cblas
所属分类：Windows编程
开发工具：C/C++
文件大小：1408KB
下载次数：8
上传日期：2014-03-14 22:31:25
上 传 者：
walker3000
说明： opencl编写的blas，和Cuda的cublas类似。
(This repository houses the code for the OpenCL™ BLAS portion of clMath. The complete set of BLAS level 1, 2 & 3 routines is implemented. )
文件列表:
clBLAS-develop (0, 2014-03-13)
clBLAS-develop\.travis.yml (1057, 2014-03-13)
clBLAS-develop\CHANGELOG (10292, 2014-03-13)
clBLAS-develop\CONTRIBUTING.md (6290, 2014-03-13)
clBLAS-develop\LICENSE (11358, 2014-03-13)
clBLAS-develop\NOTICE (163, 2014-03-13)
clBLAS-develop\doc (0, 2014-03-13)
clBLAS-develop\doc\clBLAS.doxy (82515, 2014-03-13)
clBLAS-develop\src (0, 2014-03-13)
clBLAS-develop\src\CMakeLists.txt (11710, 2014-03-13)
clBLAS-develop\src\FindNetlib.cmake (4033, 2014-03-13)
clBLAS-develop\src\FindOpenCL.cmake (3270, 2014-03-13)
clBLAS-develop\src\clAmdBlas.h (486131, 2014-03-13)
clBLAS-develop\src\clAmdBlas.version.h (906, 2014-03-13)
clBLAS-develop\src\clBLAS-complex.h (1348, 2014-03-13)
clBLAS-develop\src\clBLAS.def (3078, 2014-03-13)
clBLAS-develop\src\clBLAS.h (375950, 2014-03-13)
clBLAS-develop\src\clBLAS.version.h.in (960, 2014-03-13)
clBLAS-develop\src\client (0, 2014-03-13)
clBLAS-develop\src\client\CMakeLists.txt (2297, 2014-03-13)
clBLAS-develop\src\client\clGemm.h (19513, 2014-03-13)
clBLAS-develop\src\client\clfunc_common.hpp (10570, 2014-03-13)
clBLAS-develop\src\client\clfunc_xgemm.hpp (32802, 2014-03-13)
clBLAS-develop\src\client\clfunc_xgemv.hpp (11150, 2014-03-13)
clBLAS-develop\src\client\clfunc_xger.hpp (10496, 2014-03-13)
clBLAS-develop\src\client\clfunc_xgerc.hpp (9057, 2014-03-13)
clBLAS-develop\src\client\clfunc_xgeru.hpp (6979, 2014-03-13)
clBLAS-develop\src\client\clfunc_xhemm.hpp (16930, 2014-03-13)
clBLAS-develop\src\client\clfunc_xhemv.hpp (7587, 2014-03-13)
clBLAS-develop\src\client\clfunc_xher.hpp (8220, 2014-03-13)
clBLAS-develop\src\client\clfunc_xher2.hpp (9432, 2014-03-13)
clBLAS-develop\src\client\clfunc_xher2k.hpp (20168, 2014-03-13)
clBLAS-develop\src\client\clfunc_xherk.hpp (15761, 2014-03-13)
clBLAS-develop\src\client\clfunc_xsymm.hpp (19723, 2014-03-13)
clBLAS-develop\src\client\clfunc_xsymv.hpp (7535, 2014-03-13)
clBLAS-develop\src\client\clfunc_xsyr.hpp (6241, 2014-03-13)
clBLAS-develop\src\client\clfunc_xsyr2.hpp (6998, 2014-03-13)
... ...
clBLAS
=====
[](https://travis-ci.org/clMathLibraries/clBLAS)
This repository houses the code for the OpenCLTM BLAS portion of clMath. The complete set of BLAS level 1, 2 & 3 routines is implemented. Please see
Netlib BLAS for the list of supported routines. In addition to GPU devices, the library also supports running on CPU devices to facilitate debugging and multicore programming.
APPML 1.10 is the most current generally available pre-packaged binary version of the library available for download for both Linux and Windows platforms.
The primary goal of clBLAS is to make it easier for developers to utilize the inherent performance and power efficiency benefits of heterogeneous computing. clBLAS interfaces do not hide nor wrap OpenCL interfaces, but rather leaves OpenCL state management to the control of the user to allow for maximum performance and flexibility. The clBLAS library does generate and enqueue optimized OpenCL kernels, relieving the user from the task of writing, optimizing and maintaining kernel code themselves.
## clBLAS library user documentation
[Library and API documentation]( http://clmathlibraries.github.io/clBLAS/ ) for developers is available online as a GitHub Pages website
## clBLAS Wiki
The [project wiki](https://github.com/clMathLibraries/clBLAS/wiki) contains helpful documentation, including a [build primer](https://github.com/clMathLibraries/clBLAS/wiki/Build)
## Contributing code
Please refer to and read the [Contributing](CONTRIBUTING.md) document for guidelines on how to contribute code to this open source project
## License
The source for clBLAS is licensed under the [Apache License, Version 2.0]( http://www.apache.org/licenses/LICENSE-2.0 )
## Example
The simple example below shows how to use clBLAS to compute an OpenCL accelerated SGEMM
```c
#include
#include
/* Include the clBLAS header. It includes the appropriate OpenCL headers
*/
#include
/* This example uses predefined matrices and their characteristics for
* simplicity purpose.
*/
#define M 4
#define N 3
#define K 5
static const cl_float alpha = 10;
static const cl_float A[M*K] = {
11, 12, 13, 14, 15,
21, 22, 23, 24, 25,
31, 32, 33, 34, 35,
41, 42, 43, 44, 45,
};
static const size_t lda = K; /* i.e. lda = K */
static const cl_float B[K*N] = {
11, 12, 13,
21, 22, 23,
31, 32, 33,
41, 42, 43,
51, 52, 53,
};
static const size_t ldb = N; /* i.e. ldb = N */
static const cl_float beta = 20;
static cl_float C[M*N] = {
11, 12, 13,
21, 22, 23,
31, 32, 33,
41, 42, 43,
};
static const size_t ldc = N; /* i.e. ldc = N */
static cl_float result[M*N];
int main( void )
{
cl_int err;
cl_platform_id platform = 0;
cl_device_id device = 0;
cl_context_properties props[3] = { CL_CONTEXT_PLATFORM, 0, 0 };
cl_context ctx = 0;
cl_command_queue queue = 0;
cl_mem bufA, bufB, bufC;
cl_event event = NULL;
int ret = 0;
/* Setup OpenCL environment. */
err = clGetPlatformIDs( 1, &platform, NULL );
err = clGetDeviceIDs( platform, CL_DEVICE_TYPE_GPU, 1, &device, NULL );
props[1] = (cl_context_properties)platform;
ctx = clCreateContext( props, 1, &device, NULL, NULL, &err );
queue = clCreateCommandQueue( ctx, device, 0, &err );
/* Setup clBLAS */
err = clblasSetup( );
/* Prepare OpenCL memory objects and place matrices inside them. */
bufA = clCreateBuffer( ctx, CL_MEM_READ_ONLY, M * K * sizeof(*A),
NULL, &err );
bufB = clCreateBuffer( ctx, CL_MEM_READ_ONLY, K * N * sizeof(*B),
NULL, &err );
bufC = clCreateBuffer( ctx, CL_MEM_READ_WRITE, M * N * sizeof(*C),
NULL, &err );
err = clEnqueueWriteBuffer( queue, bufA, CL_TRUE, 0,
M * K * sizeof( *A ), A, 0, NULL, NULL );
err = clEnqueueWriteBuffer( queue, bufB, CL_TRUE, 0,
K * N * sizeof( *B ), B, 0, NULL, NULL );
err = clEnqueueWriteBuffer( queue, bufC, CL_TRUE, 0,
M * N * sizeof( *C ), C, 0, NULL, NULL );
/* Call clBLAS extended function. Perform gemm for the lower right sub-matrices */
err = clblasSgemm( clblasRowMajor, clblasNoTrans, clblasNoTrans,
M, N, K,
alpha, bufA, 0, lda,
bufB, 0, ldb, beta,
bufC, 0, ldc,
1, &queue, 0, NULL, &event );
/* Wait for calculations to be finished. */
err = clWaitForEvents( 1, &event );
/* Fetch results of calculations from GPU memory. */
err = clEnqueueReadBuffer( queue, bufC, CL_TRUE, 0,
M * N * sizeof(*result),
result, 0, NULL, NULL );
/* Release OpenCL memory objects. */
clReleaseMemObject( bufC );
clReleaseMemObject( bufB );
clReleaseMemObject( bufA );
/* Finalize work with clBLAS */
clblasTeardown( );
/* Release OpenCL working objects. */
clReleaseCommandQueue( queue );
clReleaseContext( ctx );
return ret;
}
```
## Build dependencies
### Library for Windows
* Windows 7/8
* Visual Studio 2010 SP1, 2012
* An OpenCL SDK, such as APP SDK 2.8
* Latest CMake
### Library for Linux
* GCC 4.6 and onwards
* An OpenCL SDK, such as APP SDK 2.8
* Latest CMake
### Test infrastructure
* Latest Googletest
* Latest ACML
* Latest Boost
### Performance infrastructure
* Python
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