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218 lines
6.7 KiB
Markdown
218 lines
6.7 KiB
Markdown
clBLAS
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=====
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[![Build Status](https://travis-ci.org/clMathLibraries/clBLAS.png)](https://travis-ci.org/clMathLibraries/clBLAS)
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This repository houses the code for the OpenCL™ BLAS portion of clMath.
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The complete set of BLAS level 1, 2 & 3 routines is implemented. Please
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see Netlib BLAS for the list of supported routines. In addition to GPU
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devices, the library also supports running on CPU devices to facilitate
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debugging and multicore programming. APPML 1.10 is the most current
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generally available pre-packaged binary version of the library available
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for download for both Linux and Windows platforms.
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The primary goal of clBLAS is to make it easier for developers to
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utilize the inherent performance and power efficiency benefits of
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heterogeneous computing. clBLAS interfaces do not hide nor wrap OpenCL
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interfaces, but rather leaves OpenCL state management to the control of
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the user to allow for maximum performance and flexibility. The clBLAS
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library does generate and enqueue optimized OpenCL kernels, relieving
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the user from the task of writing, optimizing and maintaining kernel
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code themselves.
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## clBLAS library user documentation
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[Library and API documentation][] for developers is available online as
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a GitHub Pages website
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### Google Groups
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Two mailing lists have been created for the clMath projects:
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- [clmath@googlegroups.com][] - group whose focus is to answer
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questions on using the library or reporting issues
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- [clmath-developers@googlegroups.com][] - group whose focus is for
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developers interested in contributing to the library code itself
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## clBLAS Wiki
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The [project wiki][] contains helpful documentation, including a [build
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primer][]
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## Contributing code
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Please refer to and read the [Contributing][] document for guidelines on
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how to contribute code to this open source project. The code in the
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/master branch is considered to be stable, and all pull-requests should
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be made against the /develop branch.
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## License
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The source for clBLAS is licensed under the [Apache License, Version
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2.0][]
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## Example
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The simple example below shows how to use clBLAS to compute an OpenCL
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accelerated SGEMM
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#include <sys/types.h>
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#include <stdio.h>
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/* Include the clBLAS header. It includes the appropriate OpenCL headers
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*/
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#include <clBLAS.h>
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/* This example uses predefined matrices and their characteristics for
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* simplicity purpose.
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*/
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#define M 4
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#define N 3
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#define K 5
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static const cl_float alpha = 10;
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static const cl_float A[M*K] = {
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11, 12, 13, 14, 15,
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21, 22, 23, 24, 25,
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31, 32, 33, 34, 35,
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41, 42, 43, 44, 45,
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};
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static const size_t lda = K; /* i.e. lda = K */
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static const cl_float B[K*N] = {
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11, 12, 13,
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21, 22, 23,
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31, 32, 33,
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41, 42, 43,
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51, 52, 53,
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};
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static const size_t ldb = N; /* i.e. ldb = N */
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static const cl_float beta = 20;
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static cl_float C[M*N] = {
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11, 12, 13,
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21, 22, 23,
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31, 32, 33,
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41, 42, 43,
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};
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static const size_t ldc = N; /* i.e. ldc = N */
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static cl_float result[M*N];
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int main( void )
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{
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cl_int err;
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cl_platform_id platform = 0;
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cl_device_id device = 0;
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cl_context_properties props[3] = { CL_CONTEXT_PLATFORM, 0, 0 };
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cl_context ctx = 0;
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cl_command_queue queue = 0;
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cl_mem bufA, bufB, bufC;
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cl_event event = NULL;
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int ret = 0;
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/* Setup OpenCL environment. */
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err = clGetPlatformIDs( 1, &platform, NULL );
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err = clGetDeviceIDs( platform, CL_DEVICE_TYPE_GPU, 1, &device, NULL );
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props[1] = (cl_context_properties)platform;
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ctx = clCreateContext( props, 1, &device, NULL, NULL, &err );
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queue = clCreateCommandQueue( ctx, device, 0, &err );
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/* Setup clBLAS */
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err = clblasSetup( );
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/* Prepare OpenCL memory objects and place matrices inside them. */
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bufA = clCreateBuffer( ctx, CL_MEM_READ_ONLY, M * K * sizeof(*A),
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NULL, &err );
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bufB = clCreateBuffer( ctx, CL_MEM_READ_ONLY, K * N * sizeof(*B),
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NULL, &err );
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bufC = clCreateBuffer( ctx, CL_MEM_READ_WRITE, M * N * sizeof(*C),
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NULL, &err );
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err = clEnqueueWriteBuffer( queue, bufA, CL_TRUE, 0,
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M * K * sizeof( *A ), A, 0, NULL, NULL );
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err = clEnqueueWriteBuffer( queue, bufB, CL_TRUE, 0,
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K * N * sizeof( *B ), B, 0, NULL, NULL );
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err = clEnqueueWriteBuffer( queue, bufC, CL_TRUE, 0,
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M * N * sizeof( *C ), C, 0, NULL, NULL );
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/* Call clBLAS extended function. Perform gemm for the lower right sub-matrices */
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err = clblasSgemm( clblasRowMajor, clblasNoTrans, clblasNoTrans,
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M, N, K,
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alpha, bufA, 0, lda,
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bufB, 0, ldb, beta,
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bufC, 0, ldc,
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1, &queue, 0, NULL, &event );
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/* Wait for calculations to be finished. */
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err = clWaitForEvents( 1, &event );
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/* Fetch results of calculations from GPU memory. */
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err = clEnqueueReadBuffer( queue, bufC, CL_TRUE, 0,
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M * N * sizeof(*result),
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result, 0, NULL, NULL );
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/* Release OpenCL memory objects. */
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clReleaseMemObject( bufC );
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clReleaseMemObject( bufB );
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clReleaseMemObject( bufA );
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/* Finalize work with clBLAS */
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clblasTeardown( );
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/* Release OpenCL working objects. */
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clReleaseCommandQueue( queue );
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clReleaseContext( ctx );
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return ret;
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}
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## Build dependencies
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### Library for Windows
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- Windows® 7/8
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- Visual Studio 2010 SP1, 2012
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- An OpenCL SDK, such as APP SDK 2.9
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- Latest CMake
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### Library for Linux
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- GCC 4.6 and onwards
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- An OpenCL SDK, such as APP SDK 2.9
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- Latest CMake
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### Library for Mac OSX
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- Recommended to generate Unix makefiles with cmake
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### Test infrastructure
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- Googletest v1.6
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- ACML on windows/linux; Accelerate on Mac OSX
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- Latest Boost
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### Performance infrastructure
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- Python
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[Library and API documentation]: http://clmathlibraries.github.io/clBLAS/
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[clmath@googlegroups.com]: https://groups.google.com/forum/#!forum/clmath
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[clmath-developers@googlegroups.com]: https://groups.google.com/forum/#!forum/clmath-developers
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[project wiki]: https://github.com/clMathLibraries/clBLAS/wiki
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[build primer]: https://github.com/clMathLibraries/clBLAS/wiki/Build
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[Contributing]: CONTRIBUTING.md
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[Apache License, Version 2.0]: http://www.apache.org/licenses/LICENSE-2.0
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