned)(-(int)t);
	s = (t != 0);		s = (unsigned)(t != 0);
	u = yy.lo - s;		u = yy.part.lo - s;
	s = u > yy.lo;		s = (unsigned)(u > yy.part.lo);
	yy.lo = u - ww.lo;		yy.part.lo = u - ww.part.lo;
	s += yy.lo > u;		s += yy.part.lo > u;
	u = yy.hi - s;		u = yy.part.hi - s;
	s = u > yy.hi;		s = (unsigned)(u > yy.part.hi);
	yy.hi = u - ww.hi;		yy.part.hi = u - ww.part.hi;
	s += yy.hi > u;		s += yy.part.hi > u;
	u = xx.lo - s;		u = xx.part.lo - s;
	s = u > xx.lo;		s = (unsigned)(u > xx.part.lo);
	xx.lo = u - zz.lo;		xx.part.lo = u - zz.part.lo;
	s += xx.lo > u;		s += xx.part.lo > u;
	xx.hi = (xx.hi - zz.hi) - s;		xx.part.hi = (xx.part.hi - zz.part.hi) - s;
	if (!(xx.hi | xx.lo | yy.hi | yy.lo | t)) {		if (!(xx.part.hi | xx.part.lo | yy.part.hi | yy.part.lo | t)) {
	/* complete cancelation, return 0 */		/* complete cancelation, return 0 */
	return xx.d;		return xx.d;
	}		}
	if ((int)xx.hi < 0) {		if ((int)xx.part.hi < 0) {
	/* Oops, augend had smaller mantissa. Negate mantissa and flip		/* Oops, augend had smaller mantissa. Negate mantissa and flip
	sign of result		sign of result
	*/		*/
	t = ~t;		t = ~t;
	yy.lo = ~yy.lo;		yy.part.lo = ~yy.part.lo;
	yy.hi = ~yy.hi;		yy.part.hi = ~yy.part.hi;
	xx.lo = ~xx.lo;		xx.part.lo = ~xx.part.lo;
	xx.hi = ~xx.hi;		xx.part.hi = ~xx.part.hi;
	if (++t == 0) {		if (++t == 0) {
	if (++yy.lo == 0) {		if (++yy.part.lo == 0) {
	if (++yy.hi == 0) {		if (++yy.part.hi == 0) {
	if (++xx.lo == 0) {		if (++xx.part.lo == 0) {
	++xx.hi;		++xx.part.hi;
	}		}
	}		}
	}		}
	}		}
	expo_y ^= 0x80000000;		expo_y ^= 0x80000000;
	}		}
	/* normalize mantissa, if necessary */		/* normalize mantissa, if necessary */
	while (!(xx.hi & 0x00100000)) {		while (!(xx.part.hi & 0x00100000)) {
	xx.hi = (xx.hi << 1) | (xx.lo >> 31);		xx.part.hi = (xx.part.hi << 1) | (xx.part.lo >> 31);
	xx.lo = (xx.lo << 1) | (yy.hi >> 31);		xx.part.lo = (xx.part.lo << 1) | (yy.part.hi >> 31);
	yy.hi = (yy.hi << 1) | (yy.lo >> 31);		yy.part.hi = (yy.part.hi << 1) | (yy.part.lo >> 31);
	yy.lo = (yy.lo << 1);		yy.part.lo = (yy.part.lo << 1);
	expo_x--;		expo_x--;
	}		}
	} else {		} else {
	/* signs are the same, effective addition */		/* signs are the same, effective addition */
	yy.lo = yy.lo + ww.lo;		yy.part.lo = yy.part.lo + ww.part.lo;
	s = yy.lo < ww.lo;		s = (unsigned)(yy.part.lo < ww.part.lo);
	yy.hi = yy.hi + s;		yy.part.hi = yy.part.hi + s;
	u = yy.hi < s;		u = (unsigned)(yy.part.hi < s);
	yy.hi = yy.hi + ww.hi;		yy.part.hi = yy.part.hi + ww.part.hi;
	u += yy.hi < ww.hi;		u += yy.part.hi < ww.part.hi;
	xx.lo = xx.lo + u;		xx.part.lo = xx.part.lo + u;
	s = xx.lo < u;		s = (unsigned)(xx.part.lo < u);
	xx.lo = xx.lo + zz.lo;		xx.part.lo = xx.part.lo + zz.part.lo;
	s += xx.lo < zz.lo;		s += xx.part.lo < zz.part.lo;
	xx.hi = xx.hi + zz.hi + s;		xx.part.hi = xx.part.hi + zz.part.hi + s;
	if (xx.hi & 0x00200000) {		if (xx.part.hi & 0x00200000) {
	t = t | (yy.lo << 31);		t = t | (yy.part.lo << 31);
	yy.lo = (yy.lo >> 1) | (yy.hi << 31);		yy.part.lo = (yy.part.lo >> 1) | (yy.part.hi << 31);
	yy.hi = (yy.hi >> 1) | (xx.lo << 31);		yy.part.hi = (yy.part.hi >> 1) | (xx.part.lo << 31);
	xx.lo = (xx.lo >> 1) | (xx.hi << 31);		xx.part.lo = (xx.part.lo >> 1) | (xx.part.hi << 31);
	xx.hi = ((xx.hi & 0x80000000) | (xx.hi >> 1)) & ~0x40000000;		xx.part.hi = ((xx.part.hi & 0x80000000) | (xx.part.hi >> 1)) & ~0x4
			0000000;
	expo_x++;		expo_x++;
	}		}
	}		}
	}		}
	t = yy.lo | (t != 0);		t = yy.part.lo | (t != 0);
	t = yy.hi | (t != 0);		t = yy.part.hi | (t != 0);
	xx.hi |= expo_y; /* or in sign bit */		xx.part.hi |= expo_y; /* or in sign bit */
	if (expo_x <= 0x7FD) {		if (expo_x <= 0x7FD) {
	/* normal */		/* normal */
	xx.hi = xx.hi & ~0x00100000; /* lop off integer bit */		xx.part.hi = xx.part.hi & ~0x00100000; /* lop off integer bit */
	s = xx.lo & 1; /* mantissa lsb */		s = xx.part.lo & 1; /* mantissa lsb */
	u = xx.lo;		u = xx.part.lo;
	xx.lo += (t == 0x80000000) ? s : (t >> 31);		xx.part.lo += (t == 0x80000000) ? s : (t >> 31);
	xx.hi += (u > xx.lo);		xx.part.hi += (u > xx.part.lo);
	xx.hi += ((expo_x + 1) << 20);		xx.part.hi += ((expo_x + 1) << 20);
	return xx.d;		return xx.d;
	} else if ((int)expo_x >= 2046) {		} else if ((int)expo_x >= 2046) {
	/* overflow */		/* overflow */
	xx.hi = (xx.hi & 0x80000000) | 0x7ff00000;		xx.part.hi = (xx.part.hi & 0x80000000) | 0x7ff00000;
	xx.lo = 0;		xx.part.lo = 0;
	return xx.d;		return xx.d;
	}		}
	/* subnormal */		/* subnormal */
	expo_x = (unsigned)(-(int)expo_x);		expo_x = (unsigned)(-(int)expo_x);
	if (expo_x > 54) {		if (expo_x > 54) {
	xx.hi = xx.hi & 0x80000000;		xx.part.hi = xx.part.hi & 0x80000000;
	xx.lo = 0;		xx.part.lo = 0;
	return xx.d;		return xx.d;
	}		}
	yy.hi = xx.hi & 0x80000000; /* save sign bit */		yy.part.hi = xx.part.hi & 0x80000000; /* save sign bit */
	xx.hi = xx.hi & ~0xffe00000;		xx.part.hi = xx.part.hi & ~0xffe00000;
	if (expo_x >= 32) {		if (expo_x >= 32) {
	t = xx.lo | (t != 0);		t = xx.part.lo | (t != 0);
	xx.lo = xx.hi;		xx.part.lo = xx.part.hi;
	xx.hi = 0;		xx.part.hi = 0;
	expo_x -= 32;		expo_x -= 32;
	}		}
	if (expo_x) {		if (expo_x) {
	t = (t >> expo_x) | (xx.lo << (32 - expo_x)) | (t != 0);		t = (t >> expo_x) | (xx.part.lo << (32 - expo_x)) | (t != 0);
	xx.lo = (xx.lo >> expo_x) | (xx.hi << (32 - expo_x));		xx.part.lo = (xx.part.lo >> expo_x) | (xx.part.hi << (32 - expo_x));
	xx.hi = (xx.hi >> expo_x);		xx.part.hi = (xx.part.hi >> expo_x);
	}		}
	expo_x = xx.lo & 1;		expo_x = xx.part.lo & 1;
	u = xx.lo;		u = xx.part.lo;
	xx.lo += (t == 0x80000000) ? expo_x : (t >> 31);		xx.part.lo += (t == 0x80000000) ? expo_x : (t >> 31);
	xx.hi += (u > xx.lo);		xx.part.hi += (u > xx.part.lo);
	xx.hi |= yy.hi;		xx.part.hi |= yy.part.hi;
	return xx.d;		return xx.d;
	}		}
	__func__(double nextafter(double a, double b))		__func__(double nextafter(double a, double b))
	{		{
	volatile union {		volatile union {
	double d;		double d;
	unsigned long long int l;		unsigned long long int l;
	} cvt;		} cvt;
	unsigned long long int ia;		unsigned long long int ia;
	skipping to change at line 4563		skipping to change at line 4726
	__func__(float expm1f(float a))		__func__(float expm1f(float a))
	{		{
	return (float)expm1((double)a);		return (float)expm1((double)a);
	}		}
	__func__(float fdimf(float a, float b))		__func__(float fdimf(float a, float b))
	{		{
	return (float)fdim((double)a, (double)b);		return (float)fdim((double)a, (double)b);
	}		}
			#if _MSC_VER < 1600
	__func__(float hypotf(float a, float b))		__func__(float hypotf(float a, float b))
	{		{
	return (float)hypot((double)a, (double)b);		return (float)hypot((double)a, (double)b);
	}		}
			#endif /* _MSC_VER < 1600 */
	__func__(float log1pf(float a))		__func__(float log1pf(float a))
	{		{
	return (float)log1p((double)a);		return (float)log1p((double)a);
	}		}
	__func__(float scalbnf(float a, int b))		__func__(float scalbnf(float a, int b))
	{		{
	return (float)scalbn((double)a, b);		return (float)scalbn((double)a, b);
	}		}
	skipping to change at line 4737		skipping to change at line 4902
	a = cos (a);		a = cos (a);
	} else {		} else {
	a = sin (a);		a = sin (a);
	}		}
	if (n & 2) {		if (n & 2) {
	a = -a;		a = -a;
	}		}
	return a;		return a;
	}		}
			__func__(double cospi(double a))
			{
			int n;
			if (__isnan(a)) {
			return a + a;
			}
			if (__isinf(a)) {
			return cos (a);
			}
			if (fabs(a) > 9.0071992547409920e+015) {
			a = 0.0;
			}
			a = remquo (a, 0.5, &n);
			a = a * CUDART_PI;
			n++;
			if (n & 1) {
			a = cos (a);
			} else {
			a = sin (a);
			}
			if (n & 2) {
			a = -a;
			}
			if (a == 0.0) {
			a = fabs(a);
			}
			return a;
			}
	__func__(double erfinv(double a))		__func__(double erfinv(double a))
	{		{
	double p, q, t, fa;		double p, q, t, fa;
	volatile union {		volatile union {
	double d;		double d;
	unsigned long long int l;		unsigned long long int l;
	} cvt;		} cvt;
	fa = fabs(a);		fa = fabs(a);
	if (fa >= 1.0) {		if (fa >= 1.0) {
	skipping to change at line 4931		skipping to change at line 5126
	__func__(float rcbrtf(float a))		__func__(float rcbrtf(float a))
	{		{
	return (float)rcbrt((double)a);		return (float)rcbrt((double)a);
	}		}
	__func__(float sinpif(float a))		__func__(float sinpif(float a))
	{		{
	return (float)sinpi((double)a);		return (float)sinpi((double)a);
	}		}
			__func__(float cospif(float a))
			{
			return (float)cospi((double)a);
			}
	__func__(float erfinvf(float a))		__func__(float erfinvf(float a))
	{		{
	return (float)erfinv((double)a);		return (float)erfinv((double)a);
	}		}
	__func__(float erfcinvf(float a))		__func__(float erfcinvf(float a))
	{		{
	return (float)erfcinv((double)a);		return (float)erfcinv((double)a);
	}		}
End of changes. 127 change blocks.
	350 lines changed or deleted		557 lines changed or added

	math_functions_dbl_ptx1.h		math_functions_dbl_ptx1.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(__MATH_FUNCTIONS_DBL_PTX1_H__)		#if !defined(__MATH_FUNCTIONS_DBL_PTX1_H__)
	#define __MATH_FUNCTIONS_DBL_PTX1_H__		#define __MATH_FUNCTIONS_DBL_PTX1_H__
	#if defined(__CUDABE__)		#if defined(__CUDABE__)
	static __forceinline__ double fabs(double a)		static __forceinline__ double fabs(double a)
	{		{
	return (double)fabsf((float)a);		return (double)fabsf((float)a);
	skipping to change at line 121		skipping to change at line 135
	static __forceinline__ double sinpi(double a)		static __forceinline__ double sinpi(double a)
	{		{
	return (double)sinpif((float)a);		return (double)sinpif((float)a);
	}		}
	static __forceinline__ double cos(double a)		static __forceinline__ double cos(double a)
	{		{
	return (double)cosf((float)a);		return (double)cosf((float)a);
	}		}
			static __forceinline__ double cospi(double a)
			{
			return (double)cospif((float)a);
			}
	static __forceinline__ void sincos(double a, double *sptr, double *cptr)		static __forceinline__ void sincos(double a, double *sptr, double *cptr)
	{		{
	float fs, fc;		float fs, fc;
	sincosf((float)a, &fs, &fc);		sincosf((float)a, &fs, &fc);
	*sptr = (double)fs;		*sptr = (double)fs;
	*cptr = (double)fc;		*cptr = (double)fc;
	}		}
End of changes. 7 change blocks.
	28 lines changed or deleted		46 lines changed or added

	math_functions_dbl_ptx3.h		math_functions_dbl_ptx3.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(__MATH_FUNCTIONS_DBL_PTX3_H__)		#if !defined(__MATH_FUNCTIONS_DBL_PTX3_H__)
	#define __MATH_FUNCTIONS_DBL_PTX3_H__		#define __MATH_FUNCTIONS_DBL_PTX3_H__
	/* True double precision implementations, since native double support */		/* True double precision implementations, since native double support */
	#if defined(__CUDABE__)		#if defined(__CUDABE__)
	/************************************************************************** *****		/************************************************************************** *****
	skipping to change at line 126		skipping to change at line 140
	alo = __double2loint(a);		alo = __double2loint(a);
	ahi = __double2hiint(a);		ahi = __double2hiint(a);
	ahi = (bhi & 0x80000000) | ahi;		ahi = (bhi & 0x80000000) | ahi;
	return __hiloint2double(ahi, alo);		return __hiloint2double(ahi, alo);
	}		}
	static __forceinline__ double __internal_fast_rcp(double a)		static __forceinline__ double __internal_fast_rcp(double a)
	{		{
	double e, y;		double e, y;
	float x;		float x;
	x = __double2float_rn(a);		asm ("cvt.f32.f64.rn %0,%1;" : "=f"(x) : "d"(a));
	y = (double)(1.0f/x);		asm ("rcp.approx.f32.ftz %0,%1;" : "=f"(x) : "f"(x));
			asm ("cvt.f64.f32 %0,%1;" : "=d"(y) : "f"(x));
	e = __fma_rn (-a, y, 1.0);		e = __fma_rn (-a, y, 1.0);
	e = __fma_rn ( e, e, e);		e = __fma_rn ( e, e, e);
	y = __fma_rn ( e, y, y);		y = __fma_rn ( e, y, y);
	return y;		return y;
	}		}
	/* 1152 bits of 2/PI for Payne-Hanek style argument reduction. */		/* 1152 bits of 2/PI for Payne-Hanek style argument reduction. */
	static __constant__ unsigned long long int __cudart_i2opi_d [] = {		static __constant__ unsigned long long int __cudart_i2opi_d [] = {
	0x6bfb5fb11f8d5d08ULL,		0x6bfb5fb11f8d5d08ULL,
	0x3d0739f78a5292eaULL,		0x3d0739f78a5292eaULL,
	skipping to change at line 201		skipping to change at line 216
	lo = result[2];		lo = result[2];
	if (e) {		if (e) {
	q = 64 - e;		q = 64 - e;
	hi = (hi << e) | (lo >> q);		hi = (hi << e) | (lo >> q);
	lo = (lo << e) | (result[1] >> q);		lo = (lo << e) | (result[1] >> q);
	}		}
	q = (int)(hi >> 62);		q = (int)(hi >> 62);
	/* fraction */		/* fraction */
	hi = (hi << 2) | (lo >> 62);		hi = (hi << 2) | (lo >> 62);
	lo = (lo << 2);		lo = (lo << 2);
	e = (hi | (lo > 0)) > 0x8000000000000000ULL; /* fraction >= 0.5 */		e = hi >> 63; /* fraction >= 0.5 */
	q += e;		q += e;
	if (s) q = -q;		if (s) q = -q;
	if (e) {		if (e) {
	unsigned long long int t;		unsigned long long int t;
	hi = ~hi;		hi = ~hi;
	lo = -(long long int)lo;		lo = -(long long int)lo;
	t = (lo == 0ULL);		t = (lo == 0ULL);
	hi += t;		hi += t;
	s = s ^ 0x8000000000000000ULL;		s = s ^ 0x8000000000000000ULL;
	}		}
	*quadrant = q;		*quadrant = q;
	/* normalize fraction */		/* normalize fraction */
	e = 0;		e = __clzll(hi);
	while ((long long int)hi > 0) {		if (e) {
	hi = (hi << 1) | (lo >> 63);		hi = (hi << e) | (lo >> (64 - e));
	lo = (lo << 1);
	e--;
	}		}
	lo = hi * 0xC90FDAA22168C235ULL;		lo = hi * 0xC90FDAA22168C235ULL;
	hi = __umul64hi (hi, 0xC90FDAA22168C235ULL);		hi = __umul64hi (hi, 0xC90FDAA22168C235ULL);
	if ((long long int)hi > 0) {		if ((long long int)hi > 0) {
	hi = (hi << 1) | (lo >> 63);		hi = (hi << 1) | (lo >> 63);
	lo = (lo << 1);		e++;
	e--;
	}		}
	hi = hi + (lo > 0);		ia = s | ((((unsigned long long int)(1022 - e)) << 52) +
	ia = s | ((((unsigned long long int)(e + 1022)) << 52) + (hi >> 11) +		((((hi + 1) >> 10) + 1) >> 1));
	((hi << 53) >= 0x8000000000000000ULL));
	return __longlong_as_double(ia);		return __longlong_as_double(ia);
	}		}
	/* NOTE: for an input of -0, this returns -0 */		/* NOTE: for an input of -0, this returns -0 */
	q = __double2int_rn (a * CUDART_2_OVER_PI);		q = __double2int_rn (a * CUDART_2_OVER_PI);
	j = (double)q;		j = (double)q;
	/* Constants from S. Boldo, M. Daumas, R.-C. Li: "Formally Verified Argum ent		/* Constants from S. Boldo, M. Daumas, R.-C. Li: "Formally Verified Argum ent
	* Reduction with a Fused-Multiply-Add", retrieved from the internet at		* Reduction with a Fused-Multiply-Add", retrieved from the internet at
	* http://arxiv.org/PS_cache/arxiv/pdf/0708/0708.3722v1.pdf		* http://arxiv.org/PS_cache/arxiv/pdf/0708/0708.3722v1.pdf
	*/		*/
	a = __fma_rn (-j, 1.5707963267948966e+000, a);		a = __fma_rn (-j, 1.5707963267948966e+000, a);
	skipping to change at line 395		skipping to change at line 406
	z = __internal_sin_kerneld(z);		z = __internal_sin_kerneld(z);
	}		}
	if (i & 2) {		if (i & 2) {
	z = -z;		z = -z;
	}		}
	return z;		return z;
	}		}
	static __forceinline__ double sinpi(double a)		static __forceinline__ double sinpi(double a)
	{		{
	double z;		volatile double z;
	double fi;		long long l;
	int i;		int i;
	if (__isinf(a) || (a == CUDART_ZERO)) {		if (__isinf(a)) {
	return __dmul_rn(a, CUDART_ZERO);		a = __dmul_rn(a, CUDART_ZERO); /* return NaN */
	}		}
	/* IEEE-754: sinPi(+n) is +0 and sinPi(-n) is -0 for positive integers n. */
	if (a == trunc(a)) {		if (a == trunc(a)) {
	return __longlong_as_double(__double_as_longlong(a)&0x8000000000000000U LL);		a = __longlong_as_double(__double_as_longlong(a)&0x8000000000000000ULL) ;
	}		}
	fi = rint (a * 2.0);		l = __double2ll_rn (a * 2.0f);
	z = __fma_rn (fi, -0.5, a);		i = (int)l;
			z = (double)l;
			z = __fma_rn (-z, 0.5, a);
	z = __fma_rn (z, CUDART_PI_HI, z * CUDART_PI_LO);		z = __fma_rn (z, CUDART_PI_HI, z * CUDART_PI_LO);
	i = (int)(((long long)fi) & 3);
	if (i & 1) {		if (i & 1) {
	z = __internal_cos_kerneld(z);		z = __internal_cos_kerneld(z);
	} else {		} else {
	z = __internal_sin_kerneld(z);		z = __internal_sin_kerneld(z);
	}		}
	if (i & 2) {		if (i & 2) {
	z = -z;		z = -z;
	}		}
			if (a == CUDART_ZERO) {
			z = a;
			}
			return z;
			}
			static __forceinline__ double cospi(double a)
			{
			volatile double z;
			long long l;
			int i;
			if (fabs(a) > CUDART_TWO_TO_53) {
			a = __dmul_rn (a, CUDART_ZERO);
			}
			l = __double2ll_rn (a * 2.0f);
			i = (int)l;
			z = (double)l;
			z = __fma_rn (-z, 0.5, a);
			z = __fma_rn (z, CUDART_PI_HI, z * CUDART_PI_LO);
			i++;
			if (i & 1) {
			z = __internal_cos_kerneld(z);
			} else {
			z = __internal_sin_kerneld(z);
			}
			if (i & 2) {
			z = -z;
			}
			if (z == CUDART_ZERO) {
			z = fabs (z);
			}
	return z;		return z;
	}		}
	static __forceinline__ double cos(double a)		static __forceinline__ double cos(double a)
	{		{
	double z;		double z;
	int i;		int i;
	if (__isinf(a)) {		if (__isinf(a)) {
	return CUDART_NAN;		return CUDART_NAN;
	}		}
	skipping to change at line 444		skipping to change at line 487
	z = __internal_sin_kerneld(z);		z = __internal_sin_kerneld(z);
	}		}
	if (i & 2) {		if (i & 2) {
	z = -z;		z = -z;
	}		}
	return z;		return z;
	}		}
	static __forceinline__ void sincos(double a, double *sptr, double *cptr)		static __forceinline__ void sincos(double a, double *sptr, double *cptr)
	{		{
	double t, u, s, c;		volatile double s, c;
			double t;
	int i;		int i;
	t = fabs(a);
	if ((t == CUDART_INF) || (t == CUDART_ZERO)) {		if (__isinf(a)) {
	s = __dmul_rn (a, CUDART_ZERO); /* generate NaN, zero */		a = __dmul_rn (a, CUDART_ZERO); /* return NaN */
	c = 1.0 + s; /* generate NaN, one */
	*sptr = s;
	*cptr = c;
	return;
	}		}
	t = __internal_trig_reduction_kerneld(a, &i);		a = __internal_trig_reduction_kerneld(a, &i);
	u = __internal_cos_kerneld(t);		c = __internal_cos_kerneld(a);
	t = __internal_sin_kerneld(t);		s = __internal_sin_kerneld(a);
			t = s;
	if (i & 1) {		if (i & 1) {
	s = u;		s = c;
	c = t;		c = t;
	} else {
	s = t;
	c = u;
	}		}
	if (i & 2) {		if (i & 2) {
	s = -s;		s = -s;
	}		}
	i++;		i++;
	if (i & 2) {		if (i & 2) {
	c = -c;		c = -c;
	}		}
			if (a == CUDART_ZERO) { /* preserve negative zero */
			s = a;
			}
	*sptr = s;		*sptr = s;
	*cptr = c;		*cptr = c;
	}		}
	static __forceinline__ double tan(double a)		static __forceinline__ double tan(double a)
	{		{
	double z;		double z;
	int i;		int i;
	if (__isinf(a)) {		if (__isinf(a)) {
	return __dadd_rn (a, -a); /* return NaN */		return __dadd_rn (a, -a); /* return NaN */
	skipping to change at line 822		skipping to change at line 863
	}		}
	return z;		return z;
	}		}
	t = (i < 0) ? CUDART_ZERO : CUDART_INF;		t = (i < 0) ? CUDART_ZERO : CUDART_INF;
	if (__isnan(a)) {		if (__isnan(a)) {
	t = a + a;		t = a + a;
	}		}
	return t;		return t;
	}		}
	static __forceinline__ double expm1(double a)		static __forceinline__ double __internal_expm1_scaled(double a, int scale)
	{		{
	double t, z, u;		double t, z, u;
	int i, j, k;		int i, j, k;
			k = __double2hiint(a);
			t = rint (a * CUDART_L2E);
			i = (int)t + scale;
			z = __fma_rn (t, -CUDART_LN2_HI, a);
			z = __fma_rn (t, -CUDART_LN2_LO, z);
			k = k + k;
			if ((unsigned)k < (unsigned)0x7fb3e647) {
			z = a;
			i = 0;
			}
			t = __internal_expm1_kernel(z);
			j = i;
			if (i == 1024) j--;
			u = __internal_exp2i_kernel(j);
			a = __hiloint2double(0x3ff00000 + (scale << 20), 0);
			a = u - a;
			t = __fma_rn (t, u, a);
			if (i == 1024) t = t + t;
			if (k == 0) t = z; /* preserve -0 */
			return t;
			}
			static __forceinline__ double expm1(double a)
			{
			double t;
			int k;
	k = __double2hiint(a);		k = __double2hiint(a);
	if (((unsigned)k < (unsigned)0x40862e43) || ((int)k < (int)0xc04a8000)) {		if (((unsigned)k < (unsigned)0x40862e43) || ((int)k < (int)0xc04a8000)) {
	t = rint (a * CUDART_L2E);		return __internal_expm1_scaled(a, 0);
	i = (int)t;
	z = __fma_rn (t, -CUDART_LN2_HI, a);
	z = __fma_rn (t, -CUDART_LN2_LO, z);
	k = k + k;
	if ((unsigned)k < (unsigned)0x7fb3e647) {
	z = a;
	i = 0;
	}
	t = __internal_expm1_kernel(z);
	j = i;
	if (i == 1024) j--;
	u = __internal_exp2i_kernel(j);
	a = u - 1.0;
	t = __fma_rn (t, u, a);
	if (i == 1024) t = t + t;
	if (k == 0) t = z; /* preserve -0 */
	return t;
	}		}
	t = (k < 0) ? -CUDART_ONE : CUDART_INF;		t = (k < 0) ? -CUDART_ONE : CUDART_INF;
	if (__isnan(a)) {		if (__isnan(a)) {
	t = a + a;		t = a + a;
	}		}
	return t;		return t;
	}		}
	static __forceinline__ double cosh(double a)		static __forceinline__ double cosh(double a)
	{		{
	skipping to change at line 877		skipping to change at line 927
	if (z > 0.0) a = CUDART_INF_F;		if (z > 0.0) a = CUDART_INF_F;
	return a + a;		return a + a;
	}		}
	}		}
	static __forceinline__ double sinh(double a)		static __forceinline__ double sinh(double a)
	{		{
	double s, z;		double s, z;
	s = a;		s = a;
	a = fabs(a);		a = fabs(a);
	if (a < 1.0) { /* danger of catastrophic cancellation */		if (__double2hiint(a) < 0x3ff00000) { /* risk of catastrophic cancellatio n */
	double a2 = a * a;		double a2 = a * a;
	/* approximate sinh(x) on [0,1] with a polynomial */		/* approximate sinh(x) on [0,1] with a polynomial */
	z = 1.632386098183803E-010;		z = 1.632386098183803E-010;
	z = __fma_rn (z, a2, 2.504854501385687E-008);		z = __fma_rn (z, a2, 2.504854501385687E-008);
	z = __fma_rn (z, a2, 2.755734274788706E-006);		z = __fma_rn (z, a2, 2.755734274788706E-006);
	z = __fma_rn (z, a2, 1.984126976294102E-004);		z = __fma_rn (z, a2, 1.984126976294102E-004);
	z = __fma_rn (z, a2, 8.333333333452911E-003);		z = __fma_rn (z, a2, 8.333333333452911E-003);
	z = __fma_rn (z, a2, 1.666666666666606E-001);		z = __fma_rn (z, a2, 1.666666666666606E-001);
	z = z * a2;		z = z * a2;
	z = __fma_rn (z, a, a);		z = __fma_rn (z, a, a);
	} else if (a < 2.0) { /* work around accuracy issue in vicinity of 1.4
	*/
	z = expm1(a);
	z = __internal_half (z + z / (z + 1.0));
	} else {		} else {
	z = __internal_exp_kernel(a, -1);		z = __internal_expm1_scaled (a, -1);
	z = z + (1.0 / (-4.0 * z));		z = z + z / __fma_rn (2.0, z, 1.0);
	if (a >= CUDART_LN2_X_1025) {		if (a >= CUDART_LN2_X_1025) {
	z = CUDART_INF; /* overflow -> infinity */		z = CUDART_INF; /* overflow -> infinity */
	}		}
	}		}
	z = __internal_copysign_pos(z, s);		z = __internal_copysign_pos(z, s);
	return z;		return z;
	}		}
	static __forceinline__ double tanh(double a)		static __forceinline__ double tanh(double a)
	{		{
	skipping to change at line 967		skipping to change at line 1014
	t = __fma_rn (t, a, a);		t = __fma_rn (t, a, a);
	return t;		return t;
	}		}
	static __forceinline__ double atan2(double a, double b)		static __forceinline__ double atan2(double a, double b)
	{		{
	double t0, t1, t3;		double t0, t1, t3;
	if (__isnan(a) || __isnan(b)) {		if (__isnan(a) || __isnan(b)) {
	return a + b;		return a + b;
	}		}
	/* reduce arguments to first octant */
	/* r = (|x| < |y|) ? (|x| / |y|) : (|y| / |x|) */
	t3 = fabs(b);		t3 = fabs(b);
	t1 = fabs(a);		t1 = fabs(a);
	if (t3 == 0.0 && t1 == 0.0) {		if (t3 == 0.0 && t1 == 0.0) {
	t3 = (__double2hiint(b) < 0) ? CUDART_PI : 0;		t3 = (__double2hiint(b) < 0) ? CUDART_PI : 0;
	} else if (__isinf(t3) && __isinf(t1)) {		} else if (__isinf(t3) && __isinf(t1)) {
	t3 = (__double2hiint(b) < 0) ? CUDART_3PIO4 : CUDART_PIO4;		t3 = (__double2hiint(b) < 0) ? CUDART_3PIO4 : CUDART_PIO4;
	} else {		} else {
	t0 = fmax (t1, t3);		t0 = fmax (t1, t3);
	t1 = fmin (t1, t3);		t1 = fmin (t1, t3);
	t3 = t1 / t0;		t3 = t1 / t0;
	t3 = __internal_atan_kernel(t3);		t3 = __internal_atan_kernel(t3);
	/* Map result according to octant. */		/* Map result according to octant. */
	if (fabs(a) > fabs(b)) t3 = CUDART_PIO2 - t3;		if (fabs(a) > fabs(b)) t3 = CUDART_PIO2 - t3;
	if (b < 0.0) t3 = CUDART_PI - t3;		if (b < 0.0) t3 = CUDART_PI - t3;
	}		}
	t3 = __internal_copysign_pos(t3, a);		t3 = __internal_copysign_pos(t3, a);
	return t3;		return t3;
	}		}
	static __forceinline__ double atan(double a)		static __forceinline__ double atan(double a)
	{		{
	double t0, t1;		double t0, t1;
	/* reduce argument to first octant */		/* reduce argument to first octant */
	t0 = fabs(a);		t0 = fabs(a);
	skipping to change at line 1546		skipping to change at line 1591
	q = __fma_rn (q, t, 1.2964671850944981713e-6);		q = __fma_rn (q, t, 1.2964671850944981713e-6);
	q = __fma_rn (q, t, 4.6156017600933592558e-9);		q = __fma_rn (q, t, 4.6156017600933592558e-9);
	q = __fma_rn (q, t, 4.5344687377088206783e-12);		q = __fma_rn (q, t, 4.5344687377088206783e-12);
	t = p / (q * t);		t = p / (q * t);
	}		}
	return t;		return t;
	}		}
	static __forceinline__ double erfc(double a)		static __forceinline__ double erfc(double a)
	{		{
	double p, q, h, l;		/*
	int ahi;		* This implementation of erfc() is based on the algorithm in: M. M. Shep
			herd
	ahi = __double2hiint(a);		* and J. G. Laframboise, "Chebyshev Approximation of (1 + 2x)exp(x^2)erf
	if (ahi < (int)0x3fea0400) { /* 1665/2048 */		c x
	return 1.0 - erf(a);		* in 0 <= x < INF", Mathematics of Computation, Vol. 36, No. 153, Januar
	}		y
	if (ahi < (int)0x40140000) { /* 5.0 */		* 1981, pp. 249-253. For the core approximation, the input domain [0,INF
	/* On the interval [1665/2048, 5.0] the following approximation is used		] is
	:		* transformed via (x-k) / (x+k) where k is a precision-dependent constan
	erfc(a) = (1.0 + 1/a * r(1/a)) * 1/a * 0.5 * exp(-a*a), where the ra		t.
	nge		* Here, we choose k = 4.0, so input domain [0,27.3] is transformed to th
	of r(1/a) is approximately [-0.17, 0.11]. r(1/a) is computed by rati		e
	onal		* core approximation domain [-1,0.744409].
	approximation.		*/
	*/		double x, t1, t2, t3, t4;
	double t;		x = fabs(a);
			/* (1+2*x)*exp(x*x)*erfc(x) */
	t = __internal_fast_rcp(a);		/* t2 = (x-4.0)/(x+4.0), transforming [0,INF] to [-1,+1] */
	p = -1.0000000252849461E+000;		t1 = x - 4.0;
	p = __fma_rn (p, t, -7.3398971987771156E-001);		t2 = x + 4.0;
	p = __fma_rn (p, t, -1.4685633784433072E-001);		t2 = 1.0 / t2;
	p = __fma_rn (p, t, 1.2963557011001836E-001);		t3 = t1 * t2;
	p = __fma_rn (p, t, 1.0901177826674287E-001);		t1 = __fma_rn (-4.0, t3 + 1.0, x);
	p = __fma_rn (p, t, 3.9250612663155882E-002);		t1 = __fma_rn (-t3, x, t1);
	p = __fma_rn (p, t, 7.5883167167654269E-003);		t2 = __fma_rn (t2, t1, t3);
	p = __fma_rn (p, t, 6.6438196820856965E-004);		/* approximate on [-1, 0.744409] */
	q = t + 2.7339900293714838E+000;		t1 = -3.5602694826817400E-010;
	q = __fma_rn (q, t, 3.3580762542361291E+000);		t1 = __fma_rn (t1, t2, -9.7239122591447274E-009);
	q = __fma_rn (q, t, 2.4165688909166021E+000);		t1 = __fma_rn (t1, t2, -8.9350224851649119E-009);
	q = __fma_rn (q, t, 1.1092158770004934E+000);		t1 = __fma_rn (t1, t2, 1.0404430921625484E-007);
	q = __fma_rn (q, t, 3.2845571970789467E-001);		t1 = __fma_rn (t1, t2, 5.8806698585341259E-008);
	q = __fma_rn (q, t, 5.9110343116276186E-002);		t1 = __fma_rn (t1, t2, -8.2147414929116908E-007);
	q = __fma_rn (q, t, 5.1750858802842702E-003);		t1 = __fma_rn (t1, t2, 3.0956409853306241E-007);
	q = __fma_rn (q, t, 1.2937416364002241E-009);		t1 = __fma_rn (t1, t2, 5.7087871844325649E-006);
	q = __internal_fast_rcp(q);		t1 = __fma_rn (t1, t2, -1.1231787437600085E-005);
	p = p * q;		t1 = __fma_rn (t1, t2, -2.4399558857200190E-005);
	p = p * t;		t1 = __fma_rn (t1, t2, 1.5062557169571788E-004);
	h = a * a;		t1 = __fma_rn (t1, t2, -1.9925637684786154E-004);
	l = __fma_rn (a, a, -h);		t1 = __fma_rn (t1, t2, -7.5777429182785833E-004);
	q = __internal_exp_kernel(-h, -1);		t1 = __fma_rn (t1, t2, 5.0319698792599572E-003);
	q = __fma_rn (l, -q, q);		t1 = __fma_rn (t1, t2, -1.6197733895953217E-002);
	p = __fma_rn (p, q, q);		t1 = __fma_rn (t1, t2, 3.7167515553018733E-002);
	p = p * t;		t1 = __fma_rn (t1, t2, -6.6330365827532434E-002);
	} else {		t1 = __fma_rn (t1, t2, 9.3732834997115544E-002);
	/* max error 4 ulps on [5, 27.3] */		t1 = __fma_rn (t1, t2, -1.0103906603555676E-001);
	double ooa, ooasq;		t1 = __fma_rn (t1, t2, 6.8097054254735140E-002);
			t1 = __fma_rn (t1, t2, 1.5379652102605428E-002);
	ooa = __internal_fast_rcp(a);		t1 = __fma_rn (t1, t2, -1.3962111684056291E-001);
	ooasq = ooa * ooa;		t1 = __fma_rn (t1, t2, 1.2329951186255526E+000);
	p = -4.0025406686930527E+005;		/* (1+2*x)*exp(x*x)*erfc(x) / (1+2*x) = exp(x*x)*erfc(x) */
	p = __fma_rn (p, ooasq, 1.4420582543942123E+005);		t2 = __fma_rn (2.0, x, 1.0);
	p = __fma_rn (p, ooasq, -2.7664185780951841E+004);		t2 = 1.0 / t2;
	p = __fma_rn (p, ooasq, 4.1144611644767283E+003);		t3 = t1 * t2;
	p = __fma_rn (p, ooasq, -5.8706000519209351E+002);		t4 = __fma_rn (x, -2.0*t3, t1);
	p = __fma_rn (p, ooasq, 9.1490086446323375E+001);		t4 = t4 - t3;
	p = __fma_rn (p, ooasq, -1.6659491387740221E+001);		t1 = __fma_rn (t4, t2, t3);
	p = __fma_rn (p, ooasq, 3.7024804085481784E+000);		/* exp(-x*x) * exp(x*x)*erfc(x) = erfc(x) */
	p = __fma_rn (p, ooasq, -1.0578553994424316E+000);		t2 = -x * x;
	p = __fma_rn (p, ooasq, 4.2314218745087778E-001);		t3 = __internal_exp_kernel (t2, 0);
	p = __fma_rn (p, ooasq, -2.8209479177354962E-001);		t2 = __fma_rn (-x, x, -t2);
	p = __fma_rn (p, ooasq, 5.6418958354775606E-001);		t2 = __fma_rn (t3, t2, t3);
	h = a * a;		t1 = t1 * t2;
	l = __fma_rn (a, a, -h);		if (x > 27.3) t1 = 0.0;
	q = __internal_exp_kernel(-h, 0);		return (__double2hiint(a) < 0) ? (2.0 - t1) : t1;
	q = __fma_rn (l, -q, q);
	p = p * ooa;
	p = p * q;
	if (a > 27.3) {
	p = 0.0;
	}
	}
	return p;
	}		}
	/* approximate 1.0/(a*gamma(a)) on [-0.5,0.5] */		/* approximate 1.0/(a*gamma(a)) on [-0.5,0.5] */
	static __forceinline__ double __internal_tgamma_kernel(double a)		static __forceinline__ double __internal_tgamma_kernel(double a)
	{		{
	double t;		double t;
	t = -4.42689340712524750E-010;		t = -4.42689340712524750E-010;
	t = __fma_rn (t, a, -2.02665918466589540E-007);		t = __fma_rn (t, a, -2.02665918466589540E-007);
	t = __fma_rn (t, a, 1.13812117211195270E-006);		t = __fma_rn (t, a, 1.13812117211195270E-006);
	t = __fma_rn (t, a, -1.25077348166307480E-006);		t = __fma_rn (t, a, -1.25077348166307480E-006);
End of changes. 33 change blocks.
	167 lines changed or deleted		202 lines changed or added

	sm_11_atomic_functions.h		sm_11_atomic_functions.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(__SM_11_ATOMIC_FUNCTIONS_H__)		#if !defined(__SM_11_ATOMIC_FUNCTIONS_H__)
	#define __SM_11_ATOMIC_FUNCTIONS_H__		#define __SM_11_ATOMIC_FUNCTIONS_H__
	#if defined(__cplusplus) && defined(__CUDACC__)		#if defined(__cplusplus) && defined(__CUDACC__)
	#if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 110		#if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 110
	/************************************************************************** *****		/************************************************************************** *****
End of changes. 6 change blocks.
	28 lines changed or deleted		41 lines changed or added

	sm_12_atomic_functions.h		sm_12_atomic_functions.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(__SM_12_ATOMIC_FUNCTIONS_H__)		#if !defined(__SM_12_ATOMIC_FUNCTIONS_H__)
	#define __SM_12_ATOMIC_FUNCTIONS_H__		#define __SM_12_ATOMIC_FUNCTIONS_H__
	#if defined(__cplusplus) && defined(__CUDACC__)		#if defined(__cplusplus) && defined(__CUDACC__)
	#if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 120		#if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 120
	/************************************************************************** *****		/************************************************************************** *****
End of changes. 6 change blocks.
	28 lines changed or deleted		41 lines changed or added

	sm_13_double_functions.h		sm_13_double_functions.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(__SM_13_DOUBLE_FUNCTIONS_H__)		#if !defined(__SM_13_DOUBLE_FUNCTIONS_H__)
	#define __SM_13_DOUBLE_FUNCTIONS_H__		#define __SM_13_DOUBLE_FUNCTIONS_H__
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
End of changes. 6 change blocks.
	28 lines changed or deleted		41 lines changed or added

	sm_20_atomic_functions.h		sm_20_atomic_functions.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(__SM_20_ATOMIC_FUNCTIONS_H__)		#if !defined(__SM_20_ATOMIC_FUNCTIONS_H__)
	#define __SM_20_ATOMIC_FUNCTIONS_H__		#define __SM_20_ATOMIC_FUNCTIONS_H__
	#if defined(__cplusplus) && defined(__CUDACC__)		#if defined(__cplusplus) && defined(__CUDACC__)
	#if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 200		#if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 200
	/************************************************************************** *****		/************************************************************************** *****
End of changes. 6 change blocks.
	28 lines changed or deleted		41 lines changed or added

	sm_20_intrinsics.h		sm_20_intrinsics.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(__SM_20_INTRINSICS_H__)		#if !defined(__SM_20_INTRINSICS_H__)
	#define __SM_20_INTRINSICS_H__		#define __SM_20_INTRINSICS_H__
	#if defined(__cplusplus) && defined(__CUDACC__)		#if defined(__cplusplus) && defined(__CUDACC__)
	#if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 200		#if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 200
	/************************************************************************** *****		/************************************************************************** *****
End of changes. 6 change blocks.
	28 lines changed or deleted		41 lines changed or added

	surface_functions.h		surface_functions.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(__SURFACE_FUNCTIONS_H__)		#if !defined(__SURFACE_FUNCTIONS_H__)
	#define __SURFACE_FUNCTIONS_H__		#define __SURFACE_FUNCTIONS_H__
	#if defined(__cplusplus) && defined(__CUDACC__)		#if defined(__cplusplus) && defined(__CUDACC__)
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	skipping to change at line 83		skipping to change at line 97
	extern __device__ uint1 __surf1Dreadu1(surface<void, 1> t, int x, enum cudaSurfaceBoundaryMode mode);		extern __device__ uint1 __surf1Dreadu1(surface<void, 1> t, int x, enum cudaSurfaceBoundaryMode mode);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __device__ uint2 __surf1Dreadu2(surface<void, 1> t, int x, enum cudaSurfaceBoundaryMode mode);		extern __device__ uint2 __surf1Dreadu2(surface<void, 1> t, int x, enum cudaSurfaceBoundaryMode mode);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __device__ uint4 __surf1Dreadu4(surface<void, 1> t, int x, enum cudaSurfaceBoundaryMode mode);		extern __device__ uint4 __surf1Dreadu4(surface<void, 1> t, int x, enum cudaSurfaceBoundaryMode mode);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __device__ ulonglong1 __surf1Dreadl1(surface<void, 1> t, int x, enum cudaSurfaceBoundaryMode mode);		extern __device__ ulonglong1 __surf1Dreadl1(surface<void, 1> t, int x, enum cudaSurfaceBoundaryMode mode);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __device__ ulonglong2 __surf1Dreadl2(surface<void, 1> t, int x, enum cudaSurfaceBoundaryMode mode);		extern __device__ ulonglong2 __surf1Dreadl2(surface<void, 1> t, int x, enum cudaSurfaceBoundaryMode mode);
	#if __CUDA_ARCH__ >= 200		#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 200
	#define __surfModeSwitch(surf, x, mode, type) \		#define __surfModeSwitch(surf, x, mode, type) \
	((mode == cudaBoundaryModeZero) ? __surf1Dread##type(surf, x, cuda BoundaryModeZero ) : \		((mode == cudaBoundaryModeZero) ? __surf1Dread##type(surf, x, cuda BoundaryModeZero ) : \
	(mode == cudaBoundaryModeClamp) ? __surf1Dread##type(surf, x, cuda BoundaryModeClamp) : \		(mode == cudaBoundaryModeClamp) ? __surf1Dread##type(surf, x, cuda BoundaryModeClamp) : \
	__surf1Dread##type(surf, x, cuda BoundaryModeTrap ))		__surf1Dread##type(surf, x, cuda BoundaryModeTrap ))
	#else /* __CUDA_ARCH__ >= 200 */		#else /* CUDA_ARCH && __CUDA_ARCH__ >= 200 */
	#define __surfModeSwitch(surf, x, mode, type) \		#define __surfModeSwitch(surf, x, mode, type) \
	__surf1Dread##type(surf, x, cudaBoundaryModeTrap)		__surf1Dread##type(surf, x, cudaBoundaryModeTrap)
	#endif /* __CUDA_ARCH__ >= 200 */		#endif /* CUDA_ARCH && __CUDA_ARCH__ >= 200 */
	template<class T>		template<class T>
	static __inline__ __device__ void surf1Dread(T *res, surface<void, 1> surf, int x, int s, enum cudaSurfaceBoundaryMode mode = cudaBoundaryModeTrap)		static __inline__ __device__ void surf1Dread(T *res, surface<void, 1> surf, int x, int s, enum cudaSurfaceBoundaryMode mode = cudaBoundaryModeTrap)
	{		{
	(s == 1) ? (void)(*(uchar1 *)res = __surfModeSwitch(surf, x, mode, c1)) :		(s == 1) ? (void)(*(uchar1 *)res = __surfModeSwitch(surf, x, mode, c1)) :
	(s == 2) ? (void)(*(ushort1*)res = __surfModeSwitch(surf, x, mode, s1)) :		(s == 2) ? (void)(*(ushort1*)res = __surfModeSwitch(surf, x, mode, s1)) :
	(s == 4) ? (void)(*(uint1 *)res = __surfModeSwitch(surf, x, mode, u1)) :		(s == 4) ? (void)(*(uint1 *)res = __surfModeSwitch(surf, x, mode, u1)) :
	(s == 8) ? (void)(*(uint2 *)res = __surfModeSwitch(surf, x, mode, u2)) :		(s == 8) ? (void)(*(uint2 *)res = __surfModeSwitch(surf, x, mode, u2)) :
	(s == 16) ? (void)(*(uint4 *)res = __surfModeSwitch(surf, x, mode, u4)) :		(s == 16) ? (void)(*(uint4 *)res = __surfModeSwitch(surf, x, mode, u4)) :
	(void)0;		(void)0;
	skipping to change at line 443		skipping to change at line 457
	extern __device__ uint1 __surf2Dreadu1(surface<void, 2> t, int x, int y, enum cudaSurfaceBoundaryMode mode);		extern __device__ uint1 __surf2Dreadu1(surface<void, 2> t, int x, int y, enum cudaSurfaceBoundaryMode mode);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __device__ uint2 __surf2Dreadu2(surface<void, 2> t, int x, int y, enum cudaSurfaceBoundaryMode mode);		extern __device__ uint2 __surf2Dreadu2(surface<void, 2> t, int x, int y, enum cudaSurfaceBoundaryMode mode);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __device__ uint4 __surf2Dreadu4(surface<void, 2> t, int x, int y, enum cudaSurfaceBoundaryMode mode);		extern __device__ uint4 __surf2Dreadu4(surface<void, 2> t, int x, int y, enum cudaSurfaceBoundaryMode mode);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __device__ ulonglong1 __surf2Dreadl1(surface<void, 2> t, int x, int y, enum cudaSurfaceBoundaryMode mode);		extern __device__ ulonglong1 __surf2Dreadl1(surface<void, 2> t, int x, int y, enum cudaSurfaceBoundaryMode mode);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __device__ ulonglong2 __surf2Dreadl2(surface<void, 2> t, int x, int y, enum cudaSurfaceBoundaryMode mode);		extern __device__ ulonglong2 __surf2Dreadl2(surface<void, 2> t, int x, int y, enum cudaSurfaceBoundaryMode mode);
	#if __CUDA_ARCH__ >= 200		#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 200
	#define __surfModeSwitch(surf, x, y, mode, type) \		#define __surfModeSwitch(surf, x, y, mode, type) \
	((mode == cudaBoundaryModeZero) ? __surf2Dread##type(surf, x, y, c udaBoundaryModeZero ) : \		((mode == cudaBoundaryModeZero) ? __surf2Dread##type(surf, x, y, c udaBoundaryModeZero ) : \
	(mode == cudaBoundaryModeClamp) ? __surf2Dread##type(surf, x, y, c udaBoundaryModeClamp) : \		(mode == cudaBoundaryModeClamp) ? __surf2Dread##type(surf, x, y, c udaBoundaryModeClamp) : \
	__surf2Dread##type(surf, x, y, c udaBoundaryModeTrap ))		__surf2Dread##type(surf, x, y, c udaBoundaryModeTrap ))
	#else /* __CUDA_ARCH__ >= 200 */		#else /* __CUDA_ARCH__ && __CUDA_ARCH__ &gt;= 200 */
	#define __surfModeSwitch(surf, x, y, mode, type) \		#define __surfModeSwitch(surf, x, y, mode, type) \
	__surf2Dread##type(surf, x, y, cudaBoundaryModeTrap)		__surf2Dread##type(surf, x, y, cudaBoundaryModeTrap)
	#endif /* __CUDA_ARCH__ >= 200 */		#endif /* __CUDA_ARCH__ && __CUDA_ARCH__ &gt;= 200 */
	template<class T>		template<class T>
	static __inline__ __device__ void surf2Dread(T *res, surface<void, 2> surf, int x, int y, int s, enum cudaSurfaceBoundaryMode mode = cudaBoundaryModeT rap)		static __inline__ __device__ void surf2Dread(T *res, surface<void, 2> surf, int x, int y, int s, enum cudaSurfaceBoundaryMode mode = cudaBoundaryModeT rap)
	{		{
	(s == 1) ? (void)(*(uchar1 *)res = __surfModeSwitch(surf, x, y, mode, c1 )) :		(s == 1) ? (void)(*(uchar1 *)res = __surfModeSwitch(surf, x, y, mode, c1 )) :
	(s == 2) ? (void)(*(ushort1*)res = __surfModeSwitch(surf, x, y, mode, s1 )) :		(s == 2) ? (void)(*(ushort1*)res = __surfModeSwitch(surf, x, y, mode, s1 )) :
	(s == 4) ? (void)(*(uint1 *)res = __surfModeSwitch(surf, x, y, mode, u1 )) :		(s == 4) ? (void)(*(uint1 *)res = __surfModeSwitch(surf, x, y, mode, u1 )) :
	(s == 8) ? (void)(*(uint2 *)res = __surfModeSwitch(surf, x, y, mode, u2 )) :		(s == 8) ? (void)(*(uint2 *)res = __surfModeSwitch(surf, x, y, mode, u2 )) :
	(s == 16) ? (void)(*(uint4 *)res = __surfModeSwitch(surf, x, y, mode, u4 )) :		(s == 16) ? (void)(*(uint4 *)res = __surfModeSwitch(surf, x, y, mode, u4 )) :
	(void)0;		(void)0;
	skipping to change at line 803		skipping to change at line 817
	extern __device__ void __surf1Dwriteu1( uint1 val, surface<void, 1> t, int x, enum cudaSurfaceBoundaryMode mode);		extern __device__ void __surf1Dwriteu1( uint1 val, surface<void, 1> t, int x, enum cudaSurfaceBoundaryMode mode);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __device__ void __surf1Dwriteu2( uint2 val, surface<void, 1> t, int x, enum cudaSurfaceBoundaryMode mode);		extern __device__ void __surf1Dwriteu2( uint2 val, surface<void, 1> t, int x, enum cudaSurfaceBoundaryMode mode);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __device__ void __surf1Dwriteu4( uint4 val, surface<void, 1> t, int x, enum cudaSurfaceBoundaryMode mode);		extern __device__ void __surf1Dwriteu4( uint4 val, surface<void, 1> t, int x, enum cudaSurfaceBoundaryMode mode);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __device__ void __surf1Dwritel1(ulonglong1 val, surface<void, 1> t, int x, enum cudaSurfaceBoundaryMode mode);		extern __device__ void __surf1Dwritel1(ulonglong1 val, surface<void, 1> t, int x, enum cudaSurfaceBoundaryMode mode);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __device__ void __surf1Dwritel2(ulonglong2 val, surface<void, 1> t, int x, enum cudaSurfaceBoundaryMode mode);		extern __device__ void __surf1Dwritel2(ulonglong2 val, surface<void, 1> t, int x, enum cudaSurfaceBoundaryMode mode);
	#if __CUDA_ARCH__ >= 200		#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 200
	#define __surfModeSwitch(val, surf, x, mode, type) \		#define __surfModeSwitch(val, surf, x, mode, type) \
	((mode == cudaBoundaryModeZero) ? __surf1Dwrite##type(val, surf, x , cudaBoundaryModeZero ) : \		((mode == cudaBoundaryModeZero) ? __surf1Dwrite##type(val, surf, x , cudaBoundaryModeZero ) : \
	(mode == cudaBoundaryModeClamp) ? __surf1Dwrite##type(val, surf, x , cudaBoundaryModeClamp) : \		(mode == cudaBoundaryModeClamp) ? __surf1Dwrite##type(val, surf, x , cudaBoundaryModeClamp) : \
	__surf1Dwrite##type(val, surf, x , cudaBoundaryModeTrap ))		__surf1Dwrite##type(val, surf, x , cudaBoundaryModeTrap ))
	#else /* __CUDA_ARCH__ >= 200 */		#else /* __CUDA_ARCH__ && __CUDA_ARCH__ &gt;= 200 */
	#define __surfModeSwitch(val, surf, x, mode, type) \		#define __surfModeSwitch(val, surf, x, mode, type) \
	__surf1Dwrite##type(val, surf, x, cudaBoundaryModeTrap)		__surf1Dwrite##type(val, surf, x, cudaBoundaryModeTrap)
	#endif /* __CUDA_ARCH__ >= 200 */		#endif /* __CUDA_ARCH__ && __CUDA_ARCH__ &gt;= 200 */
	template<class T>		template<class T>
	static __inline__ __device__ void surf1Dwrite(T val, surface<void, 1> surf, int x, int s, enum cudaSurfaceBoundaryMode mode = cudaBoundaryModeTrap)		static __inline__ __device__ void surf1Dwrite(T val, surface<void, 1> surf, int x, int s, enum cudaSurfaceBoundaryMode mode = cudaBoundaryModeTrap)
	{		{
	union {		union {
	T val;		T val;
	uchar1 c1;		uchar1 c1;
	ushort1 s1;		ushort1 s1;
	uint1 u1;		uint1 u1;
	uint2 u2;		uint2 u2;
	skipping to change at line 1095		skipping to change at line 1109
	extern __device__ void __surf2Dwriteu1( uint1 val, surface<void, 2> t, int x, int y, enum cudaSurfaceBoundaryMode mode);		extern __device__ void __surf2Dwriteu1( uint1 val, surface<void, 2> t, int x, int y, enum cudaSurfaceBoundaryMode mode);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __device__ void __surf2Dwriteu2( uint2 val, surface<void, 2> t, int x, int y, enum cudaSurfaceBoundaryMode mode);		extern __device__ void __surf2Dwriteu2( uint2 val, surface<void, 2> t, int x, int y, enum cudaSurfaceBoundaryMode mode);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __device__ void __surf2Dwriteu4( uint4 val, surface<void, 2> t, int x, int y, enum cudaSurfaceBoundaryMode mode);		extern __device__ void __surf2Dwriteu4( uint4 val, surface<void, 2> t, int x, int y, enum cudaSurfaceBoundaryMode mode);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __device__ void __surf2Dwritel1(ulonglong1 val, surface<void, 2> t, int x, int y, enum cudaSurfaceBoundaryMode mode);		extern __device__ void __surf2Dwritel1(ulonglong1 val, surface<void, 2> t, int x, int y, enum cudaSurfaceBoundaryMode mode);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	extern __device__ void __surf2Dwritel2(ulonglong2 val, surface<void, 2> t, int x, int y, enum cudaSurfaceBoundaryMode mode);		extern __device__ void __surf2Dwritel2(ulonglong2 val, surface<void, 2> t, int x, int y, enum cudaSurfaceBoundaryMode mode);
	#if __CUDA_ARCH__ >= 200		#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 200
	#define __surfModeSwitch(val, surf, x, y, mode, type) \		#define __surfModeSwitch(val, surf, x, y, mode, type) \
	((mode == cudaBoundaryModeZero) ? __surf2Dwrite##type(val, surf, x , y, cudaBoundaryModeZero ) : \		((mode == cudaBoundaryModeZero) ? __surf2Dwrite##type(val, surf, x , y, cudaBoundaryModeZero ) : \
	(mode == cudaBoundaryModeClamp) ? __surf2Dwrite##type(val, surf, x , y, cudaBoundaryModeClamp) : \		(mode == cudaBoundaryModeClamp) ? __surf2Dwrite##type(val, surf, x , y, cudaBoundaryModeClamp) : \
	__surf2Dwrite##type(val, surf, x , y, cudaBoundaryModeTrap ))		__surf2Dwrite##type(val, surf, x , y, cudaBoundaryModeTrap ))
	#else /* __CUDA_ARCH__ >= 200 */		#else /* __CUDA_ARCH__ && __CUDA_ARCH__ &gt;= 200 */
	#define __surfModeSwitch(val, surf, x, y, mode, type) \		#define __surfModeSwitch(val, surf, x, y, mode, type) \
	__surf2Dwrite##type(val, surf, x, y, cudaBoundaryModeTrap)		__surf2Dwrite##type(val, surf, x, y, cudaBoundaryModeTrap)
	#endif /* __CUDA_ARCH__ >= 200 */		#endif /* __CUDA_ARCH__ && __CUDA_ARCH__ &gt;= 200 */
	template<class T>		template<class T>
	static __inline__ __device__ void surf2Dwrite(T val, surface<void, 2> surf, int x, int y, int s, enum cudaSurfaceBoundaryMode mode = cudaBoundaryModeT rap)		static __inline__ __device__ void surf2Dwrite(T val, surface<void, 2> surf, int x, int y, int s, enum cudaSurfaceBoundaryMode mode = cudaBoundaryModeT rap)
	{		{
	union {		union {
	T val;		T val;
	uchar1 c1;		uchar1 c1;
	ushort1 s1;		ushort1 s1;
	uint1 u1;		uint1 u1;
	uint2 u2;		uint2 u2;
End of changes. 18 change blocks.
	40 lines changed or deleted		53 lines changed or added

	surface_types.h		surface_types.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(__SURFACE_TYPES_H__)		#if !defined(__SURFACE_TYPES_H__)
	#define __SURFACE_TYPES_H__		#define __SURFACE_TYPES_H__
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
End of changes. 6 change blocks.
	28 lines changed or deleted		41 lines changed or added

	texture_fetch_functions.h		texture_fetch_functions.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(__TEXTURE_FETCH_FUNCTIONS_H__)		#if !defined(__TEXTURE_FETCH_FUNCTIONS_H__)
	#define __TEXTURE_FETCH_FUNCTIONS_H__		#define __TEXTURE_FETCH_FUNCTIONS_H__
	#if defined(__cplusplus) && defined(__CUDACC__)		#if defined(__cplusplus) && defined(__CUDACC__)
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	skipping to change at line 61		skipping to change at line 75
	#include "vector_functions.h"		#include "vector_functions.h"
	#include "vector_types.h"		#include "vector_types.h"
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	template<class T, enum cudaTextureReadMode readMode> extern __device__ uin t4 __utexfetchi(texture<T, 1, readMode> t, int4 i);		template<class T, enum cudaTextureReadMode readMode> extern __device__ uin t4 __utexfetchi(texture<T, cudaTextureType1D, readMode> t, int4 i);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	template<class T, enum cudaTextureReadMode readMode> extern __device__ in t4 __itexfetchi(texture<T, 1, readMode> t, int4 i);		template<class T, enum cudaTextureReadMode readMode> extern __device__ in t4 __itexfetchi(texture<T, cudaTextureType1D, readMode> t, int4 i);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	template<class T, enum cudaTextureReadMode readMode> extern __device__ floa t4 __ftexfetchi(texture<T, 1, readMode> t, int4 i);		template<class T, enum cudaTextureReadMode readMode> extern __device__ floa t4 __ftexfetchi(texture<T, cudaTextureType1D, readMode> t, int4 i);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	template<class T, int dim, enum cudaTextureReadMode readMode> extern __devi ce__ uint4 __utexfetch(texture<T, dim, readMode> t, float4 i, int d = dim) ;		template<class T, int texType, enum cudaTextureReadMode readMode> extern __ device__ uint4 __utexfetch(texture<T, texType, readMode> t, float4 i, int d = texType);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	template<class T, int dim, enum cudaTextureReadMode readMode> extern __devi ce__ int4 __itexfetch(texture<T, dim, readMode> t, float4 i, int d = dim) ;		template<class T, int texType, enum cudaTextureReadMode readMode> extern __ device__ int4 __itexfetch(texture<T, texType, readMode> t, float4 i, int d = texType);
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	template<class T, int dim, enum cudaTextureReadMode readMode> extern __devi		template<class T, int texType, enum cudaTextureReadMode readMode> extern __
	ce__ float4 __ftexfetch(texture<T, dim, readMode> t, float4 i, int d = dim)		device__ float4 __ftexfetch(texture<T, texType, readMode> t, float4 i, int
	;		d = texType);
			/*DEVICE_BUILTIN*/
			template<class T, int texType, enum cudaTextureReadMode readMode> extern __
			device__ uint4 __utexfetchl(texture<T, texType, readMode> t, float4 i, int
			l, int d = (texType & 0xF));
			/*DEVICE_BUILTIN*/
			template<class T, int texType, enum cudaTextureReadMode readMode> extern __
			device__ int4 __itexfetchl(texture<T, texType, readMode> t, float4 i, int
			l, int d = (texType & 0xF));
			/*DEVICE_BUILTIN*/
			template<class T, int texType, enum cudaTextureReadMode readMode> extern __
			device__ float4 __ftexfetchl(texture<T, texType, readMode> t, float4 i, int
			l, int d = (texType & 0xF));
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	static __inline__ __device__ char tex1Dfetch(texture<char, 1, cudaReadModeE lementType> t, int x)		static __inline__ __device__ char tex1Dfetch(texture<char, cudaTextureType1 D, cudaReadModeElementType> t, int x)
	{		{
	#if __SIGNED_CHARS__		#if defined(_CHAR_UNSIGNED) || defined(__CHAR_UNSIGNED__)
	int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));
	#else
	uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));		uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));
	#endif		#else /* _CHAR_UNSIGNED || __CHAR_UNSIGNED__ */
			int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));
			#endif /* _CHAR_UNSIGNED || __CHAR_UNSIGNED__ */
	return (char)v.x;		return (char)v.x;
	}		}
	static __inline__ __device__ signed char tex1Dfetch(texture<signed char, 1, cudaReadModeElementType> t, int x)		static __inline__ __device__ signed char tex1Dfetch(texture<signed char, cu daTextureType1D, cudaReadModeElementType> t, int x)
	{		{
	int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));		int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));
	return (signed char)v.x;		return (signed char)v.x;
	}		}
	static __inline__ __device__ unsigned char tex1Dfetch(texture<unsigned char , 1, cudaReadModeElementType> t, int x)		static __inline__ __device__ unsigned char tex1Dfetch(texture<unsigned char , cudaTextureType1D, cudaReadModeElementType> t, int x)
	{		{
	uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));		uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));
	return (unsigned char)v.x;		return (unsigned char)v.x;
	}		}
	static __inline__ __device__ char1 tex1Dfetch(texture<char1, 1, cudaReadMod eElementType> t, int x)		static __inline__ __device__ char1 tex1Dfetch(texture<char1, cudaTextureTyp e1D, cudaReadModeElementType> t, int x)
	{		{
	int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));		int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));
	return make_char1(v.x);		return make_char1(v.x);
	}		}
	static __inline__ __device__ uchar1 tex1Dfetch(texture<uchar1, 1, cudaReadM odeElementType> t, int x)		static __inline__ __device__ uchar1 tex1Dfetch(texture<uchar1, cudaTextureT ype1D, cudaReadModeElementType> t, int x)
	{		{
	uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));		uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));
	return make_uchar1(v.x);		return make_uchar1(v.x);
	}		}
	static __inline__ __device__ char2 tex1Dfetch(texture<char2, 1, cudaReadMod eElementType> t, int x)		static __inline__ __device__ char2 tex1Dfetch(texture<char2, cudaTextureTyp e1D, cudaReadModeElementType> t, int x)
	{		{
	int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));		int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));
	return make_char2(v.x, v.y);		return make_char2(v.x, v.y);
	}		}
	static __inline__ __device__ uchar2 tex1Dfetch(texture<uchar2, 1, cudaReadM odeElementType> t, int x)		static __inline__ __device__ uchar2 tex1Dfetch(texture<uchar2, cudaTextureT ype1D, cudaReadModeElementType> t, int x)
	{		{
	uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));		uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));
	return make_uchar2(v.x, v.y);		return make_uchar2(v.x, v.y);
	}		}
	static __inline__ __device__ char4 tex1Dfetch(texture<char4, 1, cudaReadMod eElementType> t, int x)		static __inline__ __device__ char4 tex1Dfetch(texture<char4, cudaTextureTyp e1D, cudaReadModeElementType> t, int x)
	{		{
	int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));		int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));
	return make_char4(v.x, v.y, v.z, v.w);		return make_char4(v.x, v.y, v.z, v.w);
	}		}
	static __inline__ __device__ uchar4 tex1Dfetch(texture<uchar4, 1, cudaReadM odeElementType> t, int x)		static __inline__ __device__ uchar4 tex1Dfetch(texture<uchar4, cudaTextureT ype1D, cudaReadModeElementType> t, int x)
	{		{
	uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));		uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));
	return make_uchar4(v.x, v.y, v.z, v.w);		return make_uchar4(v.x, v.y, v.z, v.w);
	}		}
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	static __inline__ __device__ short tex1Dfetch(texture<short, 1, cudaReadMod eElementType> t, int x)		static __inline__ __device__ short tex1Dfetch(texture<short, cudaTextureTyp e1D, cudaReadModeElementType> t, int x)
	{		{
	int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));		int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));
	return (short)v.x;		return (short)v.x;
	}		}
	static __inline__ __device__ unsigned short tex1Dfetch(texture<unsigned sho rt, 1, cudaReadModeElementType> t, int x)		static __inline__ __device__ unsigned short tex1Dfetch(texture<unsigned sho rt, cudaTextureType1D, cudaReadModeElementType> t, int x)
	{		{
	uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));		uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));
	return (unsigned short)v.x;		return (unsigned short)v.x;
	}		}
	static __inline__ __device__ short1 tex1Dfetch(texture<short1, 1, cudaReadM odeElementType> t, int x)		static __inline__ __device__ short1 tex1Dfetch(texture<short1, cudaTextureT ype1D, cudaReadModeElementType> t, int x)
	{		{
	int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));		int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));
	return make_short1(v.x);		return make_short1(v.x);
	}		}
	static __inline__ __device__ ushort1 tex1Dfetch(texture<ushort1, 1, cudaRea dModeElementType> t, int x)		static __inline__ __device__ ushort1 tex1Dfetch(texture<ushort1, cudaTextur eType1D, cudaReadModeElementType> t, int x)
	{		{
	uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));		uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));
	return make_ushort1(v.x);		return make_ushort1(v.x);
	}		}
	static __inline__ __device__ short2 tex1Dfetch(texture<short2, 1, cudaReadM odeElementType> t, int x)		static __inline__ __device__ short2 tex1Dfetch(texture<short2, cudaTextureT ype1D, cudaReadModeElementType> t, int x)
	{		{
	int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));		int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));
	return make_short2(v.x, v.y);		return make_short2(v.x, v.y);
	}		}
	static __inline__ __device__ ushort2 tex1Dfetch(texture<ushort2, 1, cudaRea dModeElementType> t, int x)		static __inline__ __device__ ushort2 tex1Dfetch(texture<ushort2, cudaTextur eType1D, cudaReadModeElementType> t, int x)
	{		{
	uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));		uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));
	return make_ushort2(v.x, v.y);		return make_ushort2(v.x, v.y);
	}		}
	static __inline__ __device__ short4 tex1Dfetch(texture<short4, 1, cudaReadM odeElementType> t, int x)		static __inline__ __device__ short4 tex1Dfetch(texture<short4, cudaTextureT ype1D, cudaReadModeElementType> t, int x)
	{		{
	int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));		int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));
	return make_short4(v.x, v.y, v.z, v.w);		return make_short4(v.x, v.y, v.z, v.w);
	}		}
	static __inline__ __device__ ushort4 tex1Dfetch(texture<ushort4, 1, cudaRea dModeElementType> t, int x)		static __inline__ __device__ ushort4 tex1Dfetch(texture<ushort4, cudaTextur eType1D, cudaReadModeElementType> t, int x)
	{		{
	uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));		uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));
	return make_ushort4(v.x, v.y, v.z, v.w);		return make_ushort4(v.x, v.y, v.z, v.w);
	}		}
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	static __inline__ __device__ int tex1Dfetch(texture<int, 1, cudaReadModeEle mentType> t, int x)		static __inline__ __device__ int tex1Dfetch(texture<int, cudaTextureType1D, cudaReadModeElementType> t, int x)
	{		{
	int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));		int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));
	return (int)v.x;		return (int)v.x;
	}		}
	static __inline__ __device__ unsigned int tex1Dfetch(texture<unsigned int, 1, cudaReadModeElementType> t, int x)		static __inline__ __device__ unsigned int tex1Dfetch(texture<unsigned int, cudaTextureType1D, cudaReadModeElementType> t, int x)
	{		{
	uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));		uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));
	return (unsigned int)v.x;		return (unsigned int)v.x;
	}		}
	static __inline__ __device__ int1 tex1Dfetch(texture<int1, 1, cudaReadModeE lementType> t, int x)		static __inline__ __device__ int1 tex1Dfetch(texture<int1, cudaTextureType1 D, cudaReadModeElementType> t, int x)
	{		{
	int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));		int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));
	return make_int1(v.x);		return make_int1(v.x);
	}		}
	static __inline__ __device__ uint1 tex1Dfetch(texture<uint1, 1, cudaReadMod eElementType> t, int x)		static __inline__ __device__ uint1 tex1Dfetch(texture<uint1, cudaTextureTyp e1D, cudaReadModeElementType> t, int x)
	{		{
	uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));		uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));
	return make_uint1(v.x);		return make_uint1(v.x);
	}		}
	static __inline__ __device__ int2 tex1Dfetch(texture<int2, 1, cudaReadModeE lementType> t, int x)		static __inline__ __device__ int2 tex1Dfetch(texture<int2, cudaTextureType1 D, cudaReadModeElementType> t, int x)
	{		{
	int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));		int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));
	return make_int2(v.x, v.y);		return make_int2(v.x, v.y);
	}		}
	static __inline__ __device__ uint2 tex1Dfetch(texture<uint2, 1, cudaReadMod eElementType> t, int x)		static __inline__ __device__ uint2 tex1Dfetch(texture<uint2, cudaTextureTyp e1D, cudaReadModeElementType> t, int x)
	{		{
	uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));		uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));
	return make_uint2(v.x, v.y);		return make_uint2(v.x, v.y);
	}		}
	static __inline__ __device__ int4 tex1Dfetch(texture<int4, 1, cudaReadModeE lementType> t, int x)		static __inline__ __device__ int4 tex1Dfetch(texture<int4, cudaTextureType1 D, cudaReadModeElementType> t, int x)
	{		{
	int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));		int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));
	return make_int4(v.x, v.y, v.z, v.w);		return make_int4(v.x, v.y, v.z, v.w);
	}		}
	static __inline__ __device__ uint4 tex1Dfetch(texture<uint4, 1, cudaReadMod eElementType> t, int x)		static __inline__ __device__ uint4 tex1Dfetch(texture<uint4, cudaTextureTyp e1D, cudaReadModeElementType> t, int x)
	{		{
	uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));		uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));
	return make_uint4(v.x, v.y, v.z, v.w);		return make_uint4(v.x, v.y, v.z, v.w);
	}		}
	#if !defined(__LP64__)		#if !defined(__LP64__)
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	static __inline__ __device__ long tex1Dfetch(texture<long, 1, cudaReadModeE lementType> t, int x)		static __inline__ __device__ long tex1Dfetch(texture<long, cudaTextureType1 D, cudaReadModeElementType> t, int x)
	{		{
	int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));		int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));
	return (long)v.x;		return (long)v.x;
	}		}
	static __inline__ __device__ unsigned long tex1Dfetch(texture<unsigned long , 1, cudaReadModeElementType> t, int x)		static __inline__ __device__ unsigned long tex1Dfetch(texture<unsigned long , cudaTextureType1D, cudaReadModeElementType> t, int x)
	{		{
	uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));		uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));
	return (unsigned long)v.x;		return (unsigned long)v.x;
	}		}
	static __inline__ __device__ long1 tex1Dfetch(texture<long1, 1, cudaReadMod eElementType> t, int x)		static __inline__ __device__ long1 tex1Dfetch(texture<long1, cudaTextureTyp e1D, cudaReadModeElementType> t, int x)
	{		{
	int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));		int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));
	return make_long1(v.x);		return make_long1(v.x);
	}		}
	static __inline__ __device__ ulong1 tex1Dfetch(texture<ulong1, 1, cudaReadM odeElementType> t, int x)		static __inline__ __device__ ulong1 tex1Dfetch(texture<ulong1, cudaTextureT ype1D, cudaReadModeElementType> t, int x)
	{		{
	uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));		uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));
	return make_ulong1(v.x);		return make_ulong1(v.x);
	}		}
	static __inline__ __device__ long2 tex1Dfetch(texture<long2, 1, cudaReadMod eElementType> t, int x)		static __inline__ __device__ long2 tex1Dfetch(texture<long2, cudaTextureTyp e1D, cudaReadModeElementType> t, int x)
	{		{
	int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));		int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));
	return make_long2(v.x, v.y);		return make_long2(v.x, v.y);
	}		}
	static __inline__ __device__ ulong2 tex1Dfetch(texture<ulong2, 1, cudaReadM odeElementType> t, int x)		static __inline__ __device__ ulong2 tex1Dfetch(texture<ulong2, cudaTextureT ype1D, cudaReadModeElementType> t, int x)
	{		{
	uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));		uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));
	return make_ulong2(v.x, v.y);		return make_ulong2(v.x, v.y);
	}		}
	static __inline__ __device__ long4 tex1Dfetch(texture<long4, 1, cudaReadMod eElementType> t, int x)		static __inline__ __device__ long4 tex1Dfetch(texture<long4, cudaTextureTyp e1D, cudaReadModeElementType> t, int x)
	{		{
	int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));		int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));
	return make_long4(v.x, v.y, v.z, v.w);		return make_long4(v.x, v.y, v.z, v.w);
	}		}
	static __inline__ __device__ ulong4 tex1Dfetch(texture<ulong4, 1, cudaReadM odeElementType> t, int x)		static __inline__ __device__ ulong4 tex1Dfetch(texture<ulong4, cudaTextureT ype1D, cudaReadModeElementType> t, int x)
	{		{
	uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));		uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));
	return make_ulong4(v.x, v.y, v.z, v.w);		return make_ulong4(v.x, v.y, v.z, v.w);
	}		}
	#endif /* !__LP64__ */		#endif /* !__LP64__ */
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	static __inline__ __device__ float tex1Dfetch(texture<float, 1, cudaReadMod eElementType> t, int x)		static __inline__ __device__ float tex1Dfetch(texture<float, cudaTextureTyp e1D, cudaReadModeElementType> t, int x)
	{		{
	float4 v = __ftexfetchi(t, make_int4(x, 0, 0, 0));		float4 v = __ftexfetchi(t, make_int4(x, 0, 0, 0));
	return v.x;		return v.x;
	}		}
	static __inline__ __device__ float1 tex1Dfetch(texture<float1, 1, cudaReadM odeElementType> t, int x)		static __inline__ __device__ float1 tex1Dfetch(texture<float1, cudaTextureT ype1D, cudaReadModeElementType> t, int x)
	{		{
	float4 v = __ftexfetchi(t, make_int4(x, 0, 0, 0));		float4 v = __ftexfetchi(t, make_int4(x, 0, 0, 0));
	return make_float1(v.x);		return make_float1(v.x);
	}		}
	static __inline__ __device__ float2 tex1Dfetch(texture<float2, 1, cudaReadM odeElementType> t, int x)		static __inline__ __device__ float2 tex1Dfetch(texture<float2, cudaTextureT ype1D, cudaReadModeElementType> t, int x)
	{		{
	float4 v = __ftexfetchi(t, make_int4(x, 0, 0, 0));		float4 v = __ftexfetchi(t, make_int4(x, 0, 0, 0));
	return make_float2(v.x, v.y);		return make_float2(v.x, v.y);
	}		}
	static __inline__ __device__ float4 tex1Dfetch(texture<float4, 1, cudaReadM odeElementType> t, int x)		static __inline__ __device__ float4 tex1Dfetch(texture<float4, cudaTextureT ype1D, cudaReadModeElementType> t, int x)
	{		{
	float4 v = __ftexfetchi(t, make_int4(x, 0, 0, 0));		float4 v = __ftexfetchi(t, make_int4(x, 0, 0, 0));
	return make_float4(v.x, v.y, v.z, v.w);		return make_float4(v.x, v.y, v.z, v.w);
	}		}
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	static __inline__ __device__ float tex1Dfetch(texture<char, 1, cudaReadMode NormalizedFloat> t, int x)		static __inline__ __device__ float tex1Dfetch(texture<char, cudaTextureType 1D, cudaReadModeNormalizedFloat> t, int x)
	{		{
	#if __SIGNED_CHARS__		#if defined(_CHAR_UNSIGNED) || defined(__CHAR_UNSIGNED__)
	int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));
	#else
	uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));		uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));
	#endif		#else /* _CHAR_UNSIGNED || __CHAR_UNSIGNED__ */
			int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));
			#endif /* _CHAR_UNSIGNED || __CHAR_UNSIGNED__ */
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return w.x;		return w.x;
	}		}
	static __inline__ __device__ float tex1Dfetch(texture<signed char, 1, cudaR eadModeNormalizedFloat> t, int x)		static __inline__ __device__ float tex1Dfetch(texture<signed char, cudaText ureType1D, cudaReadModeNormalizedFloat> t, int x)
	{		{
	int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));		int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return w.x;		return w.x;
	}		}
	static __inline__ __device__ float tex1Dfetch(texture<unsigned char, 1, cud aReadModeNormalizedFloat> t, int x)		static __inline__ __device__ float tex1Dfetch(texture<unsigned char, cudaTe xtureType1D, cudaReadModeNormalizedFloat> t, int x)
	{		{
	uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));		uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return w.x;		return w.x;
	}		}
	static __inline__ __device__ float1 tex1Dfetch(texture<char1, 1, cudaReadMo deNormalizedFloat> t, int x)		static __inline__ __device__ float1 tex1Dfetch(texture<char1, cudaTextureTy pe1D, cudaReadModeNormalizedFloat> t, int x)
	{		{
	int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));		int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float1(w.x);		return make_float1(w.x);
	}		}
	static __inline__ __device__ float1 tex1Dfetch(texture<uchar1, 1, cudaReadM odeNormalizedFloat> t, int x)		static __inline__ __device__ float1 tex1Dfetch(texture<uchar1, cudaTextureT ype1D, cudaReadModeNormalizedFloat> t, int x)
	{		{
	uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));		uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float1(w.x);		return make_float1(w.x);
	}		}
	static __inline__ __device__ float2 tex1Dfetch(texture<char2, 1, cudaReadMo deNormalizedFloat> t, int x)		static __inline__ __device__ float2 tex1Dfetch(texture<char2, cudaTextureTy pe1D, cudaReadModeNormalizedFloat> t, int x)
	{		{
	int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));		int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float2(w.x, w.y);		return make_float2(w.x, w.y);
	}		}
	static __inline__ __device__ float2 tex1Dfetch(texture<uchar2, 1, cudaReadM odeNormalizedFloat> t, int x)		static __inline__ __device__ float2 tex1Dfetch(texture<uchar2, cudaTextureT ype1D, cudaReadModeNormalizedFloat> t, int x)
	{		{
	uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));		uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float2(w.x, w.y);		return make_float2(w.x, w.y);
	}		}
	static __inline__ __device__ float4 tex1Dfetch(texture<char4, 1, cudaReadMo deNormalizedFloat> t, int x)		static __inline__ __device__ float4 tex1Dfetch(texture<char4, cudaTextureTy pe1D, cudaReadModeNormalizedFloat> t, int x)
	{		{
	int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));		int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float4(w.x, w.y, w.z, w.w);		return make_float4(w.x, w.y, w.z, w.w);
	}		}
	static __inline__ __device__ float4 tex1Dfetch(texture<uchar4, 1, cudaReadM odeNormalizedFloat> t, int x)		static __inline__ __device__ float4 tex1Dfetch(texture<uchar4, cudaTextureT ype1D, cudaReadModeNormalizedFloat> t, int x)
	{		{
	uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));		uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float4(w.x, w.y, w.z, w.w);		return make_float4(w.x, w.y, w.z, w.w);
	}		}
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	static __inline__ __device__ float tex1Dfetch(texture<short, 1, cudaReadMod eNormalizedFloat> t, int x)		static __inline__ __device__ float tex1Dfetch(texture<short, cudaTextureTyp e1D, cudaReadModeNormalizedFloat> t, int x)
	{		{
	int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));		int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return w.x;		return w.x;
	}		}
	static __inline__ __device__ float tex1Dfetch(texture<unsigned short, 1, cu daReadModeNormalizedFloat> t, int x)		static __inline__ __device__ float tex1Dfetch(texture<unsigned short, cudaT extureType1D, cudaReadModeNormalizedFloat> t, int x)
	{		{
	uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));		uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return w.x;		return w.x;
	}		}
	static __inline__ __device__ float1 tex1Dfetch(texture<short1, 1, cudaReadM odeNormalizedFloat> t, int x)		static __inline__ __device__ float1 tex1Dfetch(texture<short1, cudaTextureT ype1D, cudaReadModeNormalizedFloat> t, int x)
	{		{
	int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));		int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float1(w.x);		return make_float1(w.x);
	}		}
	static __inline__ __device__ float1 tex1Dfetch(texture<ushort1, 1, cudaRead ModeNormalizedFloat> t, int x)		static __inline__ __device__ float1 tex1Dfetch(texture<ushort1, cudaTexture Type1D, cudaReadModeNormalizedFloat> t, int x)
	{		{
	uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));		uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float1(w.x);		return make_float1(w.x);
	}		}
	static __inline__ __device__ float2 tex1Dfetch(texture<short2, 1, cudaReadM odeNormalizedFloat> t, int x)		static __inline__ __device__ float2 tex1Dfetch(texture<short2, cudaTextureT ype1D, cudaReadModeNormalizedFloat> t, int x)
	{		{
	int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));		int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float2(w.x, w.y);		return make_float2(w.x, w.y);
	}		}
	static __inline__ __device__ float2 tex1Dfetch(texture<ushort2, 1, cudaRead ModeNormalizedFloat> t, int x)		static __inline__ __device__ float2 tex1Dfetch(texture<ushort2, cudaTexture Type1D, cudaReadModeNormalizedFloat> t, int x)
	{		{
	uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));		uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float2(w.x, w.y);		return make_float2(w.x, w.y);
	}		}
	static __inline__ __device__ float4 tex1Dfetch(texture<short4, 1, cudaReadM odeNormalizedFloat> t, int x)		static __inline__ __device__ float4 tex1Dfetch(texture<short4, cudaTextureT ype1D, cudaReadModeNormalizedFloat> t, int x)
	{		{
	int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));		int4 v = __itexfetchi(t, make_int4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float4(w.x, w.y, w.z, w.w);		return make_float4(w.x, w.y, w.z, w.w);
	}		}
	static __inline__ __device__ float4 tex1Dfetch(texture<ushort4, 1, cudaRead ModeNormalizedFloat> t, int x)		static __inline__ __device__ float4 tex1Dfetch(texture<ushort4, cudaTexture Type1D, cudaReadModeNormalizedFloat> t, int x)
	{		{
	uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));		uint4 v = __utexfetchi(t, make_int4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float4(w.x, w.y, w.z, w.w);		return make_float4(w.x, w.y, w.z, w.w);
	}		}
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	static __inline__ __device__ char tex1D(texture<char, 1, cudaReadModeElemen tType> t, float x)		static __inline__ __device__ char tex1D(texture<char, cudaTextureType1D, cu daReadModeElementType> t, float x)
	{		{
	#if __SIGNED_CHARS__		#if defined(_CHAR_UNSIGNED) || defined(__CHAR_UNSIGNED__)
	int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));
	#else
	uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));
	#endif		#else /* _CHAR_UNSIGNED || __CHAR_UNSIGNED__ */
			int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));
			#endif /* _CHAR_UNSIGNED || __CHAR_UNSIGNED__ */
	return (char)v.x;		return (char)v.x;
	}		}
	static __inline__ __device__ signed char tex1D(texture<signed char, 1, cuda ReadModeElementType> t, float x)		static __inline__ __device__ signed char tex1D(texture<signed char, cudaTex tureType1D, cudaReadModeElementType> t, float x)
	{		{
	int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));		int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));
	return (signed char)v.x;		return (signed char)v.x;
	}		}
	static __inline__ __device__ unsigned char tex1D(texture<unsigned char, 1, cudaReadModeElementType> t, float x)		static __inline__ __device__ unsigned char tex1D(texture<unsigned char, cud aTextureType1D, cudaReadModeElementType> t, float x)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));
	return (unsigned char)v.x;		return (unsigned char)v.x;
	}		}
	static __inline__ __device__ char1 tex1D(texture<char1, 1, cudaReadModeElem entType> t, float x)		static __inline__ __device__ char1 tex1D(texture<char1, cudaTextureType1D, cudaReadModeElementType> t, float x)
	{		{
	int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));		int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));
	return make_char1(v.x);		return make_char1(v.x);
	}		}
	static __inline__ __device__ uchar1 tex1D(texture<uchar1, 1, cudaReadModeEl ementType> t, float x)		static __inline__ __device__ uchar1 tex1D(texture<uchar1, cudaTextureType1D , cudaReadModeElementType> t, float x)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));
	return make_uchar1(v.x);		return make_uchar1(v.x);
	}		}
	static __inline__ __device__ char2 tex1D(texture<char2, 1, cudaReadModeElem entType> t, float x)		static __inline__ __device__ char2 tex1D(texture<char2, cudaTextureType1D, cudaReadModeElementType> t, float x)
	{		{
	int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));		int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));
	return make_char2(v.x, v.y);		return make_char2(v.x, v.y);
	}		}
	static __inline__ __device__ uchar2 tex1D(texture<uchar2, 1, cudaReadModeEl ementType> t, float x)		static __inline__ __device__ uchar2 tex1D(texture<uchar2, cudaTextureType1D , cudaReadModeElementType> t, float x)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));
	return make_uchar2(v.x, v.y);		return make_uchar2(v.x, v.y);
	}		}
	static __inline__ __device__ char4 tex1D(texture<char4, 1, cudaReadModeElem entType> t, float x)		static __inline__ __device__ char4 tex1D(texture<char4, cudaTextureType1D, cudaReadModeElementType> t, float x)
	{		{
	int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));		int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));
	return make_char4(v.x, v.y, v.z, v.w);		return make_char4(v.x, v.y, v.z, v.w);
	}		}
	static __inline__ __device__ uchar4 tex1D(texture<uchar4, 1, cudaReadModeEl ementType> t, float x)		static __inline__ __device__ uchar4 tex1D(texture<uchar4, cudaTextureType1D , cudaReadModeElementType> t, float x)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));
	return make_uchar4(v.x, v.y, v.z, v.w);		return make_uchar4(v.x, v.y, v.z, v.w);
	}		}
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	static __inline__ __device__ short tex1D(texture<short, 1, cudaReadModeElem entType> t, float x)		static __inline__ __device__ short tex1D(texture<short, cudaTextureType1D, cudaReadModeElementType> t, float x)
	{		{
	int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));		int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));
	return (short)v.x;		return (short)v.x;
	}		}
	static __inline__ __device__ unsigned short tex1D(texture<unsigned short, 1 , cudaReadModeElementType> t, float x)		static __inline__ __device__ unsigned short tex1D(texture<unsigned short, c udaTextureType1D, cudaReadModeElementType> t, float x)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));
	return (unsigned short)v.x;		return (unsigned short)v.x;
	}		}
	static __inline__ __device__ short1 tex1D(texture<short1, 1, cudaReadModeEl ementType> t, float x)		static __inline__ __device__ short1 tex1D(texture<short1, cudaTextureType1D , cudaReadModeElementType> t, float x)
	{		{
	int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));		int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));
	return make_short1(v.x);		return make_short1(v.x);
	}		}
	static __inline__ __device__ ushort1 tex1D(texture<ushort1, 1, cudaReadMode ElementType> t, float x)		static __inline__ __device__ ushort1 tex1D(texture<ushort1, cudaTextureType 1D, cudaReadModeElementType> t, float x)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));
	return make_ushort1(v.x);		return make_ushort1(v.x);
	}		}
	static __inline__ __device__ short2 tex1D(texture<short2, 1, cudaReadModeEl ementType> t, float x)		static __inline__ __device__ short2 tex1D(texture<short2, cudaTextureType1D , cudaReadModeElementType> t, float x)
	{		{
	int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));		int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));
	return make_short2(v.x, v.y);		return make_short2(v.x, v.y);
	}		}
	static __inline__ __device__ ushort2 tex1D(texture<ushort2, 1, cudaReadMode ElementType> t, float x)		static __inline__ __device__ ushort2 tex1D(texture<ushort2, cudaTextureType 1D, cudaReadModeElementType> t, float x)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));
	return make_ushort2(v.x, v.y);		return make_ushort2(v.x, v.y);
	}		}
	static __inline__ __device__ short4 tex1D(texture<short4, 1, cudaReadModeEl ementType> t, float x)		static __inline__ __device__ short4 tex1D(texture<short4, cudaTextureType1D , cudaReadModeElementType> t, float x)
	{		{
	int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));		int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));
	return make_short4(v.x, v.y, v.z, v.w);		return make_short4(v.x, v.y, v.z, v.w);
	}		}
	static __inline__ __device__ ushort4 tex1D(texture<ushort4, 1, cudaReadMode ElementType> t, float x)		static __inline__ __device__ ushort4 tex1D(texture<ushort4, cudaTextureType 1D, cudaReadModeElementType> t, float x)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));
	return make_ushort4(v.x, v.y, v.z, v.w);		return make_ushort4(v.x, v.y, v.z, v.w);
	}		}
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	static __inline__ __device__ int tex1D(texture<int, 1, cudaReadModeElementT ype> t, float x)		static __inline__ __device__ int tex1D(texture<int, cudaTextureType1D, cuda ReadModeElementType> t, float x)
	{		{
	int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));		int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));
	return (int)v.x;		return (int)v.x;
	}		}
	static __inline__ __device__ unsigned int tex1D(texture<unsigned int, 1, cu daReadModeElementType> t, float x)		static __inline__ __device__ unsigned int tex1D(texture<unsigned int, cudaT extureType1D, cudaReadModeElementType> t, float x)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));
	return (unsigned int)v.x;		return (unsigned int)v.x;
	}		}
	static __inline__ __device__ int1 tex1D(texture<int1, 1, cudaReadModeElemen tType> t, float x)		static __inline__ __device__ int1 tex1D(texture<int1, cudaTextureType1D, cu daReadModeElementType> t, float x)
	{		{
	int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));		int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));
	return make_int1(v.x);		return make_int1(v.x);
	}		}
	static __inline__ __device__ uint1 tex1D(texture<uint1, 1, cudaReadModeElem entType> t, float x)		static __inline__ __device__ uint1 tex1D(texture<uint1, cudaTextureType1D, cudaReadModeElementType> t, float x)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));
	return make_uint1(v.x);		return make_uint1(v.x);
	}		}
	static __inline__ __device__ int2 tex1D(texture<int2, 1, cudaReadModeElemen tType> t, float x)		static __inline__ __device__ int2 tex1D(texture<int2, cudaTextureType1D, cu daReadModeElementType> t, float x)
	{		{
	int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));		int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));
	return make_int2(v.x, v.y);		return make_int2(v.x, v.y);
	}		}
	static __inline__ __device__ uint2 tex1D(texture<uint2, 1, cudaReadModeElem entType> t, float x)		static __inline__ __device__ uint2 tex1D(texture<uint2, cudaTextureType1D, cudaReadModeElementType> t, float x)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));
	return make_uint2(v.x, v.y);		return make_uint2(v.x, v.y);
	}		}
	static __inline__ __device__ int4 tex1D(texture<int4, 1, cudaReadModeElemen tType> t, float x)		static __inline__ __device__ int4 tex1D(texture<int4, cudaTextureType1D, cu daReadModeElementType> t, float x)
	{		{
	int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));		int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));
	return make_int4(v.x, v.y, v.z, v.w);		return make_int4(v.x, v.y, v.z, v.w);
	}		}
	static __inline__ __device__ uint4 tex1D(texture<uint4, 1, cudaReadModeElem entType> t, float x)		static __inline__ __device__ uint4 tex1D(texture<uint4, cudaTextureType1D, cudaReadModeElementType> t, float x)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));
	return make_uint4(v.x, v.y, v.z, v.w);		return make_uint4(v.x, v.y, v.z, v.w);
	}		}
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	#if !defined(__LP64__)		#if !defined(__LP64__)
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	static __inline__ __device__ long tex1D(texture<long, 1, cudaReadModeElemen tType> t, float x)		static __inline__ __device__ long tex1D(texture<long, cudaTextureType1D, cu daReadModeElementType> t, float x)
	{		{
	int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));		int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));
	return (long)v.x;		return (long)v.x;
	}		}
	static __inline__ __device__ unsigned long tex1D(texture<unsigned long, 1, cudaReadModeElementType> t, float x)		static __inline__ __device__ unsigned long tex1D(texture<unsigned long, cud aTextureType1D, cudaReadModeElementType> t, float x)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));
	return (unsigned long)v.x;		return (unsigned long)v.x;
	}		}
	static __inline__ __device__ long1 tex1D(texture<long1, 1, cudaReadModeElem entType> t, float x)		static __inline__ __device__ long1 tex1D(texture<long1, cudaTextureType1D, cudaReadModeElementType> t, float x)
	{		{
	int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));		int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));
	return make_long1(v.x);		return make_long1(v.x);
	}		}
	static __inline__ __device__ ulong1 tex1D(texture<ulong1, 1, cudaReadModeEl ementType> t, float x)		static __inline__ __device__ ulong1 tex1D(texture<ulong1, cudaTextureType1D , cudaReadModeElementType> t, float x)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));
	return make_ulong1(v.x);		return make_ulong1(v.x);
	}		}
	static __inline__ __device__ long2 tex1D(texture<long2, 1, cudaReadModeElem entType> t, float x)		static __inline__ __device__ long2 tex1D(texture<long2, cudaTextureType1D, cudaReadModeElementType> t, float x)
	{		{
	int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));		int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));
	return make_long2(v.x, v.y);		return make_long2(v.x, v.y);
	}		}
	static __inline__ __device__ ulong2 tex1D(texture<ulong2, 1, cudaReadModeEl ementType> t, float x)		static __inline__ __device__ ulong2 tex1D(texture<ulong2, cudaTextureType1D , cudaReadModeElementType> t, float x)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));
	return make_ulong2(v.x, v.y);		return make_ulong2(v.x, v.y);
	}		}
	static __inline__ __device__ long4 tex1D(texture<long4, 1, cudaReadModeElem entType> t, float x)		static __inline__ __device__ long4 tex1D(texture<long4, cudaTextureType1D, cudaReadModeElementType> t, float x)
	{		{
	int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));		int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));
	return make_long4(v.x, v.y, v.z, v.w);		return make_long4(v.x, v.y, v.z, v.w);
	}		}
	static __inline__ __device__ ulong4 tex1D(texture<ulong4, 1, cudaReadModeEl ementType> t, float x)		static __inline__ __device__ ulong4 tex1D(texture<ulong4, cudaTextureType1D , cudaReadModeElementType> t, float x)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));
	return make_ulong4(v.x, v.y, v.z, v.w);		return make_ulong4(v.x, v.y, v.z, v.w);
	}		}
	#endif /* !__LP64__ */		#endif /* !__LP64__ */
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	static __inline__ __device__ float tex1D(texture<float, 1, cudaReadModeElem entType> t, float x)		static __inline__ __device__ float tex1D(texture<float, cudaTextureType1D, cudaReadModeElementType> t, float x)
	{		{
	float4 v = __ftexfetch(t, make_float4(x, 0, 0, 0));		float4 v = __ftexfetch(t, make_float4(x, 0, 0, 0));
	return v.x;		return v.x;
	}		}
	static __inline__ __device__ float1 tex1D(texture<float1, 1, cudaReadModeEl ementType> t, float x)		static __inline__ __device__ float1 tex1D(texture<float1, cudaTextureType1D , cudaReadModeElementType> t, float x)
	{		{
	float4 v = __ftexfetch(t, make_float4(x, 0, 0, 0));		float4 v = __ftexfetch(t, make_float4(x, 0, 0, 0));
	return make_float1(v.x);		return make_float1(v.x);
	}		}
	static __inline__ __device__ float2 tex1D(texture<float2, 1, cudaReadModeEl ementType> t, float x)		static __inline__ __device__ float2 tex1D(texture<float2, cudaTextureType1D , cudaReadModeElementType> t, float x)
	{		{
	float4 v = __ftexfetch(t, make_float4(x, 0, 0, 0));		float4 v = __ftexfetch(t, make_float4(x, 0, 0, 0));
	return make_float2(v.x, v.y);		return make_float2(v.x, v.y);
	}		}
	static __inline__ __device__ float4 tex1D(texture<float4, 1, cudaReadModeEl ementType> t, float x)		static __inline__ __device__ float4 tex1D(texture<float4, cudaTextureType1D , cudaReadModeElementType> t, float x)
	{		{
	float4 v = __ftexfetch(t, make_float4(x, 0, 0, 0));		float4 v = __ftexfetch(t, make_float4(x, 0, 0, 0));
	return make_float4(v.x, v.y, v.z, v.w);		return make_float4(v.x, v.y, v.z, v.w);
	}		}
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	static __inline__ __device__ float tex1D(texture<char, 1, cudaReadModeNorma lizedFloat> t, float x)		static __inline__ __device__ float tex1D(texture<char, cudaTextureType1D, c udaReadModeNormalizedFloat> t, float x)
	{		{
	#if __SIGNED_CHARS__		#if defined(_CHAR_UNSIGNED) || defined(__CHAR_UNSIGNED__)
	int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));
	#else
	uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));
	#endif		#else /* _CHAR_UNSIGNED || __CHAR_UNSIGNED__ */
			int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));
			#endif /* _CHAR_UNSIGNED || __CHAR_UNSIGNED__ */
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return w.x;		return w.x;
	}		}
	static __inline__ __device__ float tex1D(texture<signed char, 1, cudaReadMo deNormalizedFloat> t, float x)		static __inline__ __device__ float tex1D(texture<signed char, cudaTextureTy pe1D, cudaReadModeNormalizedFloat> t, float x)
	{		{
	int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));		int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return w.x;		return w.x;
	}		}
	static __inline__ __device__ float tex1D(texture<unsigned char, 1, cudaRead ModeNormalizedFloat> t, float x)		static __inline__ __device__ float tex1D(texture<unsigned char, cudaTexture Type1D, cudaReadModeNormalizedFloat> t, float x)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return w.x;		return w.x;
	}		}
	static __inline__ __device__ float1 tex1D(texture<char1, 1, cudaReadModeNor malizedFloat> t, float x)		static __inline__ __device__ float1 tex1D(texture<char1, cudaTextureType1D, cudaReadModeNormalizedFloat> t, float x)
	{		{
	int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));		int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float1(w.x);		return make_float1(w.x);
	}		}
	static __inline__ __device__ float1 tex1D(texture<uchar1, 1, cudaReadModeNo rmalizedFloat> t, float x)		static __inline__ __device__ float1 tex1D(texture<uchar1, cudaTextureType1D , cudaReadModeNormalizedFloat> t, float x)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float1(w.x);		return make_float1(w.x);
	}		}
	static __inline__ __device__ float2 tex1D(texture<char2, 1, cudaReadModeNor malizedFloat> t, float x)		static __inline__ __device__ float2 tex1D(texture<char2, cudaTextureType1D, cudaReadModeNormalizedFloat> t, float x)
	{		{
	int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));		int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float2(w.x, w.y);		return make_float2(w.x, w.y);
	}		}
	static __inline__ __device__ float2 tex1D(texture<uchar2, 1, cudaReadModeNo rmalizedFloat> t, float x)		static __inline__ __device__ float2 tex1D(texture<uchar2, cudaTextureType1D , cudaReadModeNormalizedFloat> t, float x)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float2(w.x, w.y);		return make_float2(w.x, w.y);
	}		}
	static __inline__ __device__ float4 tex1D(texture<char4, 1, cudaReadModeNor malizedFloat> t, float x)		static __inline__ __device__ float4 tex1D(texture<char4, cudaTextureType1D, cudaReadModeNormalizedFloat> t, float x)
	{		{
	int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));		int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float4(w.x, w.y, w.z, w.w);		return make_float4(w.x, w.y, w.z, w.w);
	}		}
	static __inline__ __device__ float4 tex1D(texture<uchar4, 1, cudaReadModeNo rmalizedFloat> t, float x)		static __inline__ __device__ float4 tex1D(texture<uchar4, cudaTextureType1D , cudaReadModeNormalizedFloat> t, float x)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float4(w.x, w.y, w.z, w.w);		return make_float4(w.x, w.y, w.z, w.w);
	}		}
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	static __inline__ __device__ float tex1D(texture<short, 1, cudaReadModeNorm alizedFloat> t, float x)		static __inline__ __device__ float tex1D(texture<short, cudaTextureType1D, cudaReadModeNormalizedFloat> t, float x)
	{		{
	int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));		int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return w.x;		return w.x;
	}		}
	static __inline__ __device__ float tex1D(texture<unsigned short, 1, cudaRea dModeNormalizedFloat> t, float x)		static __inline__ __device__ float tex1D(texture<unsigned short, cudaTextur eType1D, cudaReadModeNormalizedFloat> t, float x)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return w.x;		return w.x;
	}		}
	static __inline__ __device__ float1 tex1D(texture<short1, 1, cudaReadModeNo rmalizedFloat> t, float x)		static __inline__ __device__ float1 tex1D(texture<short1, cudaTextureType1D , cudaReadModeNormalizedFloat> t, float x)
	{		{
	int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));		int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float1(w.x);		return make_float1(w.x);
	}		}
	static __inline__ __device__ float1 tex1D(texture<ushort1, 1, cudaReadModeN ormalizedFloat> t, float x)		static __inline__ __device__ float1 tex1D(texture<ushort1, cudaTextureType1 D, cudaReadModeNormalizedFloat> t, float x)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float1(w.x);		return make_float1(w.x);
	}		}
	static __inline__ __device__ float2 tex1D(texture<short2, 1, cudaReadModeNo rmalizedFloat> t, float x)		static __inline__ __device__ float2 tex1D(texture<short2, cudaTextureType1D , cudaReadModeNormalizedFloat> t, float x)
	{		{
	int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));		int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float2(w.x, w.y);		return make_float2(w.x, w.y);
	}		}
	static __inline__ __device__ float2 tex1D(texture<ushort2, 1, cudaReadModeN ormalizedFloat> t, float x)		static __inline__ __device__ float2 tex1D(texture<ushort2, cudaTextureType1 D, cudaReadModeNormalizedFloat> t, float x)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float2(w.x, w.y);		return make_float2(w.x, w.y);
	}		}
	static __inline__ __device__ float4 tex1D(texture<short4, 1, cudaReadModeNo rmalizedFloat> t, float x)		static __inline__ __device__ float4 tex1D(texture<short4, cudaTextureType1D , cudaReadModeNormalizedFloat> t, float x)
	{		{
	int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));		int4 v = __itexfetch(t, make_float4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float4(w.x, w.y, w.z, w.w);		return make_float4(w.x, w.y, w.z, w.w);
	}		}
	static __inline__ __device__ float4 tex1D(texture<ushort4, 1, cudaReadModeN ormalizedFloat> t, float x)		static __inline__ __device__ float4 tex1D(texture<ushort4, cudaTextureType1 D, cudaReadModeNormalizedFloat> t, float x)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, 0, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float4(w.x, w.y, w.z, w.w);		return make_float4(w.x, w.y, w.z, w.w);
	}		}
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* 2D Texture functions *		* 2D Texture functions *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	static __inline__ __device__ char tex2D(texture<char, 2, cudaReadModeElemen tType> t, float x, float y)		static __inline__ __device__ char tex2D(texture<char, cudaTextureType2D, cu daReadModeElementType> t, float x, float y)
	{		{
	#if __SIGNED_CHARS__		#if defined(_CHAR_UNSIGNED) || defined(__CHAR_UNSIGNED__)
	int4 v = __itexfetch(t, make_float4(x, y, 0, 0));
	#else
	uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));
	#endif		#else /* _CHAR_UNSIGNED || __CHAR_UNSIGNED__ */
			int4 v = __itexfetch(t, make_float4(x, y, 0, 0));
			#endif /* _CHAR_UNSIGNED || __CHAR_UNSIGNED__ */
	return (char)v.x;		return (char)v.x;
	}		}
	static __inline__ __device__ signed char tex2D(texture<signed char, 2, cuda ReadModeElementType> t, float x, float y)		static __inline__ __device__ signed char tex2D(texture<signed char, cudaTex tureType2D, cudaReadModeElementType> t, float x, float y)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, 0, 0));		int4 v = __itexfetch(t, make_float4(x, y, 0, 0));
	return (signed char)v.x;		return (signed char)v.x;
	}		}
	static __inline__ __device__ unsigned char tex2D(texture<unsigned char, 2, cudaReadModeElementType> t, float x, float y)		static __inline__ __device__ unsigned char tex2D(texture<unsigned char, cud aTextureType2D, cudaReadModeElementType> t, float x, float y)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));
	return (unsigned char)v.x;		return (unsigned char)v.x;
	}		}
	static __inline__ __device__ char1 tex2D(texture<char1, 2, cudaReadModeElem entType> t, float x, float y)		static __inline__ __device__ char1 tex2D(texture<char1, cudaTextureType2D, cudaReadModeElementType> t, float x, float y)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, 0, 0));		int4 v = __itexfetch(t, make_float4(x, y, 0, 0));
	return make_char1(v.x);		return make_char1(v.x);
	}		}
	static __inline__ __device__ uchar1 tex2D(texture<uchar1, 2, cudaReadModeEl ementType> t, float x, float y)		static __inline__ __device__ uchar1 tex2D(texture<uchar1, cudaTextureType2D , cudaReadModeElementType> t, float x, float y)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));
	return make_uchar1(v.x);		return make_uchar1(v.x);
	}		}
	static __inline__ __device__ char2 tex2D(texture<char2, 2, cudaReadModeElem entType> t, float x, float y)		static __inline__ __device__ char2 tex2D(texture<char2, cudaTextureType2D, cudaReadModeElementType> t, float x, float y)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, 0, 0));		int4 v = __itexfetch(t, make_float4(x, y, 0, 0));
	return make_char2(v.x, v.y);		return make_char2(v.x, v.y);
	}		}
	static __inline__ __device__ uchar2 tex2D(texture<uchar2, 2, cudaReadModeEl ementType> t, float x, float y)		static __inline__ __device__ uchar2 tex2D(texture<uchar2, cudaTextureType2D , cudaReadModeElementType> t, float x, float y)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));
	return make_uchar2(v.x, v.y);		return make_uchar2(v.x, v.y);
	}		}
	static __inline__ __device__ char4 tex2D(texture<char4, 2, cudaReadModeElem entType> t, float x, float y)		static __inline__ __device__ char4 tex2D(texture<char4, cudaTextureType2D, cudaReadModeElementType> t, float x, float y)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, 0, 0));		int4 v = __itexfetch(t, make_float4(x, y, 0, 0));
	return make_char4(v.x, v.y, v.z, v.w);		return make_char4(v.x, v.y, v.z, v.w);
	}		}
	static __inline__ __device__ uchar4 tex2D(texture<uchar4, 2, cudaReadModeEl ementType> t, float x, float y)		static __inline__ __device__ uchar4 tex2D(texture<uchar4, cudaTextureType2D , cudaReadModeElementType> t, float x, float y)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));
	return make_uchar4(v.x, v.y, v.z, v.w);		return make_uchar4(v.x, v.y, v.z, v.w);
	}		}
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	static __inline__ __device__ short tex2D(texture<short, 2, cudaReadModeElem entType> t, float x, float y)		static __inline__ __device__ short tex2D(texture<short, cudaTextureType2D, cudaReadModeElementType> t, float x, float y)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, 0, 0));		int4 v = __itexfetch(t, make_float4(x, y, 0, 0));
	return (short)v.x;		return (short)v.x;
	}		}
	static __inline__ __device__ unsigned short tex2D(texture<unsigned short, 2 , cudaReadModeElementType> t, float x, float y)		static __inline__ __device__ unsigned short tex2D(texture<unsigned short, c udaTextureType2D, cudaReadModeElementType> t, float x, float y)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));
	return (unsigned short)v.x;		return (unsigned short)v.x;
	}		}
	static __inline__ __device__ short1 tex2D(texture<short1, 2, cudaReadModeEl ementType> t, float x, float y)		static __inline__ __device__ short1 tex2D(texture<short1, cudaTextureType2D , cudaReadModeElementType> t, float x, float y)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, 0, 0));		int4 v = __itexfetch(t, make_float4(x, y, 0, 0));
	return make_short1(v.x);		return make_short1(v.x);
	}		}
	static __inline__ __device__ ushort1 tex2D(texture<ushort1, 2, cudaReadMode ElementType> t, float x, float y)		static __inline__ __device__ ushort1 tex2D(texture<ushort1, cudaTextureType 2D, cudaReadModeElementType> t, float x, float y)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));
	return make_ushort1(v.x);		return make_ushort1(v.x);
	}		}
	static __inline__ __device__ short2 tex2D(texture<short2, 2, cudaReadModeEl ementType> t, float x, float y)		static __inline__ __device__ short2 tex2D(texture<short2, cudaTextureType2D , cudaReadModeElementType> t, float x, float y)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, 0, 0));		int4 v = __itexfetch(t, make_float4(x, y, 0, 0));
	return make_short2(v.x, v.y);		return make_short2(v.x, v.y);
	}		}
	static __inline__ __device__ ushort2 tex2D(texture<ushort2, 2, cudaReadMode ElementType> t, float x, float y)		static __inline__ __device__ ushort2 tex2D(texture<ushort2, cudaTextureType 2D, cudaReadModeElementType> t, float x, float y)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));
	return make_ushort2(v.x, v.y);		return make_ushort2(v.x, v.y);
	}		}
	static __inline__ __device__ short4 tex2D(texture<short4, 2, cudaReadModeEl ementType> t, float x, float y)		static __inline__ __device__ short4 tex2D(texture<short4, cudaTextureType2D , cudaReadModeElementType> t, float x, float y)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, 0, 0));		int4 v = __itexfetch(t, make_float4(x, y, 0, 0));
	return make_short4(v.x, v.y, v.z, v.w);		return make_short4(v.x, v.y, v.z, v.w);
	}		}
	static __inline__ __device__ ushort4 tex2D(texture<ushort4, 2, cudaReadMode ElementType> t, float x, float y)		static __inline__ __device__ ushort4 tex2D(texture<ushort4, cudaTextureType 2D, cudaReadModeElementType> t, float x, float y)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));
	return make_ushort4(v.x, v.y, v.z, v.w);		return make_ushort4(v.x, v.y, v.z, v.w);
	}		}
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	static __inline__ __device__ int tex2D(texture<int, 2, cudaReadModeElementT ype> t, float x, float y)		static __inline__ __device__ int tex2D(texture<int, cudaTextureType2D, cuda ReadModeElementType> t, float x, float y)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, 0, 0));		int4 v = __itexfetch(t, make_float4(x, y, 0, 0));
	return (int)v.x;		return (int)v.x;
	}		}
	static __inline__ __device__ unsigned int tex2D(texture<unsigned int, 2, cu daReadModeElementType> t, float x, float y)		static __inline__ __device__ unsigned int tex2D(texture<unsigned int, cudaT extureType2D, cudaReadModeElementType> t, float x, float y)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));
	return (unsigned int)v.x;		return (unsigned int)v.x;
	}		}
	static __inline__ __device__ int1 tex2D(texture<int1, 2, cudaReadModeElemen tType> t, float x, float y)		static __inline__ __device__ int1 tex2D(texture<int1, cudaTextureType2D, cu daReadModeElementType> t, float x, float y)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, 0, 0));		int4 v = __itexfetch(t, make_float4(x, y, 0, 0));
	return make_int1(v.x);		return make_int1(v.x);
	}		}
	static __inline__ __device__ uint1 tex2D(texture<uint1, 2, cudaReadModeElem entType> t, float x, float y)		static __inline__ __device__ uint1 tex2D(texture<uint1, cudaTextureType2D, cudaReadModeElementType> t, float x, float y)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));
	return make_uint1(v.x);		return make_uint1(v.x);
	}		}
	static __inline__ __device__ int2 tex2D(texture<int2, 2, cudaReadModeElemen tType> t, float x, float y)		static __inline__ __device__ int2 tex2D(texture<int2, cudaTextureType2D, cu daReadModeElementType> t, float x, float y)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, 0, 0));		int4 v = __itexfetch(t, make_float4(x, y, 0, 0));
	return make_int2(v.x, v.y);		return make_int2(v.x, v.y);
	}		}
	static __inline__ __device__ uint2 tex2D(texture<uint2, 2, cudaReadModeElem entType> t, float x, float y)		static __inline__ __device__ uint2 tex2D(texture<uint2, cudaTextureType2D, cudaReadModeElementType> t, float x, float y)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));
	return make_uint2(v.x, v.y);		return make_uint2(v.x, v.y);
	}		}
	static __inline__ __device__ int4 tex2D(texture<int4, 2, cudaReadModeElemen tType> t, float x, float y)		static __inline__ __device__ int4 tex2D(texture<int4, cudaTextureType2D, cu daReadModeElementType> t, float x, float y)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, 0, 0));		int4 v = __itexfetch(t, make_float4(x, y, 0, 0));
	return make_int4(v.x, v.y, v.z, v.w);		return make_int4(v.x, v.y, v.z, v.w);
	}		}
	static __inline__ __device__ uint4 tex2D(texture<uint4, 2, cudaReadModeElem entType> t, float x, float y)		static __inline__ __device__ uint4 tex2D(texture<uint4, cudaTextureType2D, cudaReadModeElementType> t, float x, float y)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));
	return make_uint4(v.x, v.y, v.z, v.w);		return make_uint4(v.x, v.y, v.z, v.w);
	}		}
	#if !defined(__LP64__)		#if !defined(__LP64__)
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	static __inline__ __device__ long tex2D(texture<long, 2, cudaReadModeElemen tType> t, float x, float y)		static __inline__ __device__ long tex2D(texture<long, cudaTextureType2D, cu daReadModeElementType> t, float x, float y)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, 0, 0));		int4 v = __itexfetch(t, make_float4(x, y, 0, 0));
	return (long)v.x;		return (long)v.x;
	}		}
	static __inline__ __device__ unsigned long tex2D(texture<unsigned long, 2, cudaReadModeElementType> t, float x, float y)		static __inline__ __device__ unsigned long tex2D(texture<unsigned long, cud aTextureType2D, cudaReadModeElementType> t, float x, float y)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));
	return (unsigned long)v.x;		return (unsigned long)v.x;
	}		}
	static __inline__ __device__ long1 tex2D(texture<long1, 2, cudaReadModeElem entType> t, float x, float y)		static __inline__ __device__ long1 tex2D(texture<long1, cudaTextureType2D, cudaReadModeElementType> t, float x, float y)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, 0, 0));		int4 v = __itexfetch(t, make_float4(x, y, 0, 0));
	return make_long1(v.x);		return make_long1(v.x);
	}		}
	static __inline__ __device__ ulong1 tex2D(texture<ulong1, 2, cudaReadModeEl ementType> t, float x, float y)		static __inline__ __device__ ulong1 tex2D(texture<ulong1, cudaTextureType2D , cudaReadModeElementType> t, float x, float y)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));
	return make_ulong1(v.x);		return make_ulong1(v.x);
	}		}
	static __inline__ __device__ long2 tex2D(texture<long2, 2, cudaReadModeElem entType> t, float x, float y)		static __inline__ __device__ long2 tex2D(texture<long2, cudaTextureType2D, cudaReadModeElementType> t, float x, float y)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, 0, 0));		int4 v = __itexfetch(t, make_float4(x, y, 0, 0));
	return make_long2(v.x, v.y);		return make_long2(v.x, v.y);
	}		}
	static __inline__ __device__ ulong2 tex2D(texture<ulong2, 2, cudaReadModeEl ementType> t, float x, float y)		static __inline__ __device__ ulong2 tex2D(texture<ulong2, cudaTextureType2D , cudaReadModeElementType> t, float x, float y)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));
	return make_ulong2(v.x, v.y);		return make_ulong2(v.x, v.y);
	}		}
	static __inline__ __device__ long4 tex2D(texture<long4, 2, cudaReadModeElem entType> t, float x, float y)		static __inline__ __device__ long4 tex2D(texture<long4, cudaTextureType2D, cudaReadModeElementType> t, float x, float y)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, 0, 0));		int4 v = __itexfetch(t, make_float4(x, y, 0, 0));
	return make_long4(v.x, v.y, v.z, v.w);		return make_long4(v.x, v.y, v.z, v.w);
	}		}
	static __inline__ __device__ ulong4 tex2D(texture<ulong4, 2, cudaReadModeEl ementType> t, float x, float y)		static __inline__ __device__ ulong4 tex2D(texture<ulong4, cudaTextureType2D , cudaReadModeElementType> t, float x, float y)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));
	return make_ulong4(v.x, v.y, v.z, v.w);		return make_ulong4(v.x, v.y, v.z, v.w);
	}		}
	#endif /* !__LP64__ */		#endif /* !__LP64__ */
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	static __inline__ __device__ float tex2D(texture<float, 2, cudaReadModeElem entType> t, float x, float y)		static __inline__ __device__ float tex2D(texture<float, cudaTextureType2D, cudaReadModeElementType> t, float x, float y)
	{		{
	float4 v = __ftexfetch(t, make_float4(x, y, 0, 0));		float4 v = __ftexfetch(t, make_float4(x, y, 0, 0));
	return v.x;		return v.x;
	}		}
	static __inline__ __device__ float1 tex2D(texture<float1, 2, cudaReadModeEl ementType> t, float x, float y)		static __inline__ __device__ float1 tex2D(texture<float1, cudaTextureType2D , cudaReadModeElementType> t, float x, float y)
	{		{
	float4 v = __ftexfetch(t, make_float4(x, y, 0, 0));		float4 v = __ftexfetch(t, make_float4(x, y, 0, 0));
	return make_float1(v.x);		return make_float1(v.x);
	}		}
	static __inline__ __device__ float2 tex2D(texture<float2, 2, cudaReadModeEl ementType> t, float x, float y)		static __inline__ __device__ float2 tex2D(texture<float2, cudaTextureType2D , cudaReadModeElementType> t, float x, float y)
	{		{
	float4 v = __ftexfetch(t, make_float4(x, y, 0, 0));		float4 v = __ftexfetch(t, make_float4(x, y, 0, 0));
	return make_float2(v.x, v.y);		return make_float2(v.x, v.y);
	}		}
	static __inline__ __device__ float4 tex2D(texture<float4, 2, cudaReadModeEl ementType> t, float x, float y)		static __inline__ __device__ float4 tex2D(texture<float4, cudaTextureType2D , cudaReadModeElementType> t, float x, float y)
	{		{
	float4 v = __ftexfetch(t, make_float4(x, y, 0, 0));		float4 v = __ftexfetch(t, make_float4(x, y, 0, 0));
	return make_float4(v.x, v.y, v.z, v.w);		return make_float4(v.x, v.y, v.z, v.w);
	}		}
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	static __inline__ __device__ float tex2D(texture<char, 2, cudaReadModeNorma lizedFloat> t, float x, float y)		static __inline__ __device__ float tex2D(texture<char, cudaTextureType2D, c udaReadModeNormalizedFloat> t, float x, float y)
	{		{
	#if __SIGNED_CHARS__		#if defined(_CHAR_UNSIGNED) || defined(__CHAR_UNSIGNED__)
	int4 v = __itexfetch(t, make_float4(x, y, 0, 0));
	#else
	uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));
	#endif		#else /* _CHAR_UNSIGNED || __CHAR_UNSIGNED__ */
			int4 v = __itexfetch(t, make_float4(x, y, 0, 0));
			#endif /* _CHAR_UNSIGNED || __CHAR_UNSIGNED__ */
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return w.x;		return w.x;
	}		}
	static __inline__ __device__ float tex2D(texture<signed char, 2, cudaReadMo deNormalizedFloat> t, float x, float y)		static __inline__ __device__ float tex2D(texture<signed char, cudaTextureTy pe2D, cudaReadModeNormalizedFloat> t, float x, float y)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, 0, 0));		int4 v = __itexfetch(t, make_float4(x, y, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return w.x;		return w.x;
	}		}
	static __inline__ __device__ float tex2D(texture<unsigned char, 2, cudaRead ModeNormalizedFloat> t, float x, float y)		static __inline__ __device__ float tex2D(texture<unsigned char, cudaTexture Type2D, cudaReadModeNormalizedFloat> t, float x, float y)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return w.x;		return w.x;
	}		}
	static __inline__ __device__ float1 tex2D(texture<char1, 2, cudaReadModeNor malizedFloat> t, float x, float y)		static __inline__ __device__ float1 tex2D(texture<char1, cudaTextureType2D, cudaReadModeNormalizedFloat> t, float x, float y)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, 0, 0));		int4 v = __itexfetch(t, make_float4(x, y, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float1(w.x);		return make_float1(w.x);
	}		}
	static __inline__ __device__ float1 tex2D(texture<uchar1, 2, cudaReadModeNo rmalizedFloat> t, float x, float y)		static __inline__ __device__ float1 tex2D(texture<uchar1, cudaTextureType2D , cudaReadModeNormalizedFloat> t, float x, float y)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float1(w.x);		return make_float1(w.x);
	}		}
	static __inline__ __device__ float2 tex2D(texture<char2, 2, cudaReadModeNor malizedFloat> t, float x, float y)		static __inline__ __device__ float2 tex2D(texture<char2, cudaTextureType2D, cudaReadModeNormalizedFloat> t, float x, float y)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, 0, 0));		int4 v = __itexfetch(t, make_float4(x, y, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float2(w.x, w.y);		return make_float2(w.x, w.y);
	}		}
	static __inline__ __device__ float2 tex2D(texture<uchar2, 2, cudaReadModeNo rmalizedFloat> t, float x, float y)		static __inline__ __device__ float2 tex2D(texture<uchar2, cudaTextureType2D , cudaReadModeNormalizedFloat> t, float x, float y)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float2(w.x, w.y);		return make_float2(w.x, w.y);
	}		}
	static __inline__ __device__ float4 tex2D(texture<char4, 2, cudaReadModeNor malizedFloat> t, float x, float y)		static __inline__ __device__ float4 tex2D(texture<char4, cudaTextureType2D, cudaReadModeNormalizedFloat> t, float x, float y)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, 0, 0));		int4 v = __itexfetch(t, make_float4(x, y, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float4(w.x, w.y, w.z, w.w);		return make_float4(w.x, w.y, w.z, w.w);
	}		}
	static __inline__ __device__ float4 tex2D(texture<uchar4, 2, cudaReadModeNo rmalizedFloat> t, float x, float y)		static __inline__ __device__ float4 tex2D(texture<uchar4, cudaTextureType2D , cudaReadModeNormalizedFloat> t, float x, float y)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float4(w.x, w.y, w.z, w.w);		return make_float4(w.x, w.y, w.z, w.w);
	}		}
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	static __inline__ __device__ float tex2D(texture<short, 2, cudaReadModeNorm alizedFloat> t, float x, float y)		static __inline__ __device__ float tex2D(texture<short, cudaTextureType2D, cudaReadModeNormalizedFloat> t, float x, float y)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, 0, 0));		int4 v = __itexfetch(t, make_float4(x, y, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return w.x;		return w.x;
	}		}
	static __inline__ __device__ float tex2D(texture<unsigned short, 2, cudaRea dModeNormalizedFloat> t, float x, float y)		static __inline__ __device__ float tex2D(texture<unsigned short, cudaTextur eType2D, cudaReadModeNormalizedFloat> t, float x, float y)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return w.x;		return w.x;
	}		}
	static __inline__ __device__ float1 tex2D(texture<short1, 2, cudaReadModeNo rmalizedFloat> t, float x, float y)		static __inline__ __device__ float1 tex2D(texture<short1, cudaTextureType2D , cudaReadModeNormalizedFloat> t, float x, float y)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, 0, 0));		int4 v = __itexfetch(t, make_float4(x, y, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float1(w.x);		return make_float1(w.x);
	}		}
	static __inline__ __device__ float1 tex2D(texture<ushort1, 2, cudaReadModeN ormalizedFloat> t, float x, float y)		static __inline__ __device__ float1 tex2D(texture<ushort1, cudaTextureType2 D, cudaReadModeNormalizedFloat> t, float x, float y)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float1(w.x);		return make_float1(w.x);
	}		}
	static __inline__ __device__ float2 tex2D(texture<short2, 2, cudaReadModeNo rmalizedFloat> t, float x, float y)		static __inline__ __device__ float2 tex2D(texture<short2, cudaTextureType2D , cudaReadModeNormalizedFloat> t, float x, float y)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, 0, 0));		int4 v = __itexfetch(t, make_float4(x, y, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float2(w.x, w.y);		return make_float2(w.x, w.y);
	}		}
	static __inline__ __device__ float2 tex2D(texture<ushort2, 2, cudaReadModeN ormalizedFloat> t, float x, float y)		static __inline__ __device__ float2 tex2D(texture<ushort2, cudaTextureType2 D, cudaReadModeNormalizedFloat> t, float x, float y)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float2(w.x, w.y);		return make_float2(w.x, w.y);
	}		}
	static __inline__ __device__ float4 tex2D(texture<short4, 2, cudaReadModeNo rmalizedFloat> t, float x, float y)		static __inline__ __device__ float4 tex2D(texture<short4, cudaTextureType2D , cudaReadModeNormalizedFloat> t, float x, float y)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, 0, 0));		int4 v = __itexfetch(t, make_float4(x, y, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float4(w.x, w.y, w.z, w.w);		return make_float4(w.x, w.y, w.z, w.w);
	}		}
	static __inline__ __device__ float4 tex2D(texture<ushort4, 2, cudaReadModeN ormalizedFloat> t, float x, float y)		static __inline__ __device__ float4 tex2D(texture<ushort4, cudaTextureType2 D, cudaReadModeNormalizedFloat> t, float x, float y)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));		uint4 v = __utexfetch(t, make_float4(x, y, 0, 0));
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float4(w.x, w.y, w.z, w.w);		return make_float4(w.x, w.y, w.z, w.w);
	}		}
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* 3D Texture functions *		* 1D Layered Texture functions *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	static __inline__ __device__ char tex3D(texture<char, 3, cudaReadModeElemen tType> t, float x, float y, float z)		static __inline__ __device__ char tex1DLayered(texture<char, cudaTextureTyp e1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	#if __SIGNED_CHARS__		#if defined(_CHAR_UNSIGNED) || defined(__CHAR_UNSIGNED__)
	int4 v = __itexfetch(t, make_float4(x, y, z, 0));		uint4 v = __utexfetchl(t, make_float4(x, 0, 0, 0), layer);
	#else		#else /* _CHAR_UNSIGNED || __CHAR_UNSIGNED__ */
	uint4 v = __utexfetch(t, make_float4(x, y, z, 0));		int4 v = __itexfetchl(t, make_float4(x, 0, 0, 0), layer);
	#endif		#endif /* _CHAR_UNSIGNED || __CHAR_UNSIGNED__ */
	return (char)v.x;		return (char)v.x;
	}		}
	static __inline__ __device__ signed char tex3D(texture<signed char, 3, cuda ReadModeElementType> t, float x, float y, float z)		static __inline__ __device__ signed char tex1DLayered(texture<signed char, cudaTextureType1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, z, 0));		int4 v = __itexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return (signed char)v.x;		return (signed char)v.x;
	}		}
	static __inline__ __device__ unsigned char tex3D(texture<unsigned char, 3, cudaReadModeElementType> t, float x, float y, float z)		static __inline__ __device__ unsigned char tex1DLayered(texture<unsigned ch ar, cudaTextureType1DLayered, cudaReadModeElementType> t, float x, int laye r)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, z, 0));		uint4 v = __utexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return (unsigned char)v.x;		return (unsigned char)v.x;
	}		}
	static __inline__ __device__ char1 tex3D(texture<char1, 3, cudaReadModeElem entType> t, float x, float y, float z)		static __inline__ __device__ char1 tex1DLayered(texture<char1, cudaTextureT ype1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, z, 0));		int4 v = __itexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return make_char1(v.x);		return make_char1(v.x);
	}		}
	static __inline__ __device__ uchar1 tex3D(texture<uchar1, 3, cudaReadModeEl ementType> t, float x, float y, float z)		static __inline__ __device__ uchar1 tex1DLayered(texture<uchar1, cudaTextur eType1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, z, 0));		uint4 v = __utexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return make_uchar1(v.x);		return make_uchar1(v.x);
	}		}
	static __inline__ __device__ char2 tex3D(texture<char2, 3, cudaReadModeElem entType> t, float x, float y, float z)		static __inline__ __device__ char2 tex1DLayered(texture<char2, cudaTextureT ype1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, z, 0));		int4 v = __itexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return make_char2(v.x, v.y);		return make_char2(v.x, v.y);
	}		}
	static __inline__ __device__ uchar2 tex3D(texture<uchar2, 3, cudaReadModeEl ementType> t, float x, float y, float z)		static __inline__ __device__ uchar2 tex1DLayered(texture<uchar2, cudaTextur eType1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, z, 0));		uint4 v = __utexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return make_uchar2(v.x, v.y);		return make_uchar2(v.x, v.y);
	}		}
	static __inline__ __device__ char4 tex3D(texture<char4, 3, cudaReadModeElem entType> t, float x, float y, float z)		static __inline__ __device__ char4 tex1DLayered(texture<char4, cudaTextureT ype1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, z, 0));		int4 v = __itexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return make_char4(v.x, v.y, v.z, v.w);		return make_char4(v.x, v.y, v.z, v.w);
	}		}
	static __inline__ __device__ uchar4 tex3D(texture<uchar4, 3, cudaReadModeEl ementType> t, float x, float y, float z)		static __inline__ __device__ uchar4 tex1DLayered(texture<uchar4, cudaTextur eType1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, z, 0));		uint4 v = __utexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return make_uchar4(v.x, v.y, v.z, v.w);		return make_uchar4(v.x, v.y, v.z, v.w);
	}		}
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	static __inline__ __device__ short tex3D(texture<short, 3, cudaReadModeElem entType> t, float x, float y, float z)		static __inline__ __device__ short tex1DLayered(texture<short, cudaTextureT ype1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, z, 0));		int4 v = __itexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return (short)v.x;		return (short)v.x;
	}		}
	static __inline__ __device__ unsigned short tex3D(texture<unsigned short, 3 , cudaReadModeElementType> t, float x, float y, float z)		static __inline__ __device__ unsigned short tex1DLayered(texture<unsigned s hort, cudaTextureType1DLayered, cudaReadModeElementType> t, float x, int la yer)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, z, 0));		uint4 v = __utexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return (unsigned short)v.x;		return (unsigned short)v.x;
	}		}
	static __inline__ __device__ short1 tex3D(texture<short1, 3, cudaReadModeEl ementType> t, float x, float y, float z)		static __inline__ __device__ short1 tex1DLayered(texture<short1, cudaTextur eType1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, z, 0));		int4 v = __itexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return make_short1(v.x);		return make_short1(v.x);
	}		}
	static __inline__ __device__ ushort1 tex3D(texture<ushort1, 3, cudaReadMode ElementType> t, float x, float y, float z)		static __inline__ __device__ ushort1 tex1DLayered(texture<ushort1, cudaText ureType1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, z, 0));		uint4 v = __utexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return make_ushort1(v.x);		return make_ushort1(v.x);
	}		}
	static __inline__ __device__ short2 tex3D(texture<short2, 3, cudaReadModeEl ementType> t, float x, float y, float z)		static __inline__ __device__ short2 tex1DLayered(texture<short2, cudaTextur eType1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, z, 0));		int4 v = __itexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return make_short2(v.x, v.y);		return make_short2(v.x, v.y);
	}		}
	static __inline__ __device__ ushort2 tex3D(texture<ushort2, 3, cudaReadMode ElementType> t, float x, float y, float z)		static __inline__ __device__ ushort2 tex1DLayered(texture<ushort2, cudaText ureType1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, z, 0));		uint4 v = __utexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return make_ushort2(v.x, v.y);		return make_ushort2(v.x, v.y);
	}		}
	static __inline__ __device__ short4 tex3D(texture<short4, 3, cudaReadModeEl ementType> t, float x, float y, float z)		static __inline__ __device__ short4 tex1DLayered(texture<short4, cudaTextur eType1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, z, 0));		int4 v = __itexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return make_short4(v.x, v.y, v.z, v.w);		return make_short4(v.x, v.y, v.z, v.w);
	}		}
	static __inline__ __device__ ushort4 tex3D(texture<ushort4, 3, cudaReadMode ElementType> t, float x, float y, float z)		static __inline__ __device__ ushort4 tex1DLayered(texture<ushort4, cudaText ureType1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, z, 0));		uint4 v = __utexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return make_ushort4(v.x, v.y, v.z, v.w);		return make_ushort4(v.x, v.y, v.z, v.w);
	}		}
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	static __inline__ __device__ int tex3D(texture<int, 3, cudaReadModeElementT ype> t, float x, float y, float z)		static __inline__ __device__ int tex1DLayered(texture<int, cudaTextureType1 DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, z, 0));		int4 v = __itexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return (int)v.x;		return (int)v.x;
	}		}
	static __inline__ __device__ unsigned int tex3D(texture<unsigned int, 3, cu daReadModeElementType> t, float x, float y, float z)		static __inline__ __device__ unsigned int tex1DLayered(texture<unsigned int , cudaTextureType1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, z, 0));		uint4 v = __utexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return (unsigned int)v.x;		return (unsigned int)v.x;
	}		}
	static __inline__ __device__ int1 tex3D(texture<int1, 3, cudaReadModeElemen tType> t, float x, float y, float z)		static __inline__ __device__ int1 tex1DLayered(texture<int1, cudaTextureTyp e1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, z, 0));		int4 v = __itexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return make_int1(v.x);		return make_int1(v.x);
	}		}
	static __inline__ __device__ uint1 tex3D(texture<uint1, 3, cudaReadModeElem entType> t, float x, float y, float z)		static __inline__ __device__ uint1 tex1DLayered(texture<uint1, cudaTextureT ype1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, z, 0));		uint4 v = __utexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return make_uint1(v.x);		return make_uint1(v.x);
	}		}
	static __inline__ __device__ int2 tex3D(texture<int2, 3, cudaReadModeElemen tType> t, float x, float y, float z)		static __inline__ __device__ int2 tex1DLayered(texture<int2, cudaTextureTyp e1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, z, 0));		int4 v = __itexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return make_int2(v.x, v.y);		return make_int2(v.x, v.y);
	}		}
	static __inline__ __device__ uint2 tex3D(texture<uint2, 3, cudaReadModeElem entType> t, float x, float y, float z)		static __inline__ __device__ uint2 tex1DLayered(texture<uint2, cudaTextureT ype1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, z, 0));		uint4 v = __utexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return make_uint2(v.x, v.y);		return make_uint2(v.x, v.y);
	}		}
	static __inline__ __device__ int4 tex3D(texture<int4, 3, cudaReadModeElemen tType> t, float x, float y, float z)		static __inline__ __device__ int4 tex1DLayered(texture<int4, cudaTextureTyp e1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, z, 0));		int4 v = __itexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return make_int4(v.x, v.y, v.z, v.w);		return make_int4(v.x, v.y, v.z, v.w);
	}		}
	static __inline__ __device__ uint4 tex3D(texture<uint4, 3, cudaReadModeElem entType> t, float x, float y, float z)		static __inline__ __device__ uint4 tex1DLayered(texture<uint4, cudaTextureT ype1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, z, 0));		uint4 v = __utexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return make_uint4(v.x, v.y, v.z, v.w);		return make_uint4(v.x, v.y, v.z, v.w);
	}		}
	#if !defined(__LP64__)		#if !defined(__LP64__)
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	static __inline__ __device__ long tex3D(texture<long, 3, cudaReadModeElemen tType> t, float x, float y, float z)		static __inline__ __device__ long tex1DLayered(texture<long, cudaTextureTyp e1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, z, 0));		int4 v = __itexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return (long)v.x;		return (long)v.x;
	}		}
	static __inline__ __device__ unsigned long tex3D(texture<unsigned long, 3, cudaReadModeElementType> t, float x, float y, float z)		static __inline__ __device__ unsigned long tex1DLayered(texture<unsigned lo ng, cudaTextureType1DLayered, cudaReadModeElementType> t, float x, int laye r)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, z, 0));		uint4 v = __utexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return (unsigned long)v.x;		return (unsigned long)v.x;
	}		}
	static __inline__ __device__ long1 tex3D(texture<long1, 3, cudaReadModeElem entType> t, float x, float y, float z)		static __inline__ __device__ long1 tex1DLayered(texture<long1, cudaTextureT ype1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, z, 0));		int4 v = __itexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return make_long1(v.x);		return make_long1(v.x);
	}		}
	static __inline__ __device__ ulong1 tex3D(texture<ulong1, 3, cudaReadModeEl ementType> t, float x, float y, float z)		static __inline__ __device__ ulong1 tex1DLayered(texture<ulong1, cudaTextur eType1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, z, 0));		uint4 v = __utexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return make_ulong1(v.x);		return make_ulong1(v.x);
	}		}
	static __inline__ __device__ long2 tex3D(texture<long2, 3, cudaReadModeElem entType> t, float x, float y, float z)		static __inline__ __device__ long2 tex1DLayered(texture<long2, cudaTextureT ype1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, z, 0));		int4 v = __itexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return make_long2(v.x, v.y);		return make_long2(v.x, v.y);
	}		}
	static __inline__ __device__ ulong2 tex3D(texture<ulong2, 3, cudaReadModeEl ementType> t, float x, float y, float z)		static __inline__ __device__ ulong2 tex1DLayered(texture<ulong2, cudaTextur eType1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, z, 0));		uint4 v = __utexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return make_ulong2(v.x, v.y);		return make_ulong2(v.x, v.y);
	}		}
	static __inline__ __device__ long4 tex3D(texture<long4, 3, cudaReadModeElem entType> t, float x, float y, float z)		static __inline__ __device__ long4 tex1DLayered(texture<long4, cudaTextureT ype1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, z, 0));		int4 v = __itexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return make_long4(v.x, v.y, v.z, v.w);		return make_long4(v.x, v.y, v.z, v.w);
	}		}
	static __inline__ __device__ ulong4 tex3D(texture<ulong4, 3, cudaReadModeEl ementType> t, float x, float y, float z)		static __inline__ __device__ ulong4 tex1DLayered(texture<ulong4, cudaTextur eType1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, z, 0));		uint4 v = __utexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return make_ulong4(v.x, v.y, v.z, v.w);		return make_ulong4(v.x, v.y, v.z, v.w);
	}		}
	#endif /* !__LP64__ */		#endif /* !__LP64__ */
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	static __inline__ __device__ float tex3D(texture<float, 3, cudaReadModeElem entType> t, float x, float y, float z)		static __inline__ __device__ float tex1DLayered(texture<float, cudaTextureT ype1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	float4 v = __ftexfetch(t, make_float4(x, y, z, 0));		float4 v = __ftexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return v.x;		return v.x;
	}		}
	static __inline__ __device__ float1 tex3D(texture<float1, 3, cudaReadModeEl ementType> t, float x, float y, float z)		static __inline__ __device__ float1 tex1DLayered(texture<float1, cudaTextur eType1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	float4 v = __ftexfetch(t, make_float4(x, y, z, 0));		float4 v = __ftexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return make_float1(v.x);		return make_float1(v.x);
	}		}
	static __inline__ __device__ float2 tex3D(texture<float2, 3, cudaReadModeEl ementType> t, float x, float y, float z)		static __inline__ __device__ float2 tex1DLayered(texture<float2, cudaTextur eType1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	float4 v = __ftexfetch(t, make_float4(x, y, z, 0));		float4 v = __ftexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return make_float2(v.x, v.y);		return make_float2(v.x, v.y);
	}		}
	static __inline__ __device__ float4 tex3D(texture<float4, 3, cudaReadModeEl ementType> t, float x, float y, float z)		static __inline__ __device__ float4 tex1DLayered(texture<float4, cudaTextur eType1DLayered, cudaReadModeElementType> t, float x, int layer)
	{		{
	float4 v = __ftexfetch(t, make_float4(x, y, z, 0));		float4 v = __ftexfetchl(t, make_float4(x, 0, 0, 0), layer);
	return make_float4(v.x, v.y, v.z, v.w);		return make_float4(v.x, v.y, v.z, v.w);
	}		}
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	static __inline__ __device__ float tex3D(texture<char, 3, cudaReadModeNorma lizedFloat> t, float x, float y, float z)		static __inline__ __device__ float tex1DLayered(texture<char, cudaTextureTy pe1DLayered, cudaReadModeNormalizedFloat> t, float x, int layer)
	{		{
	#if __SIGNED_CHARS__		#if defined(_CHAR_UNSIGNED) || defined(__CHAR_UNSIGNED__)
	int4 v = __itexfetch(t, make_float4(x, y, z, 0));		uint4 v = __utexfetchl(t, make_float4(x, 0, 0, 0), layer);
	#else		#else /* _CHAR_UNSIGNED || __CHAR_UNSIGNED__ */
	uint4 v = __utexfetch(t, make_float4(x, y, z, 0));		int4 v = __itexfetchl(t, make_float4(x, 0, 0, 0), layer);
	#endif		#endif /* _CHAR_UNSIGNED || __CHAR_UNSIGNED__ */
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return w.x;		return w.x;
	}		}
	static __inline__ __device__ float tex3D(texture<signed char, 3, cudaReadMo deNormalizedFloat> t, float x, float y, float z)		static __inline__ __device__ float tex1DLayered(texture<signed char, cudaTe xtureType1DLayered, cudaReadModeNormalizedFloat> t, float x, int layer)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, z, 0));		int4 v = __itexfetchl(t, make_float4(x, 0, 0, 0), layer);
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return w.x;		return w.x;
	}		}
	static __inline__ __device__ float tex3D(texture<unsigned char, 3, cudaRead ModeNormalizedFloat> t, float x, float y, float z)		static __inline__ __device__ float tex1DLayered(texture<unsigned char, cuda TextureType1DLayered, cudaReadModeNormalizedFloat> t, float x, int layer)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, z, 0));		uint4 v = __utexfetchl(t, make_float4(x, 0, 0, 0), layer);
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return w.x;		return w.x;
	}		}
	static __inline__ __device__ float1 tex3D(texture<char1, 3, cudaReadModeNor malizedFloat> t, float x, float y, float z)		static __inline__ __device__ float1 tex1DLayered(texture<char1, cudaTexture Type1DLayered, cudaReadModeNormalizedFloat> t, float x, int layer)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, z, 0));		int4 v = __itexfetchl(t, make_float4(x, 0, 0, 0), layer);
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float1(w.x);		return make_float1(w.x);
	}		}
	static __inline__ __device__ float1 tex3D(texture<uchar1, 3, cudaReadModeNo rmalizedFloat> t, float x, float y, float z)		static __inline__ __device__ float1 tex1DLayered(texture<uchar1, cudaTextur eType1DLayered, cudaReadModeNormalizedFloat> t, float x, int layer)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, z, 0));		uint4 v = __utexfetchl(t, make_float4(x, 0, 0, 0), layer);
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float1(w.x);		return make_float1(w.x);
	}		}
	static __inline__ __device__ float2 tex3D(texture<char2, 3, cudaReadModeNor malizedFloat> t, float x, float y, float z)		static __inline__ __device__ float2 tex1DLayered(texture<char2, cudaTexture Type1DLayered, cudaReadModeNormalizedFloat> t, float x, int layer)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, z, 0));		int4 v = __itexfetchl(t, make_float4(x, 0, 0, 0), layer);
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float2(w.x, w.y);		return make_float2(w.x, w.y);
	}		}
	static __inline__ __device__ float2 tex3D(texture<uchar2, 3, cudaReadModeNo rmalizedFloat> t, float x, float y, float z)		static __inline__ __device__ float2 tex1DLayered(texture<uchar2, cudaTextur eType1DLayered, cudaReadModeNormalizedFloat> t, float x, int layer)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, z, 0));		uint4 v = __utexfetchl(t, make_float4(x, 0, 0, 0), layer);
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float2(w.x, w.y);		return make_float2(w.x, w.y);
	}		}
	static __inline__ __device__ float4 tex3D(texture<char4, 3, cudaReadModeNor malizedFloat> t, float x, float y, float z)		static __inline__ __device__ float4 tex1DLayered(texture<char4, cudaTexture Type1DLayered, cudaReadModeNormalizedFloat> t, float x, int layer)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, z, 0));		int4 v = __itexfetchl(t, make_float4(x, 0, 0, 0), layer);
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float4(w.x, w.y, w.z, w.w);		return make_float4(w.x, w.y, w.z, w.w);
	}		}
	static __inline__ __device__ float4 tex3D(texture<uchar4, 3, cudaReadModeNo rmalizedFloat> t, float x, float y, float z)		static __inline__ __device__ float4 tex1DLayered(texture<uchar4, cudaTextur eType1DLayered, cudaReadModeNormalizedFloat> t, float x, int layer)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, z, 0));		uint4 v = __utexfetchl(t, make_float4(x, 0, 0, 0), layer);
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float4(w.x, w.y, w.z, w.w);		return make_float4(w.x, w.y, w.z, w.w);
	}		}
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	static __inline__ __device__ float tex3D(texture<short, 3, cudaReadModeNorm alizedFloat> t, float x, float y, float z)		static __inline__ __device__ float tex1DLayered(texture<short, cudaTextureT ype1DLayered, cudaReadModeNormalizedFloat> t, float x, int layer)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, z, 0));		int4 v = __itexfetchl(t, make_float4(x, 0, 0, 0), layer);
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return w.x;		return w.x;
	}		}
	static __inline__ __device__ float tex3D(texture<unsigned short, 3, cudaRea dModeNormalizedFloat> t, float x, float y, float z)		static __inline__ __device__ float tex1DLayered(texture<unsigned short, cud aTextureType1DLayered, cudaReadModeNormalizedFloat> t, float x, int layer)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, z, 0));		uint4 v = __utexfetchl(t, make_float4(x, 0, 0, 0), layer);
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return w.x;		return w.x;
	}		}
	static __inline__ __device__ float1 tex3D(texture<short1, 3, cudaReadModeNo rmalizedFloat> t, float x, float y, float z)		static __inline__ __device__ float1 tex1DLayered(texture<short1, cudaTextur eType1DLayered, cudaReadModeNormalizedFloat> t, float x, int layer)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, z, 0));		int4 v = __itexfetchl(t, make_float4(x, 0, 0, 0), layer);
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float1(w.x);		return make_float1(w.x);
	}		}
	static __inline__ __device__ float1 tex3D(texture<ushort1, 3, cudaReadModeN ormalizedFloat> t, float x, float y, float z)		static __inline__ __device__ float1 tex1DLayered(texture<ushort1, cudaTextu reType1DLayered, cudaReadModeNormalizedFloat> t, float x, int layer)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, z, 0));		uint4 v = __utexfetchl(t, make_float4(x, 0, 0, 0), layer);
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float1(w.x);		return make_float1(w.x);
	}		}
	static __inline__ __device__ float2 tex3D(texture<short2, 3, cudaReadModeNo rmalizedFloat> t, float x, float y, float z)		static __inline__ __device__ float2 tex1DLayered(texture<short2, cudaTextur eType1DLayered, cudaReadModeNormalizedFloat> t, float x, int layer)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, z, 0));		int4 v = __itexfetchl(t, make_float4(x, 0, 0, 0), layer);
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float2(w.x, w.y);		return make_float2(w.x, w.y);
	}		}
	static __inline__ __device__ float2 tex3D(texture<ushort2, 3, cudaReadModeN ormalizedFloat> t, float x, float y, float z)		static __inline__ __device__ float2 tex1DLayered(texture<ushort2, cudaTextu reType1DLayered, cudaReadModeNormalizedFloat> t, float x, int layer)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, z, 0));		uint4 v = __utexfetchl(t, make_float4(x, 0, 0, 0), layer);
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float2(w.x, w.y);		return make_float2(w.x, w.y);
	}		}
	static __inline__ __device__ float4 tex3D(texture<short4, 3, cudaReadModeNo rmalizedFloat> t, float x, float y, float z)		static __inline__ __device__ float4 tex1DLayered(texture<short4, cudaTextur eType1DLayered, cudaReadModeNormalizedFloat> t, float x, int layer)
	{		{
	int4 v = __itexfetch(t, make_float4(x, y, z, 0));		int4 v = __itexfetchl(t, make_float4(x, 0, 0, 0), layer);
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float4(w.x, w.y, w.z, w.w);		return make_float4(w.x, w.y, w.z, w.w);
	}		}
	static __inline__ __device__ float4 tex3D(texture<ushort4, 3, cudaReadModeN ormalizedFloat> t, float x, float y, float z)		static __inline__ __device__ float4 tex1DLayered(texture<ushort4, cudaTextu reType1DLayered, cudaReadModeNormalizedFloat> t, float x, int layer)
	{		{
	uint4 v = __utexfetch(t, make_float4(x, y, z, 0));		uint4 v = __utexfetchl(t, make_float4(x, 0, 0, 0), layer);
	float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));		float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as _float(v.z), __int_as_float(v.w));
	return make_float4(w.x, w.y, w.z, w.w);		return make_float4(w.x, w.y, w.z, w.w);
	}		}
	#define __utexfetchi \		/**************************************************************************
	000 incorect invocation of builtin __utexfetchi 000		*****
	#define __itexfetchi \		*
	000 incorect invocation of builtin __itexfetchi 000		*
	#define __ftexfetchi \		* 2D Layered Texture functions
	000 incorect invocation of builtin __ftexfetchi 000		*
	#define __utexfetch \		*
	000 incorect invocation of builtin __utexfetch 000		*
	#define __itexfetch \		***************************************************************************
	000 incorect invocation of builtin __itexfetch 000		****/
	#define __ftexfetch \
	000 incorect invocation of builtin __ftexfetch 000
	#elif defined(__CUDABE__)		static __inline__ __device__ char tex2DLayered(texture<char, cudaTextureTyp
			e2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
			{
			#if defined(_CHAR_UNSIGNED) || defined(__CHAR_UNSIGNED__)
			uint4 v = __utexfetchl(t, make_float4(x, y, 0, 0), layer);
			#else /* _CHAR_UNSIGNED || __CHAR_UNSIGNED__ */
			int4 v = __itexfetchl(t, make_float4(x, y, 0, 0), layer);
			#endif /* _CHAR_UNSIGNED || __CHAR_UNSIGNED__ */
	extern uint4 __utexfetchi1D(const void*, int4);		return (char)v.x;
	extern int4 __itexfetchi1D(const void*, int4);		}
	extern float4 __ftexfetchi1D(const void*, int4);
	extern uint4 __utexfetch1D(const void*, float4);
	extern int4 __itexfetch1D(const void*, float4);
	extern float4 __ftexfetch1D(const void*, float4);
	extern uint4 __utexfetch2D(const void*, float4);
	extern int4 __itexfetch2D(const void*, float4);
	extern float4 __ftexfetch2D(const void*, float4);
	extern uint4 __utexfetch3D(const void*, float4);
	extern int4 __itexfetch3D(const void*, float4);
	extern float4 __ftexfetch3D(const void*, float4);
	#define __utexfetchi(t, i) \		static __inline__ __device__ signed char tex2DLayered(texture<signed char,
	__utexfetchi1D(t, i)		cudaTextureType2DLayered, cudaReadModeElementType> t, float x, float y, int
	#define __itexfetchi(t, i) \		layer)
	__itexfetchi1D(t, i)		{
	#define __ftexfetchi(t, i) \		int4 v = __itexfetchl(t, make_float4(x, y, 0, 0), layer);
	__ftexfetchi1D(t, i)
	#define __utexfetch(t, i, d) \
	__utexfetch##d##D(t, i)
	#define __itexfetch(t, i, d) \
	__itexfetch##d##D(t, i)
	#define __ftexfetch(t, i, d) \
	__ftexfetch##d##D(t, i)
	#endif /* __cplusplus && __CUDACC__ */		return (signed char)v.x;
			}
	#if defined(__cplusplus) && defined(__CUDACC__)		static __inline__ __device__ unsigned char tex2DLayered(texture<unsigned ch
			ar, cudaTextureType2DLayered, cudaReadModeElementType> t, float x, float y,
			int layer)
			{
			uint4 v = __utexfetchl(t, make_float4(x, y, 0, 0), layer);
	#if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 200		return (unsigned char)v.x;
			}
			static __inline__ __device__ char1 tex2DLayered(texture<char1, cudaTextureT
			ype2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
			{
			int4 v = __itexfetchl(t, make_float4(x, y, 0, 0), layer);
			return make_char1(v.x);
			}
			static __inline__ __device__ uchar1 tex2DLayered(texture<uchar1, cudaTextur
			eType2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
			{
			uint4 v = __utexfetchl(t, make_float4(x, y, 0, 0), layer);
			return make_uchar1(v.x);
			}
			static __inline__ __device__ char2 tex2DLayered(texture<char2, cudaTextureT
			ype2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
			{
			int4 v = __itexfetchl(t, make_float4(x, y, 0, 0), layer);
			return make_char2(v.x, v.y);
			}
			static __inline__ __device__ uchar2 tex2DLayered(texture<uchar2, cudaTextur
			eType2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
			{
			uint4 v = __utexfetchl(t, make_float4(x, y, 0, 0), layer);
			return make_uchar2(v.x, v.y);
			}
			static __inline__ __device__ char4 tex2DLayered(texture<char4, cudaTextureT
			ype2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
			{
			int4 v = __itexfetchl(t, make_float4(x, y, 0, 0), layer);
			return make_char4(v.x, v.y, v.z, v.w);
			}
			static __inline__ __device__ uchar4 tex2DLayered(texture<uchar4, cudaTextur
			eType2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
			{
			uint4 v = __utexfetchl(t, make_float4(x, y, 0, 0), layer);
			return make_uchar4(v.x, v.y, v.z, v.w);
			}
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	/*DEVICE_BUILTIN*/		static __inline__ __device__ short tex2DLayered(texture<short, cudaTextureT
	template<int comp, class T> extern __device__ int4 __itex2Dgather(texture		ype2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
	<T, 2, cudaReadModeElementType> t, float2 i, int c = comp);		{
	/*DEVICE_BUILTIN*/		int4 v = __itexfetchl(t, make_float4(x, y, 0, 0), layer);
	template<int comp, class T> extern __device__ uint4 __utex2Dgather(texture
	<T, 2, cudaReadModeElementType> t, float2 i, int c = comp);
	/*DEVICE_BUILTIN*/
	template<int comp, class T> extern __device__ float4 __ftex2Dgather(texture
	<T, 2, cudaReadModeElementType> t, float2 i, int c = comp);
	#define __tex2DgatherUtil(T, f, r, c) \		return (short)v.x;
	{ T v = f<c>(t, make_float2(x, y)); return r; }		}
	#define __tex2DgatherUtil1(T, f, r) \		static __inline__ __device__ unsigned short tex2DLayered(texture<unsigned s
	__tex2DgatherUtil(T, f, r, 0)		hort, cudaTextureType2DLayered, cudaReadModeElementType> t, float x, float
			y, int layer)
			{
			uint4 v = __utexfetchl(t, make_float4(x, y, 0, 0), layer);
	#define __tex2DgatherUtil2(T, f, r) \		return (unsigned short)v.x;
	if (comp == 1) __tex2DgatherUtil(T, f, r, 1) \		}
	else __tex2DgatherUtil1(T, f, r)
	#define __tex2DgatherUtil3(T, f, r) \		static __inline__ __device__ short1 tex2DLayered(texture<short1, cudaTextur
	if (comp == 2) __tex2DgatherUtil(T, f, r, 2) \		eType2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
	else __tex2DgatherUtil2(T, f, r)		{
			int4 v = __itexfetchl(t, make_float4(x, y, 0, 0), layer);
	#define __tex2DgatherUtil4(T, f, r) \		return make_short1(v.x);
	if (comp == 3) __tex2DgatherUtil(T, f, r, 3) \		}
	else __tex2DgatherUtil3(T, f, r)
	static __inline__ __device__ char4 tex2Dgather(texture<char, 2, cudaReadMod eElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ ushort1 tex2DLayered(texture<ushort1, cudaText ureType2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil1(int4, __itex2Dgather, make_char4(v.x, v.y, v.z, v.w));		uint4 v = __utexfetchl(t, make_float4(x, y, 0, 0), layer);
			return make_ushort1(v.x);
	}		}
	static __inline__ __device__ char4 tex2Dgather(texture<signed char, 2, cuda ReadModeElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ short2 tex2DLayered(texture<short2, cudaTextur eType2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil1(int4, __itex2Dgather, make_char4(v.x, v.y, v.z, v.w));		int4 v = __itexfetchl(t, make_float4(x, y, 0, 0), layer);
			return make_short2(v.x, v.y);
	}		}
	static __inline__ __device__ uchar4 tex2Dgather(texture<unsigned char, 2, c udaReadModeElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ ushort2 tex2DLayered(texture<ushort2, cudaText ureType2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil1(uint4, __utex2Dgather, make_uchar4(v.x, v.y, v.z, v.w)		uint4 v = __utexfetchl(t, make_float4(x, y, 0, 0), layer);
	);
			return make_ushort2(v.x, v.y);
	}		}
	static __inline__ __device__ char4 tex2Dgather(texture<char1, 2, cudaReadMo deElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ short4 tex2DLayered(texture<short4, cudaTextur eType2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil1(int4, __itex2Dgather, make_char4(v.x, v.y, v.z, v.w));		int4 v = __itexfetchl(t, make_float4(x, y, 0, 0), layer);
			return make_short4(v.x, v.y, v.z, v.w);
	}		}
	static __inline__ __device__ uchar4 tex2Dgather(texture<uchar1, 2, cudaRead ModeElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ ushort4 tex2DLayered(texture<ushort4, cudaText ureType2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil1(uint4, __utex2Dgather, make_uchar4(v.x, v.y, v.z, v.w)		uint4 v = __utexfetchl(t, make_float4(x, y, 0, 0), layer);
	);
			return make_ushort4(v.x, v.y, v.z, v.w);
	}		}
	static __inline__ __device__ char4 tex2Dgather(texture<char2, 2, cudaReadMo		/**************************************************************************
	deElementType> t, float x, float y, int comp = 0)		*****
			*
			*
			*
			*
			*
			*
			***************************************************************************
			****/
			static __inline__ __device__ int tex2DLayered(texture<int, cudaTextureType2
			DLayered, cudaReadModeElementType> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil2(int4, __itex2Dgather, make_char4(v.x, v.y, v.z, v.w));		int4 v = __itexfetchl(t, make_float4(x, y, 0, 0), layer);
			return (int)v.x;
	}		}
	static __inline__ __device__ uchar4 tex2Dgather(texture<uchar2, 2, cudaRead ModeElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ unsigned int tex2DLayered(texture<unsigned int , cudaTextureType2DLayered, cudaReadModeElementType> t, float x, float y, i nt layer)
	{		{
	__tex2DgatherUtil2(uint4, __utex2Dgather, make_uchar4(v.x, v.y, v.z, v.w)		uint4 v = __utexfetchl(t, make_float4(x, y, 0, 0), layer);
	);
			return (unsigned int)v.x;
	}		}
	static __inline__ __device__ char4 tex2Dgather(texture<char3, 2, cudaReadMo deElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ int1 tex2DLayered(texture<int1, cudaTextureTyp e2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil3(int4, __itex2Dgather, make_char4(v.x, v.y, v.z, v.w));		int4 v = __itexfetchl(t, make_float4(x, y, 0, 0), layer);
			return make_int1(v.x);
	}		}
	static __inline__ __device__ uchar4 tex2Dgather(texture<uchar3, 2, cudaRead ModeElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ uint1 tex2DLayered(texture<uint1, cudaTextureT ype2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil3(uint4, __utex2Dgather, make_uchar4(v.x, v.y, v.z, v.w)		uint4 v = __utexfetchl(t, make_float4(x, y, 0, 0), layer);
	);
			return make_uint1(v.x);
	}		}
	static __inline__ __device__ char4 tex2Dgather(texture<char4, 2, cudaReadMo deElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ int2 tex2DLayered(texture<int2, cudaTextureTyp e2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil4(int4, __itex2Dgather, make_char4(v.x, v.y, v.z, v.w));		int4 v = __itexfetchl(t, make_float4(x, y, 0, 0), layer);
			return make_int2(v.x, v.y);
	}		}
	static __inline__ __device__ uchar4 tex2Dgather(texture<uchar4, 2, cudaRead ModeElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ uint2 tex2DLayered(texture<uint2, cudaTextureT ype2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil4(uint4, __utex2Dgather, make_uchar4(v.x, v.y, v.z, v.w)		uint4 v = __utexfetchl(t, make_float4(x, y, 0, 0), layer);
	);
			return make_uint2(v.x, v.y);
	}		}
	static __inline__ __device__ short4 tex2Dgather(texture<signed short, 2, cu daReadModeElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ int4 tex2DLayered(texture<int4, cudaTextureTyp e2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil1(int4, __itex2Dgather, make_short4(v.x, v.y, v.z, v.w))		int4 v = __itexfetchl(t, make_float4(x, y, 0, 0), layer);
	;
			return make_int4(v.x, v.y, v.z, v.w);
	}		}
	static __inline__ __device__ ushort4 tex2Dgather(texture<unsigned short, 2, cudaReadModeElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ uint4 tex2DLayered(texture<uint4, cudaTextureT ype2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil1(uint4, __utex2Dgather, make_ushort4(v.x, v.y, v.z, v.w		uint4 v = __utexfetchl(t, make_float4(x, y, 0, 0), layer);
	));
			return make_uint4(v.x, v.y, v.z, v.w);
	}		}
	static __inline__ __device__ short4 tex2Dgather(texture<short1, 2, cudaRead		#if !defined(__LP64__)
	ModeElementType> t, float x, float y, int comp = 0)
			/**************************************************************************
			*****
			*
			*
			*
			*
			*
			*
			***************************************************************************
			****/
			static __inline__ __device__ long tex2DLayered(texture<long, cudaTextureTyp
			e2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil1(int4, __itex2Dgather, make_short4(v.x, v.y, v.z, v.w))		int4 v = __itexfetchl(t, make_float4(x, y, 0, 0), layer);
	;
			return (long)v.x;
	}		}
	static __inline__ __device__ ushort4 tex2Dgather(texture<ushort1, 2, cudaRe adModeElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ unsigned long tex2DLayered(texture<unsigned lo ng, cudaTextureType2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil1(uint4, __utex2Dgather, make_ushort4(v.x, v.y, v.z, v.w		uint4 v = __utexfetchl(t, make_float4(x, y, 0, 0), layer);
	));
			return (unsigned long)v.x;
	}		}
	static __inline__ __device__ short4 tex2Dgather(texture<short2, 2, cudaRead ModeElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ long1 tex2DLayered(texture<long1, cudaTextureT ype2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil2(int4, __itex2Dgather, make_short4(v.x, v.y, v.z, v.w))		int4 v = __itexfetchl(t, make_float4(x, y, 0, 0), layer);
	;
			return make_long1(v.x);
	}		}
	static __inline__ __device__ ushort4 tex2Dgather(texture<ushort2, 2, cudaRe adModeElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ ulong1 tex2DLayered(texture<ulong1, cudaTextur eType2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil2(uint4, __utex2Dgather, make_ushort4(v.x, v.y, v.z, v.w		uint4 v = __utexfetchl(t, make_float4(x, y, 0, 0), layer);
	));
			return make_ulong1(v.x);
	}		}
	static __inline__ __device__ short4 tex2Dgather(texture<short3, 2, cudaRead ModeElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ long2 tex2DLayered(texture<long2, cudaTextureT ype2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil3(int4, __itex2Dgather, make_short4(v.x, v.y, v.z, v.w))		int4 v = __itexfetchl(t, make_float4(x, y, 0, 0), layer);
	;
			return make_long2(v.x, v.y);
	}		}
	static __inline__ __device__ ushort4 tex2Dgather(texture<ushort3, 2, cudaRe adModeElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ ulong2 tex2DLayered(texture<ulong2, cudaTextur eType2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil3(uint4, __utex2Dgather, make_ushort4(v.x, v.y, v.z, v.w		uint4 v = __utexfetchl(t, make_float4(x, y, 0, 0), layer);
	));
			return make_ulong2(v.x, v.y);
	}		}
	static __inline__ __device__ short4 tex2Dgather(texture<short4, 2, cudaRead ModeElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ long4 tex2DLayered(texture<long4, cudaTextureT ype2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil4(int4, __itex2Dgather, make_short4(v.x, v.y, v.z, v.w))		int4 v = __itexfetchl(t, make_float4(x, y, 0, 0), layer);
	;
			return make_long4(v.x, v.y, v.z, v.w);
	}		}
	static __inline__ __device__ ushort4 tex2Dgather(texture<ushort4, 2, cudaRe adModeElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ ulong4 tex2DLayered(texture<ulong4, cudaTextur eType2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil4(uint4, __utex2Dgather, make_ushort4(v.x, v.y, v.z, v.w		uint4 v = __utexfetchl(t, make_float4(x, y, 0, 0), layer);
	));
			return make_ulong4(v.x, v.y, v.z, v.w);
	}		}
	static __inline__ __device__ int4 tex2Dgather(texture<signed int, 2, cudaRe		#endif /* !__LP64__ */
	adModeElementType> t, float x, float y, int comp = 0)
			/**************************************************************************
			*****
			*
			*
			*
			*
			*
			*
			***************************************************************************
			****/
			static __inline__ __device__ float tex2DLayered(texture<float, cudaTextureT
			ype2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil1(int4, __itex2Dgather, v);		float4 v = __ftexfetchl(t, make_float4(x, y, 0, 0), layer);
			return v.x;
	}		}
	static __inline__ __device__ uint4 tex2Dgather(texture<unsigned int, 2, cud aReadModeElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ float1 tex2DLayered(texture<float1, cudaTextur eType2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil1(uint4, __utex2Dgather, v);		float4 v = __ftexfetchl(t, make_float4(x, y, 0, 0), layer);
			return make_float1(v.x);
	}		}
	static __inline__ __device__ int4 tex2Dgather(texture<int1, 2, cudaReadMode ElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ float2 tex2DLayered(texture<float2, cudaTextur eType2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil1(int4, __itex2Dgather, v);		float4 v = __ftexfetchl(t, make_float4(x, y, 0, 0), layer);
			return make_float2(v.x, v.y);
	}		}
	static __inline__ __device__ uint4 tex2Dgather(texture<uint1, 2, cudaReadMo deElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ float4 tex2DLayered(texture<float4, cudaTextur eType2DLayered, cudaReadModeElementType> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil1(uint4, __utex2Dgather, v);		float4 v = __ftexfetchl(t, make_float4(x, y, 0, 0), layer);
			return make_float4(v.x, v.y, v.z, v.w);
	}		}
	static __inline__ __device__ int4 tex2Dgather(texture<int2, 2, cudaReadMode		/**************************************************************************
	ElementType> t, float x, float y, int comp = 0)		*****
			*
			*
			*
			*
			*
			*
			***************************************************************************
			****/
			static __inline__ __device__ float tex2DLayered(texture<char, cudaTextureTy
			pe2DLayered, cudaReadModeNormalizedFloat> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil2(int4, __itex2Dgather, v);		#if defined(_CHAR_UNSIGNED) || defined(__CHAR_UNSIGNED__)
			uint4 v = __utexfetchl(t, make_float4(x, y, 0, 0), layer);
			#else /* _CHAR_UNSIGNED || __CHAR_UNSIGNED__ */
			int4 v = __itexfetchl(t, make_float4(x, y, 0, 0), layer);
			#endif /* _CHAR_UNSIGNED || __CHAR_UNSIGNED__ */
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return w.x;
	}		}
	static __inline__ __device__ uint4 tex2Dgather(texture<uint2, 2, cudaReadMo deElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ float tex2DLayered(texture<signed char, cudaTe xtureType2DLayered, cudaReadModeNormalizedFloat> t, float x, float y, int l ayer)
	{		{
	__tex2DgatherUtil2(uint4, __utex2Dgather, v);		int4 v = __itexfetchl(t, make_float4(x, y, 0, 0), layer);
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return w.x;
	}		}
	static __inline__ __device__ int4 tex2Dgather(texture<int3, 2, cudaReadMode ElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ float tex2DLayered(texture<unsigned char, cuda TextureType2DLayered, cudaReadModeNormalizedFloat> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil3(int4, __itex2Dgather, v);		uint4 v = __utexfetchl(t, make_float4(x, y, 0, 0), layer);
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return w.x;
	}		}
	static __inline__ __device__ uint4 tex2Dgather(texture<uint3, 2, cudaReadMo deElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ float1 tex2DLayered(texture<char1, cudaTexture Type2DLayered, cudaReadModeNormalizedFloat> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil3(uint4, __utex2Dgather, v);		int4 v = __itexfetchl(t, make_float4(x, y, 0, 0), layer);
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return make_float1(w.x);
	}		}
	static __inline__ __device__ int4 tex2Dgather(texture<int4, 2, cudaReadMode ElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ float1 tex2DLayered(texture<uchar1, cudaTextur eType2DLayered, cudaReadModeNormalizedFloat> t, float x, float y, int layer )
	{		{
	__tex2DgatherUtil4(int4, __itex2Dgather, v);		uint4 v = __utexfetchl(t, make_float4(x, y, 0, 0), layer);
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return make_float1(w.x);
	}		}
	static __inline__ __device__ uint4 tex2Dgather(texture<uint4, 2, cudaReadMo deElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ float2 tex2DLayered(texture<char2, cudaTexture Type2DLayered, cudaReadModeNormalizedFloat> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil4(uint4, __utex2Dgather, v);		int4 v = __itexfetchl(t, make_float4(x, y, 0, 0), layer);
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return make_float2(w.x, w.y);
	}		}
	static __inline__ __device__ float4 tex2Dgather(texture<float, 2, cudaReadM odeElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ float2 tex2DLayered(texture<uchar2, cudaTextur eType2DLayered, cudaReadModeNormalizedFloat> t, float x, float y, int layer )
	{		{
	__tex2DgatherUtil1(float4, __ftex2Dgather, v);		uint4 v = __utexfetchl(t, make_float4(x, y, 0, 0), layer);
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return make_float2(w.x, w.y);
	}		}
	static __inline__ __device__ float4 tex2Dgather(texture<float1, 2, cudaRead ModeElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ float4 tex2DLayered(texture<char4, cudaTexture Type2DLayered, cudaReadModeNormalizedFloat> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil1(float4, __ftex2Dgather, v);		int4 v = __itexfetchl(t, make_float4(x, y, 0, 0), layer);
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return make_float4(w.x, w.y, w.z, w.w);
	}		}
	static __inline__ __device__ float4 tex2Dgather(texture<float2, 2, cudaRead ModeElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ float4 tex2DLayered(texture<uchar4, cudaTextur eType2DLayered, cudaReadModeNormalizedFloat> t, float x, float y, int layer )
	{		{
	__tex2DgatherUtil2(float4, __ftex2Dgather, v);		uint4 v = __utexfetchl(t, make_float4(x, y, 0, 0), layer);
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return make_float4(w.x, w.y, w.z, w.w);
	}		}
	static __inline__ __device__ float4 tex2Dgather(texture<float3, 2, cudaRead		/**************************************************************************
	ModeElementType> t, float x, float y, int comp = 0)		*****
			*
			*
			*
			*
			*
			*
			***************************************************************************
			****/
			static __inline__ __device__ float tex2DLayered(texture<short, cudaTextureT
			ype2DLayered, cudaReadModeNormalizedFloat> t, float x, float y, int layer)
	{		{
	__tex2DgatherUtil3(float4, __ftex2Dgather, v);		int4 v = __itexfetchl(t, make_float4(x, y, 0, 0), layer);
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return w.x;
	}		}
	static __inline__ __device__ float4 tex2Dgather(texture<float4, 2, cudaRead ModeElementType> t, float x, float y, int comp = 0)		static __inline__ __device__ float tex2DLayered(texture<unsigned short, cud aTextureType2DLayered, cudaReadModeNormalizedFloat> t, float x, float y, in t layer)
	{		{
	__tex2DgatherUtil4(float4, __ftex2Dgather, v);		uint4 v = __utexfetchl(t, make_float4(x, y, 0, 0), layer);
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return w.x;
	}		}
	#undef __tex2DgatherUtil		static __inline__ __device__ float1 tex2DLayered(texture<short1, cudaTextur
	#undef __tex2DgatherUtil1		eType2DLayered, cudaReadModeNormalizedFloat> t, float x, float y, int layer
	#undef __tex2DgatherUtil2		)
	#undef __tex2DgatherUtil3		{
	#undef __tex2DgatherUtil4		int4 v = __itexfetchl(t, make_float4(x, y, 0, 0), layer);
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
	#define __utex2Dgather \		return make_float1(w.x);
	000 incorect invocation of builtin __utex2Dgather 000		}
	#define __itex2Dgather \
	000 incorect invocation of builtin __itex2Dgather 000
	#define __ftex2Dgather \
	000 incorect invocation of builtin __ftex2Dgather 000
	#endif /* !__CUDA_ARCH__ || __CUDA_ARCH__ >= 200 */		static __inline__ __device__ float1 tex2DLayered(texture<ushort1, cudaTextu
			reType2DLayered, cudaReadModeNormalizedFloat> t, float x, float y, int laye
			r)
			{
			uint4 v = __utexfetchl(t, make_float4(x, y, 0, 0), layer);
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return make_float1(w.x);
			}
			static __inline__ __device__ float2 tex2DLayered(texture<short2, cudaTextur
			eType2DLayered, cudaReadModeNormalizedFloat> t, float x, float y, int layer
			)
			{
			int4 v = __itexfetchl(t, make_float4(x, y, 0, 0), layer);
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return make_float2(w.x, w.y);
			}
			static __inline__ __device__ float2 tex2DLayered(texture<ushort2, cudaTextu
			reType2DLayered, cudaReadModeNormalizedFloat> t, float x, float y, int laye
			r)
			{
			uint4 v = __utexfetchl(t, make_float4(x, y, 0, 0), layer);
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return make_float2(w.x, w.y);
			}
			static __inline__ __device__ float4 tex2DLayered(texture<short4, cudaTextur
			eType2DLayered, cudaReadModeNormalizedFloat> t, float x, float y, int layer
			)
			{
			int4 v = __itexfetchl(t, make_float4(x, y, 0, 0), layer);
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return make_float4(w.x, w.y, w.z, w.w);
			}
			static __inline__ __device__ float4 tex2DLayered(texture<ushort4, cudaTextu
			reType2DLayered, cudaReadModeNormalizedFloat> t, float x, float y, int laye
			r)
			{
			uint4 v = __utexfetchl(t, make_float4(x, y, 0, 0), layer);
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return make_float4(w.x, w.y, w.z, w.w);
			}
			/**************************************************************************
			*****
			*
			*
			* 3D Texture functions
			*
			*
			*
			***************************************************************************
			****/
			static __inline__ __device__ char tex3D(texture<char, cudaTextureType3D, cu
			daReadModeElementType> t, float x, float y, float z)
			{
			#if defined(_CHAR_UNSIGNED) || defined(__CHAR_UNSIGNED__)
			uint4 v = __utexfetch(t, make_float4(x, y, z, 0));
			#else /* _CHAR_UNSIGNED || __CHAR_UNSIGNED__ */
			int4 v = __itexfetch(t, make_float4(x, y, z, 0));
			#endif /* _CHAR_UNSIGNED || __CHAR_UNSIGNED__ */
			return (char)v.x;
			}
			static __inline__ __device__ signed char tex3D(texture<signed char, cudaTex
			tureType3D, cudaReadModeElementType> t, float x, float y, float z)
			{
			int4 v = __itexfetch(t, make_float4(x, y, z, 0));
			return (signed char)v.x;
			}
			static __inline__ __device__ unsigned char tex3D(texture<unsigned char, cud
			aTextureType3D, cudaReadModeElementType> t, float x, float y, float z)
			{
			uint4 v = __utexfetch(t, make_float4(x, y, z, 0));
			return (unsigned char)v.x;
			}
			static __inline__ __device__ char1 tex3D(texture<char1, cudaTextureType3D,
			cudaReadModeElementType> t, float x, float y, float z)
			{
			int4 v = __itexfetch(t, make_float4(x, y, z, 0));
			return make_char1(v.x);
			}
			static __inline__ __device__ uchar1 tex3D(texture<uchar1, cudaTextureType3D
			, cudaReadModeElementType> t, float x, float y, float z)
			{
			uint4 v = __utexfetch(t, make_float4(x, y, z, 0));
			return make_uchar1(v.x);
			}
			static __inline__ __device__ char2 tex3D(texture<char2, cudaTextureType3D,
			cudaReadModeElementType> t, float x, float y, float z)
			{
			int4 v = __itexfetch(t, make_float4(x, y, z, 0));
			return make_char2(v.x, v.y);
			}
			static __inline__ __device__ uchar2 tex3D(texture<uchar2, cudaTextureType3D
			, cudaReadModeElementType> t, float x, float y, float z)
			{
			uint4 v = __utexfetch(t, make_float4(x, y, z, 0));
			return make_uchar2(v.x, v.y);
			}
			static __inline__ __device__ char4 tex3D(texture<char4, cudaTextureType3D,
			cudaReadModeElementType> t, float x, float y, float z)
			{
			int4 v = __itexfetch(t, make_float4(x, y, z, 0));
			return make_char4(v.x, v.y, v.z, v.w);
			}
			static __inline__ __device__ uchar4 tex3D(texture<uchar4, cudaTextureType3D
			, cudaReadModeElementType> t, float x, float y, float z)
			{
			uint4 v = __utexfetch(t, make_float4(x, y, z, 0));
			return make_uchar4(v.x, v.y, v.z, v.w);
			}
			/**************************************************************************
			*****
			*
			*
			*
			*
			*
			*
			***************************************************************************
			****/
			static __inline__ __device__ short tex3D(texture<short, cudaTextureType3D,
			cudaReadModeElementType> t, float x, float y, float z)
			{
			int4 v = __itexfetch(t, make_float4(x, y, z, 0));
			return (short)v.x;
			}
			static __inline__ __device__ unsigned short tex3D(texture<unsigned short, c
			udaTextureType3D, cudaReadModeElementType> t, float x, float y, float z)
			{
			uint4 v = __utexfetch(t, make_float4(x, y, z, 0));
			return (unsigned short)v.x;
			}
			static __inline__ __device__ short1 tex3D(texture<short1, cudaTextureType3D
			, cudaReadModeElementType> t, float x, float y, float z)
			{
			int4 v = __itexfetch(t, make_float4(x, y, z, 0));
			return make_short1(v.x);
			}
			static __inline__ __device__ ushort1 tex3D(texture<ushort1, cudaTextureType
			3D, cudaReadModeElementType> t, float x, float y, float z)
			{
			uint4 v = __utexfetch(t, make_float4(x, y, z, 0));
			return make_ushort1(v.x);
			}
			static __inline__ __device__ short2 tex3D(texture<short2, cudaTextureType3D
			, cudaReadModeElementType> t, float x, float y, float z)
			{
			int4 v = __itexfetch(t, make_float4(x, y, z, 0));
			return make_short2(v.x, v.y);
			}
			static __inline__ __device__ ushort2 tex3D(texture<ushort2, cudaTextureType
			3D, cudaReadModeElementType> t, float x, float y, float z)
			{
			uint4 v = __utexfetch(t, make_float4(x, y, z, 0));
			return make_ushort2(v.x, v.y);
			}
			static __inline__ __device__ short4 tex3D(texture<short4, cudaTextureType3D
			, cudaReadModeElementType> t, float x, float y, float z)
			{
			int4 v = __itexfetch(t, make_float4(x, y, z, 0));
			return make_short4(v.x, v.y, v.z, v.w);
			}
			static __inline__ __device__ ushort4 tex3D(texture<ushort4, cudaTextureType
			3D, cudaReadModeElementType> t, float x, float y, float z)
			{
			uint4 v = __utexfetch(t, make_float4(x, y, z, 0));
			return make_ushort4(v.x, v.y, v.z, v.w);
			}
			/**************************************************************************
			*****
			*
			*
			*
			*
			*
			*
			***************************************************************************
			****/
			static __inline__ __device__ int tex3D(texture<int, cudaTextureType3D, cuda
			ReadModeElementType> t, float x, float y, float z)
			{
			int4 v = __itexfetch(t, make_float4(x, y, z, 0));
			return (int)v.x;
			}
			static __inline__ __device__ unsigned int tex3D(texture<unsigned int, cudaT
			extureType3D, cudaReadModeElementType> t, float x, float y, float z)
			{
			uint4 v = __utexfetch(t, make_float4(x, y, z, 0));
			return (unsigned int)v.x;
			}
			static __inline__ __device__ int1 tex3D(texture<int1, cudaTextureType3D, cu
			daReadModeElementType> t, float x, float y, float z)
			{
			int4 v = __itexfetch(t, make_float4(x, y, z, 0));
			return make_int1(v.x);
			}
			static __inline__ __device__ uint1 tex3D(texture<uint1, cudaTextureType3D,
			cudaReadModeElementType> t, float x, float y, float z)
			{
			uint4 v = __utexfetch(t, make_float4(x, y, z, 0));
			return make_uint1(v.x);
			}
			static __inline__ __device__ int2 tex3D(texture<int2, cudaTextureType3D, cu
			daReadModeElementType> t, float x, float y, float z)
			{
			int4 v = __itexfetch(t, make_float4(x, y, z, 0));
			return make_int2(v.x, v.y);
			}
			static __inline__ __device__ uint2 tex3D(texture<uint2, cudaTextureType3D,
			cudaReadModeElementType> t, float x, float y, float z)
			{
			uint4 v = __utexfetch(t, make_float4(x, y, z, 0));
			return make_uint2(v.x, v.y);
			}
			static __inline__ __device__ int4 tex3D(texture<int4, cudaTextureType3D, cu
			daReadModeElementType> t, float x, float y, float z)
			{
			int4 v = __itexfetch(t, make_float4(x, y, z, 0));
			return make_int4(v.x, v.y, v.z, v.w);
			}
			static __inline__ __device__ uint4 tex3D(texture<uint4, cudaTextureType3D,
			cudaReadModeElementType> t, float x, float y, float z)
			{
			uint4 v = __utexfetch(t, make_float4(x, y, z, 0));
			return make_uint4(v.x, v.y, v.z, v.w);
			}
			#if !defined(__LP64__)
			/**************************************************************************
			*****
			*
			*
			*
			*
			*
			*
			***************************************************************************
			****/
			static __inline__ __device__ long tex3D(texture<long, cudaTextureType3D, cu
			daReadModeElementType> t, float x, float y, float z)
			{
			int4 v = __itexfetch(t, make_float4(x, y, z, 0));
			return (long)v.x;
			}
			static __inline__ __device__ unsigned long tex3D(texture<unsigned long, cud
			aTextureType3D, cudaReadModeElementType> t, float x, float y, float z)
			{
			uint4 v = __utexfetch(t, make_float4(x, y, z, 0));
			return (unsigned long)v.x;
			}
			static __inline__ __device__ long1 tex3D(texture<long1, cudaTextureType3D,
			cudaReadModeElementType> t, float x, float y, float z)
			{
			int4 v = __itexfetch(t, make_float4(x, y, z, 0));
			return make_long1(v.x);
			}
			static __inline__ __device__ ulong1 tex3D(texture<ulong1, cudaTextureType3D
			, cudaReadModeElementType> t, float x, float y, float z)
			{
			uint4 v = __utexfetch(t, make_float4(x, y, z, 0));
			return make_ulong1(v.x);
			}
			static __inline__ __device__ long2 tex3D(texture<long2, cudaTextureType3D,
			cudaReadModeElementType> t, float x, float y, float z)
			{
			int4 v = __itexfetch(t, make_float4(x, y, z, 0));
			return make_long2(v.x, v.y);
			}
			static __inline__ __device__ ulong2 tex3D(texture<ulong2, cudaTextureType3D
			, cudaReadModeElementType> t, float x, float y, float z)
			{
			uint4 v = __utexfetch(t, make_float4(x, y, z, 0));
			return make_ulong2(v.x, v.y);
			}
			static __inline__ __device__ long4 tex3D(texture<long4, cudaTextureType3D,
			cudaReadModeElementType> t, float x, float y, float z)
			{
			int4 v = __itexfetch(t, make_float4(x, y, z, 0));
			return make_long4(v.x, v.y, v.z, v.w);
			}
			static __inline__ __device__ ulong4 tex3D(texture<ulong4, cudaTextureType3D
			, cudaReadModeElementType> t, float x, float y, float z)
			{
			uint4 v = __utexfetch(t, make_float4(x, y, z, 0));
			return make_ulong4(v.x, v.y, v.z, v.w);
			}
			#endif /* !__LP64__ */
			/**************************************************************************
			*****
			*
			*
			*
			*
			*
			*
			***************************************************************************
			****/
			static __inline__ __device__ float tex3D(texture<float, cudaTextureType3D,
			cudaReadModeElementType> t, float x, float y, float z)
			{
			float4 v = __ftexfetch(t, make_float4(x, y, z, 0));
			return v.x;
			}
			static __inline__ __device__ float1 tex3D(texture<float1, cudaTextureType3D
			, cudaReadModeElementType> t, float x, float y, float z)
			{
			float4 v = __ftexfetch(t, make_float4(x, y, z, 0));
			return make_float1(v.x);
			}
			static __inline__ __device__ float2 tex3D(texture<float2, cudaTextureType3D
			, cudaReadModeElementType> t, float x, float y, float z)
			{
			float4 v = __ftexfetch(t, make_float4(x, y, z, 0));
			return make_float2(v.x, v.y);
			}
			static __inline__ __device__ float4 tex3D(texture<float4, cudaTextureType3D
			, cudaReadModeElementType> t, float x, float y, float z)
			{
			float4 v = __ftexfetch(t, make_float4(x, y, z, 0));
			return make_float4(v.x, v.y, v.z, v.w);
			}
			/**************************************************************************
			*****
			*
			*
			*
			*
			*
			*
			***************************************************************************
			****/
			static __inline__ __device__ float tex3D(texture<char, cudaTextureType3D, c
			udaReadModeNormalizedFloat> t, float x, float y, float z)
			{
			#if defined(_CHAR_UNSIGNED) || defined(__CHAR_UNSIGNED__)
			uint4 v = __utexfetch(t, make_float4(x, y, z, 0));
			#else /* _CHAR_UNSIGNED || __CHAR_UNSIGNED__ */
			int4 v = __itexfetch(t, make_float4(x, y, z, 0));
			#endif /* _CHAR_UNSIGNED || __CHAR_UNSIGNED__ */
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return w.x;
			}
			static __inline__ __device__ float tex3D(texture<signed char, cudaTextureTy
			pe3D, cudaReadModeNormalizedFloat> t, float x, float y, float z)
			{
			int4 v = __itexfetch(t, make_float4(x, y, z, 0));
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return w.x;
			}
			static __inline__ __device__ float tex3D(texture<unsigned char, cudaTexture
			Type3D, cudaReadModeNormalizedFloat> t, float x, float y, float z)
			{
			uint4 v = __utexfetch(t, make_float4(x, y, z, 0));
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return w.x;
			}
			static __inline__ __device__ float1 tex3D(texture<char1, cudaTextureType3D,
			cudaReadModeNormalizedFloat> t, float x, float y, float z)
			{
			int4 v = __itexfetch(t, make_float4(x, y, z, 0));
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return make_float1(w.x);
			}
			static __inline__ __device__ float1 tex3D(texture<uchar1, cudaTextureType3D
			, cudaReadModeNormalizedFloat> t, float x, float y, float z)
			{
			uint4 v = __utexfetch(t, make_float4(x, y, z, 0));
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return make_float1(w.x);
			}
			static __inline__ __device__ float2 tex3D(texture<char2, cudaTextureType3D,
			cudaReadModeNormalizedFloat> t, float x, float y, float z)
			{
			int4 v = __itexfetch(t, make_float4(x, y, z, 0));
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return make_float2(w.x, w.y);
			}
			static __inline__ __device__ float2 tex3D(texture<uchar2, cudaTextureType3D
			, cudaReadModeNormalizedFloat> t, float x, float y, float z)
			{
			uint4 v = __utexfetch(t, make_float4(x, y, z, 0));
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return make_float2(w.x, w.y);
			}
			static __inline__ __device__ float4 tex3D(texture<char4, cudaTextureType3D,
			cudaReadModeNormalizedFloat> t, float x, float y, float z)
			{
			int4 v = __itexfetch(t, make_float4(x, y, z, 0));
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return make_float4(w.x, w.y, w.z, w.w);
			}
			static __inline__ __device__ float4 tex3D(texture<uchar4, cudaTextureType3D
			, cudaReadModeNormalizedFloat> t, float x, float y, float z)
			{
			uint4 v = __utexfetch(t, make_float4(x, y, z, 0));
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return make_float4(w.x, w.y, w.z, w.w);
			}
			/**************************************************************************
			*****
			*
			*
			*
			*
			*
			*
			***************************************************************************
			****/
			static __inline__ __device__ float tex3D(texture<short, cudaTextureType3D,
			cudaReadModeNormalizedFloat> t, float x, float y, float z)
			{
			int4 v = __itexfetch(t, make_float4(x, y, z, 0));
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return w.x;
			}
			static __inline__ __device__ float tex3D(texture<unsigned short, cudaTextur
			eType3D, cudaReadModeNormalizedFloat> t, float x, float y, float z)
			{
			uint4 v = __utexfetch(t, make_float4(x, y, z, 0));
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return w.x;
			}
			static __inline__ __device__ float1 tex3D(texture<short1, cudaTextureType3D
			, cudaReadModeNormalizedFloat> t, float x, float y, float z)
			{
			int4 v = __itexfetch(t, make_float4(x, y, z, 0));
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return make_float1(w.x);
			}
			static __inline__ __device__ float1 tex3D(texture<ushort1, cudaTextureType3
			D, cudaReadModeNormalizedFloat> t, float x, float y, float z)
			{
			uint4 v = __utexfetch(t, make_float4(x, y, z, 0));
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return make_float1(w.x);
			}
			static __inline__ __device__ float2 tex3D(texture<short2, cudaTextureType3D
			, cudaReadModeNormalizedFloat> t, float x, float y, float z)
			{
			int4 v = __itexfetch(t, make_float4(x, y, z, 0));
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return make_float2(w.x, w.y);
			}
			static __inline__ __device__ float2 tex3D(texture<ushort2, cudaTextureType3
			D, cudaReadModeNormalizedFloat> t, float x, float y, float z)
			{
			uint4 v = __utexfetch(t, make_float4(x, y, z, 0));
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return make_float2(w.x, w.y);
			}
			static __inline__ __device__ float4 tex3D(texture<short4, cudaTextureType3D
			, cudaReadModeNormalizedFloat> t, float x, float y, float z)
			{
			int4 v = __itexfetch(t, make_float4(x, y, z, 0));
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return make_float4(w.x, w.y, w.z, w.w);
			}
			static __inline__ __device__ float4 tex3D(texture<ushort4, cudaTextureType3
			D, cudaReadModeNormalizedFloat> t, float x, float y, float z)
			{
			uint4 v = __utexfetch(t, make_float4(x, y, z, 0));
			float4 w = make_float4(__int_as_float(v.x), __int_as_float(v.y), __int_as
			_float(v.z), __int_as_float(v.w));
			return make_float4(w.x, w.y, w.z, w.w);
			}
	#elif defined(__CUDABE__)		#elif defined(__CUDABE__)
	extern uint4 __utex2Dgather0(const void*, float2);		extern uint4 __utexfetchi1D(const void*, int4);
	extern uint4 __utex2Dgather1(const void*, float2);		extern int4 __itexfetchi1D(const void*, int4);
	extern uint4 __utex2Dgather2(const void*, float2);		extern float4 __ftexfetchi1D(const void*, int4);
	extern uint4 __utex2Dgather3(const void*, float2);		extern uint4 __utexfetch1D(const void*, float4);
	extern int4 __itex2Dgather0(const void*, float2);		extern int4 __itexfetch1D(const void*, float4);
	extern int4 __itex2Dgather1(const void*, float2);		extern float4 __ftexfetch1D(const void*, float4);
	extern int4 __itex2Dgather2(const void*, float2);		extern uint4 __utexfetch2D(const void*, float4);
	extern int4 __itex2Dgather3(const void*, float2);		extern int4 __itexfetch2D(const void*, float4);
	extern float4 __ftex2Dgather0(const void*, float2);		extern float4 __ftexfetch2D(const void*, float4);
	extern float4 __ftex2Dgather1(const void*, float2);		extern uint4 __utexfetch3D(const void*, float4);
	extern float4 __ftex2Dgather2(const void*, float2);		extern int4 __itexfetch3D(const void*, float4);
	extern float4 __ftex2Dgather3(const void*, float2);		extern float4 __ftexfetch3D(const void*, float4);
			extern uint4 __utexfetchl1D(const void*, float4, int);
			extern int4 __itexfetchl1D(const void*, float4, int);
			extern float4 __ftexfetchl1D(const void*, float4, int);
			extern uint4 __utexfetchl2D(const void*, float4, int);
			extern int4 __itexfetchl2D(const void*, float4, int);
			extern float4 __ftexfetchl2D(const void*, float4, int);
	#define __utex2Dgather(t, i, c) \		#define __utexfetchi(t, i) \
	__utex2Dgather##c(t, i)		__utexfetchi1D(t, i)
	#define __itex2Dgather(t, i, c) \		#define __itexfetchi(t, i) \
	__itex2Dgather##c(t, i)		__itexfetchi1D(t, i)
	#define __ftex2Dgather(t, i, c) \		#define __ftexfetchi(t, i) \
	__ftex2Dgather##c(t, i)		__ftexfetchi1D(t, i)
			#define __utexfetch(t, i, d) \
			__utexfetch##d##D(t, i)
			#define __itexfetch(t, i, d) \
			__itexfetch##d##D(t, i)
			#define __ftexfetch(t, i, d) \
			__ftexfetch##d##D(t, i)
			#define __utexfetchl(t, i, l, d) \
			__utexfetchl##d##D(t, i, l)
			#define __itexfetchl(t, i, l, d) \
			__itexfetchl##d##D(t, i, l)
			#define __ftexfetchl(t, i, l, d) \
			__ftexfetchl##d##D(t, i, l)
	#endif /* __cplusplus && __CUDACC__ */		#endif /* __cplusplus && __CUDACC__ */
	#endif /* !__TEXTURE_FETCH_FUNCTIONS_H__ */		#endif /* !__TEXTURE_FETCH_FUNCTIONS_H__ */
End of changes. 385 change blocks.
	523 lines changed or deleted		1354 lines changed or added

	texture_types.h		texture_types.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(__TEXTURE_TYPES_H__)		#if !defined(__TEXTURE_TYPES_H__)
	#define __TEXTURE_TYPES_H__		#define __TEXTURE_TYPES_H__
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
	skipping to change at line 59		skipping to change at line 73
	*		*
	* @{		* @{
	*/		*/
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
			#define cudaTextureType1D 0x01
			#define cudaTextureType2D 0x02
			#define cudaTextureType3D 0x03
			#define cudaTextureType1DLayered 0xF1
			#define cudaTextureType2DLayered 0xF2
	/**		/**
	* CUDA texture address modes		* CUDA texture address modes
	*/		*/
	/*DEVICE_BUILTIN*/		/*DEVICE_BUILTIN*/
	enum cudaTextureAddressMode		enum cudaTextureAddressMode
	{		{
	cudaAddressModeWrap = 0, /**< Wrapping address mode */		cudaAddressModeWrap = 0, /**< Wrapping address mode */
	cudaAddressModeClamp = 1, /**< Clamp to edge address mode */		cudaAddressModeClamp = 1, /**< Clamp to edge address mode */
	cudaAddressModeMirror = 2, /**< Mirror address mode */		cudaAddressModeMirror = 2, /**< Mirror address mode */
	cudaAddressModeBorder = 3 /**< Border address mode */		cudaAddressModeBorder = 3 /**< Border address mode */
	skipping to change at line 113		skipping to change at line 133
	*/		*/
	enum cudaTextureFilterMode filterMode;		enum cudaTextureFilterMode filterMode;
	/**		/**
	* Texture address mode for up to 3 dimensions		* Texture address mode for up to 3 dimensions
	*/		*/
	enum cudaTextureAddressMode addressMode[3];		enum cudaTextureAddressMode addressMode[3];
	/**		/**
	* Channel descriptor for the texture reference		* Channel descriptor for the texture reference
	*/		*/
	struct cudaChannelFormatDesc channelDesc;		struct cudaChannelFormatDesc channelDesc;
	int __cudaReserved[16];		/**
			* Perform sRGB->linear conversion during texture read
			*/
			int sRGB;
			int __cudaReserved[15];
	};		};
	/** @} */		/** @} */
	/** @} */ /* END CUDART_TYPES */		/** @} */ /* END CUDART_TYPES */
	#endif /* !__TEXTURE_TYPES_H__ */		#endif /* !__TEXTURE_TYPES_H__ */
End of changes. 8 change blocks.
	29 lines changed or deleted		52 lines changed or added

	vector_functions.h		vector_functions.h
	/*		/*
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 1993-2011 NVIDIA Corporation. All rights reserved.
	*		*
	* NOTICE TO USER:		* NOTICE TO LICENSEE:
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* This source code and/or documentation ("Licensed Deliverables") are
	* international Copyright laws. Users and possessors of this source code		* subject to NVIDIA intellectual property rights under U.S. and
	* are hereby granted a nonexclusive, royalty-free license to use this code		* international Copyright laws.
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* These Licensed Deliverables contained herein is PROPRIETARY and
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* CONFIDENTIAL to NVIDIA and is being provided under the terms and
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* conditions of a form of NVIDIA software license agreement by and
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* between NVIDIA and Licensee ("License Agreement") or electronically
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* accepted by Licensee. Notwithstanding any terms or conditions to
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL		* the contrary in the License Agreement, reproduction or disclosure
	,		* of the Licensed Deliverables to any third party without the express
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS		* written consent of NVIDIA is prohibited.
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
	* "commercial computer software" and "commercial computer software		* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)		* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
	* and is provided to the U.S. Government only as a commercial end item.		* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through		* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the		* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
	* source code with only those rights set forth herein.		* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
			* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
			* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
			* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
			* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
			* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
			* OF THESE LICENSED DELIVERABLES.
	*		*
	* Any use of this source code in individual and commercial software must		* U.S. Government End Users. These Licensed Deliverables are a
	* include, in the user documentation and internal comments to the code,		* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
	* the above Disclaimer and U.S. Government End Users Notice.		* 1995), consisting of "commercial computer software" and "commercial
			* computer software documentation" as such terms are used in 48
			* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
			* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
			* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
			* U.S. Government End Users acquire the Licensed Deliverables with
			* only those rights set forth herein.
			*
			* Any use of the Licensed Deliverables in individual and commercial
			* software must include, in the user documentation and internal
			* comments to the code, the above Disclaimer and U.S. Government End
			* Users Notice.
	*/		*/
	#if !defined(__VECTOR_FUNCTIONS_H__)		#if !defined(__VECTOR_FUNCTIONS_H__)
	#define __VECTOR_FUNCTIONS_H__		#define __VECTOR_FUNCTIONS_H__
	/************************************************************************** *****		/************************************************************************** *****
	* *		* *
	* *		* *
	* *		* *
	*************************************************************************** ****/		*************************************************************************** ****/
End of changes. 6 change blocks.
	28 lines changed or deleted		41 lines changed or added

	vector_types.h		vector_types.h
	/*		/**
	* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.		* Copyright 2010 Duane Merrill
	*		*
	* NOTICE TO USER:		* Licensed under the Apache License, Version 2.0 (the "License");
			* you may not use this file except in compliance with the License.
			* You may obtain a copy of the License at
	*		*
	* This source code is subject to NVIDIA ownership rights under U.S. and		* http://www.apache.org/licenses/LICENSE-2.0
	* international Copyright laws. Users and possessors of this source code
	* are hereby granted a nonexclusive, royalty-free license to use this code
	* in individual and commercial software.
	*		*
	* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE		* Unless required by applicable law or agreed to in writing, software
	* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR		* distributed under the License is distributed on an "AS IS" BASIS,
	* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH		* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF		* See the License for the specific language governing permissions and
	* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.		* limitations under the License.
	* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL
	,
	* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
	* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
	* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
	* OR PERFORMANCE OF THIS SOURCE CODE.
	*		*
	* U.S. Government End Users. This source code is a "commercial item" as		* For more information, see our Google Code project site:
	* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of		* http://code.google.com/p/back40computing/
	* "commercial computer software" and "commercial computer software
	* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)
	* and is provided to the U.S. Government only as a commercial end item.
	* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through
	* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the
	* source code with only those rights set forth herein.
	*		*
	* Any use of this source code in individual and commercial software must		* Thanks!
	* include, in the user documentation and internal comments to the code,
	* the above Disclaimer and U.S. Government End Users Notice.
	*/		*/
	#if !defined(__VECTOR_TYPES_H__)		#pragma once
	#define __VECTOR_TYPES_H__
	/**************************************************************************
	*****
	*
	*
	*
	*
	*
	*
	***************************************************************************
	****/
	#include "builtin_types.h"
	#include "host_defines.h"
	/**************************************************************************
	*****
	*
	*
	*
	*
	*
	*
	***************************************************************************
	****/
	#if !defined(__CUDACC__) && !defined(__CUDABE__) && \
	defined(_WIN32) && !defined(_WIN64)
	#define __cuda_builtin_vector_align8(tag, members) \
	struct tag { \
	union { \
	struct { members }; \
	struct { long long int :1,:0; }; \
	}; \
	}
	#else /* !__CUDACC__ && !__CUDABE__ && _WIN32 && !_WIN64 */
	#define __cuda_builtin_vector_align8(tag, members) \
	struct __align__(8) tag { \
	members \
	}
	#endif /* !__CUDACC__ && !__CUDABE__ && _WIN32 && !_WIN64 */
	/*DEVICE_BUILTIN*/
	struct char1
	{
	signed char x;
	};
	/*DEVICE_BUILTIN*/
	struct uchar1
	{
	unsigned char x;
	};
	/*DEVICE_BUILTIN*/
	struct __align__(2) char2
	{
	signed char x, y;
	};
	/*DEVICE_BUILTIN*/
	struct __align__(2) uchar2
	{
	unsigned char x, y;
	};
	/*DEVICE_BUILTIN*/
	struct char3
	{
	signed char x, y, z;
	};
	/*DEVICE_BUILTIN*/
	struct uchar3
	{
	unsigned char x, y, z;
	};
	/*DEVICE_BUILTIN*/
	struct __align__(4) char4
	{
	signed char x, y, z, w;
	};
	/*DEVICE_BUILTIN*/
	struct __align__(4) uchar4
	{
	unsigned char x, y, z, w;
	};
	/*DEVICE_BUILTIN*/
	struct short1
	{
	short x;
	};
	/*DEVICE_BUILTIN*/
	struct ushort1
	{
	unsigned short x;
	};
	/*DEVICE_BUILTIN*/
	struct __align__(4) short2
	{
	short x, y;
	};
	/*DEVICE_BUILTIN*/
	struct __align__(4) ushort2
	{
	unsigned short x, y;
	};
	/*DEVICE_BUILTIN*/
	struct short3
	{
	short x, y, z;
	};
	/*DEVICE_BUILTIN*/
	struct ushort3
	{
	unsigned short x, y, z;
	};
	/*DEVICE_BUILTIN*/
	__cuda_builtin_vector_align8(short4, short x; short y; short z; short w;);
	/*DEVICE_BUILTIN*/
	__cuda_builtin_vector_align8(ushort4, unsigned short x; unsigned short y; u
	nsigned short z; unsigned short w;);
	/*DEVICE_BUILTIN*/
	struct int1
	{
	int x;
	};
	/*DEVICE_BUILTIN*/
	struct uint1
	{
	unsigned int x;
	};
	/*DEVICE_BUILTIN*/
	__cuda_builtin_vector_align8(int2, int x; int y;);
	/*DEVICE_BUILTIN*/
	__cuda_builtin_vector_align8(uint2, unsigned int x; unsigned int y;);
	/*DEVICE_BUILTIN*/
	struct int3
	{
	int x, y, z;
	};
	/*DEVICE_BUILTIN*/
	struct uint3
	{
	unsigned int x, y, z;
	};
	/*DEVICE_BUILTIN*/
	struct __builtin_align__(16) int4
	{
	int x, y, z, w;
	};
	/*DEVICE_BUILTIN*/
	struct __builtin_align__(16) uint4
	{
	unsigned int x, y, z, w;
	};
	/*DEVICE_BUILTIN*/
	struct long1
	{
	long int x;
	};
	/*DEVICE_BUILTIN*/
	struct ulong1
	{
	unsigned long x;
	};
	#if defined (_WIN32)
	/*DEVICE_BUILTIN*/
	__cuda_builtin_vector_align8(long2, long int x; long int y;);
	/*DEVICE_BUILTIN*/
	__cuda_builtin_vector_align8(ulong2, unsigned long int x; unsigned long int
	y;);
	#else /* _WIN32 */
	/*DEVICE_BUILTIN*/
	struct __align__(2*sizeof(long int)) long2
	{
	long int x, y;
	};
	/*DEVICE_BUILTIN*/
	struct __align__(2*sizeof(unsigned long int)) ulong2
	{
	unsigned long int x, y;
	};
	#endif /* _WIN32 */
	/*DEVICE_BUILTIN*/
	struct long3
	{
	long int x, y, z;
	};
	/*DEVICE_BUILTIN*/
	struct ulong3
	{
	unsigned long int x, y, z;
	};
	/*DEVICE_BUILTIN*/
	struct __builtin_align__(16) long4
	{
	long int x, y, z, w;
	};
	/*DEVICE_BUILTIN*/
	struct __builtin_align__(16) ulong4
	{
	unsigned long int x, y, z, w;
	};
	/*DEVICE_BUILTIN*/
	struct float1
	{
	float x;
	};
	/*DEVICE_BUILTIN*/
	__cuda_builtin_vector_align8(float2, float x; float y;);
	/*DEVICE_BUILTIN*/
	struct float3
	{
	float x, y, z;
	};
	/*DEVICE_BUILTIN*/
	struct __builtin_align__(16) float4
	{
	float x, y, z, w;
	};
	/*DEVICE_BUILTIN*/
	struct longlong1
	{
	long long int x;
	};
	/*DEVICE_BUILTIN*/
	struct ulonglong1
	{
	unsigned long long int x;
	};
	/*DEVICE_BUILTIN*/
	struct __builtin_align__(16) longlong2
	{
	long long int x, y;
	};
	/*DEVICE_BUILTIN*/
	struct __builtin_align__(16) ulonglong2
	{
	unsigned long long int x, y;
	};
	/*DEVICE_BUILTIN*/		#include <vector_types.h>
	struct longlong3
	{
	long long int x, y, z;
	};
	/*DEVICE_BUILTIN*/		namespace thrust {
	struct ulonglong3		namespace detail {
	{		namespace device {
	unsigned long long int x, y, z;		namespace cuda {
	};		namespace detail {
			namespace b40c_thrust {
	/*DEVICE_BUILTIN*/		//-------------------------------------------------------------------------
	struct __builtin_align__(16) longlong4		-----
	{		// Vector types
	long long int x, y, z ,w;		//-------------------------------------------------------------------------
	};		-----
	/*DEVICE_BUILTIN*/		template <typename K, int vec_elements> struct VecType;
	struct __builtin_align__(16) ulonglong4
	{
	unsigned long long int x, y, z, w;
	};
	/*DEVICE_BUILTIN*/		//
	struct double1		// Define general vector types
	{		//
	double x;
	};
	/*DEVICE_BUILTIN*/		template <typename K>
	struct __builtin_align__(16) double2		struct VecType<K, 1> {
	{		K x;
	double x, y;		typedef K Type;
	};		};
	/*DEVICE_BUILTIN*/		template <typename K>
	struct double3		struct VecType<K, 2> {
	{		K x;
	double x, y, z;		K y;
			typedef VecType<K, 2> Type;
	};		};
	/*DEVICE_BUILTIN*/		template <typename K>
	struct __builtin_align__(16) double4		struct VecType<K, 4> {
	{		K x;
	double x, y, z, w;		K y;
			K z;
			K w;
			typedef VecType<K, 4> Type;
	};		};
	/**************************************************************************		//
	*****		// Specialize certain built-in vector types
	*		//
	*
	*
	*
	*
	*
	***************************************************************************
	****/
	/*DEVICE_BUILTIN*/
	typedef struct char1 char1;
	/*DEVICE_BUILTIN*/
	typedef struct uchar1 uchar1;
	/*DEVICE_BUILTIN*/
	typedef struct char2 char2;
	/*DEVICE_BUILTIN*/
	typedef struct uchar2 uchar2;
	/*DEVICE_BUILTIN*/
	typedef struct char3 char3;
	/*DEVICE_BUILTIN*/
	typedef struct uchar3 uchar3;
	/*DEVICE_BUILTIN*/
	typedef struct char4 char4;
	/*DEVICE_BUILTIN*/
	typedef struct uchar4 uchar4;
	/*DEVICE_BUILTIN*/
	typedef struct short1 short1;
	/*DEVICE_BUILTIN*/
	typedef struct ushort1 ushort1;
	/*DEVICE_BUILTIN*/
	typedef struct short2 short2;
	/*DEVICE_BUILTIN*/
	typedef struct ushort2 ushort2;
	/*DEVICE_BUILTIN*/
	typedef struct short3 short3;
	/*DEVICE_BUILTIN*/
	typedef struct ushort3 ushort3;
	/*DEVICE_BUILTIN*/
	typedef struct short4 short4;
	/*DEVICE_BUILTIN*/
	typedef struct ushort4 ushort4;
	/*DEVICE_BUILTIN*/
	typedef struct int1 int1;
	/*DEVICE_BUILTIN*/
	typedef struct uint1 uint1;
	/*DEVICE_BUILTIN*/
	typedef struct int2 int2;
	/*DEVICE_BUILTIN*/
	typedef struct uint2 uint2;
	/*DEVICE_BUILTIN*/
	typedef struct int3 int3;
	/*DEVICE_BUILTIN*/
	typedef struct uint3 uint3;
	/*DEVICE_BUILTIN*/
	typedef struct int4 int4;
	/*DEVICE_BUILTIN*/
	typedef struct uint4 uint4;
	/*DEVICE_BUILTIN*/
	typedef struct long1 long1;
	/*DEVICE_BUILTIN*/
	typedef struct ulong1 ulong1;
	/*DEVICE_BUILTIN*/
	typedef struct long2 long2;
	/*DEVICE_BUILTIN*/
	typedef struct ulong2 ulong2;
	/*DEVICE_BUILTIN*/
	typedef struct long3 long3;
	/*DEVICE_BUILTIN*/
	typedef struct ulong3 ulong3;
	/*DEVICE_BUILTIN*/
	typedef struct long4 long4;
	/*DEVICE_BUILTIN*/
	typedef struct ulong4 ulong4;
	/*DEVICE_BUILTIN*/
	typedef struct float1 float1;
	/*DEVICE_BUILTIN*/
	typedef struct float2 float2;
	/*DEVICE_BUILTIN*/
	typedef struct float3 float3;
	/*DEVICE_BUILTIN*/
	typedef struct float4 float4;
	/*DEVICE_BUILTIN*/
	typedef struct longlong1 longlong1;
	/*DEVICE_BUILTIN*/
	typedef struct ulonglong1 ulonglong1;
	/*DEVICE_BUILTIN*/
	typedef struct longlong2 longlong2;
	/*DEVICE_BUILTIN*/
	typedef struct ulonglong2 ulonglong2;
	/*DEVICE_BUILTIN*/
	typedef struct longlong3 longlong3;
	/*DEVICE_BUILTIN*/
	typedef struct ulonglong3 ulonglong3;
	/*DEVICE_BUILTIN*/
	typedef struct longlong4 longlong4;
	/*DEVICE_BUILTIN*/
	typedef struct ulonglong4 ulonglong4;
	/*DEVICE_BUILTIN*/
	typedef struct double1 double1;
	/*DEVICE_BUILTIN*/
	typedef struct double2 double2;
	/*DEVICE_BUILTIN*/
	typedef struct double3 double3;
	/*DEVICE_BUILTIN*/
	typedef struct double4 double4;
	/**************************************************************************
	*****
	*
	*
	*
	*
	*
	*
	***************************************************************************
	****/
	/*DEVICE_BUILTIN*/		#define B40C_DEFINE_VECTOR_TYPE(base_type,short_type)
	struct dim3		\
	{		template<> struct VecType<base_type, 1> { typedef short_type##1 Type; };
	unsigned int x, y, z;		\
	#if defined(__cplusplus)		template<> struct VecType<base_type, 2> { typedef short_type##2 Type; };
	__host__ __device__ dim3(unsigned int vx = 1, unsigned int vy = 1, unsi		\
	gned int vz = 1) : x(vx), y(vy), z(vz) {}		template<> struct VecType<base_type, 4> { typedef short_type##4 Type; };
	__host__ __device__ dim3(uint3 v) : x(v.x), y(v.y), z(v.z) {}
	__host__ __device__ operator uint3(void) { uint3 t; t.x = x; t.y = y; t
	.z = z; return t; }
	#endif /* __cplusplus */
	};
	/*DEVICE_BUILTIN*/		B40C_DEFINE_VECTOR_TYPE(char, char)
	typedef struct dim3 dim3;		B40C_DEFINE_VECTOR_TYPE(short, short)
			B40C_DEFINE_VECTOR_TYPE(int, int)
			B40C_DEFINE_VECTOR_TYPE(long, long)
			B40C_DEFINE_VECTOR_TYPE(long long, longlong)
			B40C_DEFINE_VECTOR_TYPE(unsigned char, uchar)
			B40C_DEFINE_VECTOR_TYPE(unsigned short, ushort)
			B40C_DEFINE_VECTOR_TYPE(unsigned int, uint)
			B40C_DEFINE_VECTOR_TYPE(unsigned long, ulong)
			B40C_DEFINE_VECTOR_TYPE(unsigned long long, ulonglong)
			B40C_DEFINE_VECTOR_TYPE(float, float)
			B40C_DEFINE_VECTOR_TYPE(double, double)
	#undef __cuda_builtin_vector_align8		#undef B40C_DEFINE_VECTOR_TYPE
	#endif /* !__VECTOR_TYPES_H__ */		} // end namespace b40c_thrust
			} // end namespace detail
			} // end namespace cuda
			} // end namespace device
			} // end namespace detail
			} // end namespace thrust
End of changes. 20 change blocks.
	485 lines changed or deleted		70 lines changed or added

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