| Build branch | master | develop |
|---|---|---|
| GCC/Clang x64 |  |
 |
| Visual Studio x64 |
This repository houses the code for the OpenCL™ 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.
- Introducing AutoGemm
- clBLAS's Gemm implementation has been comprehensively overhauled to use AutoGemm. AutoGemm is a suite of python scripts which generate optimized kernels and kernel selection logic, for all precisions, transposes, tile sizes and so on.
- CMake is configured to use AutoGemm for clBLAS so the build and usage experience of Gemm remains unchanged (only performance and maintainability has been improved). Kernel sources are generated at build time (not runtime) and can be configured within CMake to be pre-compiled at build time.
- clBLAS users with unique Gemm requirements can customize AutoGemm to their needs (such as non-default tile sizes for very small or very skinny matrices); see AutoGemm documentation for details.
Library and API documentation for developers is available online as a GitHub Pages website
Two mailing lists have been created for the clMath projects:
-
[email protected] - group whose focus is to answer questions on using the library or reporting issues
-
[email protected] - group whose focus is for developers interested in contributing to the library code itself
The project wiki contains helpful documentation, including a build primer
Please refer to and read the Contributing document for guidelines on how to contribute code to this open source project. The code in the /master branch is considered to be stable, and all pull-requests should be made against the /develop branch.
The source for clBLAS is licensed under the Apache License, Version 2.0
The simple example below shows how to use clBLAS to compute an OpenCL accelerated SGEMM
#include <sys/types.h>
#include <stdio.h>
/* Include the clBLAS header. It includes the appropriate OpenCL headers */
#include <clBLAS.h>
/* 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;
}- Windows® 7/8
- Visual Studio 2010 SP1, 2012
- An OpenCL SDK, such as APP SDK 2.8
- Latest CMake
- GCC 4.6 and onwards
- An OpenCL SDK, such as APP SDK 2.9
- Latest CMake
- Recommended to generate Unix makefiles with cmake
- Googletest v1.6
- ACML on windows/linux; Accelerate on Mac OSX
- Latest Boost
- Python