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https://github.com/clMathLibraries/clBLAS
a software library containing BLAS functions written in OpenCL
https://github.com/clMathLibraries/clBLAS
Last synced: about 1 month ago
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a software library containing BLAS functions written in OpenCL
- Host: GitHub
- URL: https://github.com/clMathLibraries/clBLAS
- Owner: clMathLibraries
- License: apache-2.0
- Created: 2013-08-13T15:05:53.000Z (almost 11 years ago)
- Default Branch: master
- Last Pushed: 2023-11-16T15:41:06.000Z (7 months ago)
- Last Synced: 2024-04-28T07:16:03.053Z (about 2 months ago)
- Language: C++
- Size: 2.78 MB
- Stars: 830
- Watchers: 92
- Forks: 237
- Open Issues: 94
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG
- Contributing: CONTRIBUTING.md
- License: LICENSE
Lists
- CPLD-Guide - clBLAS
- VHDL-Guide - clBLAS
- DSP-Guide - clBLAS
- Parallel-Computing-Guide - clBLAS
- awesome-c-zh - clBLAS - 用OpenCL编写的bla函数。[](https://spdx.org/licenses/Apache-2.0.html) (数值 / 高级书籍)
- awesome-android-cpp - clMathLibraries/clBLAS - a software library containing BLAS functions written in OpenCL (TODO scan for Android support in followings)
README
## Build Status
| Build branch | master | develop |
|-----|-----|-----|
| GCC/Clang x64 | [](https://travis-ci.org/clMathLibraries/clBLAS/branches) | [](https://travis-ci.org/clMathLibraries/clBLAS/branches) |
| Visual Studio x64 | [](https://ci.appveyor.com/project/kknox/clblas-5ph9i/branch/master)|[](https://ci.appveyor.com/project/kknox/clblas-5ph9i/branch/develop) |
clBLAS
=====
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 1.12 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 update notes 01/2017
- v2.12 is a bugfix release as a rollup of all fixes in /develop branch
- Thanks to @pavanky, @iotamudelta, @shahsan10, @psyhtest, @haahh, @hughperkins, @tfauck
@abhiShandy, @IvanVergiliev, @zougloub, @mgates3 for contributions to clBLAS v2.12
- Summary of fixes available to read on the releases tab
## clBLAS library user documentation
[Library and API documentation][] for developers is available online as
a GitHub Pages website
## Google Groups
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
## clBLAS Wiki
The [project wiki][] contains helpful documentation, including a [build
primer][]
## Contributing code
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.
## 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 2.9
* Latest CMake
### Library for Mac OSX
* Recommended to generate Unix makefiles with cmake
### Test infrastructure
* Googletest v1.6
* Latest Boost
* CPU BLAS
- Netlib CBLAS (recommended)
Ubuntu: install by "apt-get install libblas-dev"
Windows: download & install lapack-3.6.0 which comes with CBLAS
- or ACML on windows/linux; Accelerate on Mac OSX
### Performance infrastructure
* Python
[Library and API documentation]: http://clmathlibraries.github.io/clBLAS/
[[email protected]]: https://groups.google.com/forum/#!forum/clmath
[[email protected]]: https://groups.google.com/forum/#!forum/clmath-developers
[project wiki]: https://github.com/clMathLibraries/clBLAS/wiki
[build primer]: https://github.com/clMathLibraries/clBLAS/wiki/Build
[Contributing]: CONTRIBUTING.md
[Apache License, Version 2.0]: http://www.apache.org/licenses/LICENSE-2.0