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155 lines
8.7 KiB
Markdown
155 lines
8.7 KiB
Markdown
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CLBlast: The tuned OpenCL BLAS library
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================
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| | Build status | Tests on Intel CPU | Tests on NVIDIA GPU | Tests on AMD GPU |
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| Windows | [![Build Status](https://ci.appveyor.com/api/projects/status/github/cnugteren/clblast?branch=master&svg=true)](https://ci.appveyor.com/project/CNugteren/clblast) | N/A | N/A | N/A |
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| Linux | [![Build Status](https://travis-ci.org/CNugteren/CLBlast.svg?branch=master)](https://travis-ci.org/CNugteren/CLBlast/branches) | [![Build Status](http://67.207.87.39:8010/badges/clblast-linux-intel-e5-2620-v4.svg)](http://67.207.87.39:8010/#/builders/97) | [![Build Status](http://67.207.87.39:8010/badges/clblast-linux-nvidia-k80.svg)](http://67.207.87.39:8010/#/builders/98) | [![Build Status](http://67.207.87.39:8010/badges/clblast-linux-amd-w9100.svg)](http://67.207.87.39:8010/#/builders/96) |
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| OS X | [![Build Status](https://travis-ci.org/CNugteren/CLBlast.svg?branch=master)](https://travis-ci.org/CNugteren/CLBlast/branches) | N/A | N/A | N/A |
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CLBlast is a modern, lightweight, performant and tunable OpenCL BLAS library written in C++11. It is designed to leverage the full performance potential of a wide variety of OpenCL devices from different vendors, including desktop and laptop GPUs, embedded GPUs, and other accelerators. CLBlast implements BLAS routines: basic linear algebra subprograms operating on vectors and matrices. See [the CLBlast website](https://cnugteren.github.io/clblast) for performance reports on various devices as well as the latest CLBlast news.
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The library is not tuned for all possible OpenCL devices: __if out-of-the-box performance is poor, please run the tuners first__. See below for a list of already tuned devices and instructions on how to tune yourself and contribute to future releases of the CLBlast library. See also the [CLBlast feature roadmap](ROADMAP.md) to get an indication of the future of CLBlast.
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Why CLBlast and not clBLAS or cuBLAS?
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-------------
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Use CLBlast instead of clBLAS:
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* When you care about achieving maximum performance.
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* When you want to be able to inspect the BLAS kernels or easily customize them to your needs.
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* When you run on exotic OpenCL devices for which you need to tune yourself.
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* When you are still running on OpenCL 1.1 hardware.
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* When you prefer a C++ API over a C API (C API also available in CLBlast).
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* When you value an organized and modern C++ codebase.
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* When you target Intel CPUs and GPUs or embedded devices.
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* When you can benefit from the increased performance of half-precision fp16 data-types.
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Use CLBlast instead of cuBLAS:
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* When you want your code to run on devices other than NVIDIA CUDA-enabled GPUs.
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* When you want to tune for a specific configuration (e.g. rectangular matrix-sizes).
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* When you sleep better if you know that the library you use is open-source.
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* When you are using OpenCL rather than CUDA.
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When not to use CLBlast:
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* When you run on NVIDIA's CUDA-enabled GPUs only and can benefit from cuBLAS's assembly-level tuned kernels.
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Getting started
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-------------
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CLBlast can be compiled with minimal dependencies (apart from OpenCL) in the usual CMake-way, e.g.:
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mkdir build && cd build
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cmake ..
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make
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Detailed instructions for various platforms can be found are [here](doc/installation.md).
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Like clBLAS and cuBLAS, CLBlast also requires OpenCL device buffers as arguments to its routines. This means you'll have full control over the OpenCL buffers and the host-device memory transfers. CLBlast's API is designed to resemble clBLAS's C API as much as possible, requiring little integration effort in case clBLAS was previously used. Using CLBlast starts by including the C++ header:
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#include <clblast.h>
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Or alternatively the plain C version:
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#include <clblast_c.h>
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Afterwards, any of CLBlast's routines can be called directly: there is no need to initialize the library. The available routines and the required arguments are described in the above mentioned include files and the included [API documentation](doc/api.md). The API is kept as close as possible to the Netlib BLAS and the cuBLAS/clBLAS APIs. For an overview of the supported routines, see [here](doc/routines.md).
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To get started quickly, a couple of stand-alone example programs are included in the `samples` subfolder. They can optionally be compiled using the CMake infrastructure of CLBlast by providing the `-DSAMPLES=ON` flag, for example as follows:
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cmake -DSAMPLES=ON ..
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Afterwards, you can optionally read more about running proper [benchmarks](doc/benchmarking.md) and [tuning the library](doc/tuning.md).
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Full documentation
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-------------
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More detailed documentation is available in separate files:
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* [Building and installing](doc/installation.md)
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* [Supported routines overview](doc/routines.md)
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* [Performance measuring and benchmarking](doc/benchmarking.md)
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* [Tuning for better performance](doc/tuning.md)
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* [Testing the library for correctness](doc/testing.md)
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* [Bindings / wrappers for other languages](doc/bindings.md)
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Known issues
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-------------
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Known performance related issues:
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* Severe performance issues with Beignet v1.3.0 due to missing support for local memory. Please downgrade to v1.2.1 or upgrade to v1.3.1 or newer.
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* Performance issues on Qualcomm Adreno GPUs.
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Other known issues:
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* Routines returning an integer are currently not properly tested for half-precision FP16: IHAMAX/IHAMIN/IHMAX/IHMIN
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* Half-precision FP16 tests might sometimes fail based on order multiplication, i.e. (a * b) * c != (c * b) * a
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* The AMD APP SDK has a bug causing a conflict with libstdc++, resulting in a segfault when initialising static variables. This has been reported to occur with the CLBlast tuners.
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* The AMD run-time compiler has a bug causing it to get stuck in an infinite loop. This is reported to happen occasionally when tuning the CLBlast GEMM routine.
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Contributing
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-------------
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Contributions are welcome in the form of tuning results for OpenCL devices previously untested or pull requests. See [the contributing guidelines](CONTRIBUTING.md) for more details.
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The main contributing authors (code, pull requests, testing) are:
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* [Cedric Nugteren](http://cnugteren.github.io) - main author
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* [Anton Lokhmotov](https://github.com/psyhtest)
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* [Dragan Djuric](https://github.com/blueberry)
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* [Marco Hutter](http://marco-hutter.de/)
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* [Hugh Perkins](https://github.com/hughperkins)
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* [Gian-Carlo Pascutto](https://github.com/gcp)
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* [Ivan Shapovalov](https://github.com/intelfx)
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* [Dimitri Van Assche](https://github.com/dvasschemacq)
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* [Shehzan Mohammed](https://shehzan10.github.io)
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* [Marco Cianfriglia](https://github.com/mcian)
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* Everyone else listed as a [GitHub contributor](https://github.com/CNugteren/CLBlast/graphs/contributors)
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Tuning and testing on a variety of OpenCL devices was made possible by:
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* [TU/e ES research group](http://www.es.ele.tue.nl/)
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* [ASCI DAS4 and DAS5](http://www.cs.vu.nl/das4/)
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* [dividiti](http://www.dividiti.com)
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* [SURFsara HPC center](http://www.surfsara.com)
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* [ArrayFire](http://arrayfire.org)
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* Everyone reporting [tuning results](https://github.com/CNugteren/CLBlast/issues/1)
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Hardware/software for this project was contributed by:
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* [ArrayFire](http://arrayfire.org) for settings up and supporting Jenkins CI correctness tests on 7 platforms
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* [JetBrains](https://www.jetbrains.com/clion/) for supply a free CLion IDE license for CLBlast developers
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* [Travis CI](https://travis-ci.org/CNugteren/CLBlast/branches) and [AppVeyor](https://ci.appveyor.com/project/CNugteren/clblast) for free automated build tests for open-source projects
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More information
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-------------
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Further information on CLBlast is available through the following links:
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* A 20-minute presentation of CLBlast was given at the GPU Technology Conference in May 2017. A recording is available on the [GTC on-demand website](http://on-demand.gputechconf.com/gtc/2017/video/s7280-nugteren-clblast.mp4) (poor audio quality however) and a full slide-set is also available [as PDF](http://on-demand.gputechconf.com/gtc/2017/presentation/s7280-cedric-nugteren-clblast.pdf).
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* More in-depth information and experimental results are also available in a scientific paper titled [CLBlast: A Tuned OpenCL BLAS Library](https://arxiv.org/abs/1705.05249) (May 2017). For CLTune, the inspiration for the included auto-tuner, see also the [CLTune: A Generic Auto-Tuner for OpenCL Kernels](https://arxiv.org/abs/1703.06503) paper.
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How to cite this work:
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C. Nugteren. CLBlast: A Tuned OpenCL BLAS Library. ArXiv pre-print 1705.05249, 2017.
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Support us
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This project started in March 2015 as an evenings and weekends free-time project next to a full-time job for Cedric Nugteren. If you are in the position to support the project by OpenCL-hardware donations or otherwise, please find contact information on the [website of the main author](http://cnugteren.github.io).
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