Leverage the Speed of OpenCL™ with AMD Math Libraries
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The document provides an overview of various heterogeneous math libraries, including clmathlibraries, clblas, clfft, acml, clmagma, and bolt, highlighting their functionalities and applications within OpenCL. It details the APIs, usage instructions, and community contributions such as Python and Julia wrappers for these libraries. Additionally, it discusses performance optimizations and the integration of these libraries into larger systems for improved computational efficiency.
Leverage the Speed of OpenCL™ with AMD Math Libraries
- 1. HETEROGENEOUS MATH LIBRARIES KENT KNOX 12/16/2014
- 2. 2 | HETEROGENEOUS MATH LIBRARIES | DECEMBER 16, 2014 AGENDA clMATH ‒clBLAS ‒clFFT ACML clMAGMA Bolt LIBRARIES COVERED A survey of available libraries
- 3. 3 | HETEROGENEOUS MATH LIBRARIES | DECEMBER 16, 2014 CLMATHLIBRARIES clMathLibraries is a github organization for OpenCL™ math related subprojects https://github.com/clMathLibraries Currently hosting two subprojects: clBLAS & clFFT
- 5. 5 | HETEROGENEOUS MATH LIBRARIES | DECEMBER 16, 2014 CLBLAS - HTTPS://GITHUB.COM/CLMATHLIBRARIES/CLBLAS clBLAS implements the NetLib BLAS functionality with OpenCL ‒ Level 3 – Matrix x Matrix operations, O( N^3 ), compute bound ‒ Level 2 – Matrix x Vector operations, O( N^2 ), mostly memory bound ‒ Level 1 – Vector x Vector operations, O( N ), memory bound The API is in the same style as NetLib, but appends OpenCL structures ‒ clblasStatus clblasSgemm( clblasOrder order, clblasTranspose transA, clblasTranspose transB, size_t M, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, cl_float beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events ) clBLAS assumes that the user is comfortable with OpenCL programming ‒ The host code is responsible for detecting /choosing devices, transferring memory and synchronizing operations API
- 6. 6 | HETEROGENEOUS MATH LIBRARIES | DECEMBER 16, 2014 CLBLAS - HTTPS://GITHUB.COM/CLMATHLIBRARIES/CLBLAS A proof of concept Python wrapper for clBLAS started, but only sgemm wrapped ‒https://github.com/clMathLibraries/clBLAS/tree/master/src/wrappers/python ‒Based on Cython ‒Works with PyOpenCL to manage OpenCL state ‒Would love help from the community to finish this The community wrote a Julia wrapper for clBLAS ‒https://github.com/JuliaGPU/CLBLAS.jl API
- 7. 7 | HETEROGENEOUS MATH LIBRARIES | DECEMBER 16, 2014 CLBLAS - HTTPS://GITHUB.COM/CLMATHLIBRARIES/CLBLAS • The user is responsible for running the tool on their machine as a preprocessing step • The tool creates a kernel database file (.kdb) that contains the best performing kernel for a given BLAS routine • The .kdb file is specific to an OpenCL device; will be named after that device; e.g. tahiti.kdb • Example • export CLBLAS_STORAGE_PATH = /usr/local/lib • ./tune --gemm --double clBLAS contains a Tune tool for finding better OpenCL kernels
- 9. 9 | HETEROGENEOUS MATH LIBRARIES | DECEMBER 16, 2014 CLFFT - HTTPS://GITHUB.COM/CLMATHLIBRARIES/CLFFT clFFT implements an FFTW inspired interface with OpenCL ‒ Provides a fast and accurate platform for calculating discrete FFTs ‒ Supports 1D, 2D, and 3D transforms with a batch size that can be greater than 1 ‒ Supports dimension lengths that can be any mix of powers of 2, 3, and 5 ‒ Supports single and double precision floating point formats clFFT assumes that the user is comfortable with OpenCL programming ‒ The host code is responsible for detecting/choosing devices, transferring memory and synchronizing operations The community wrote a Python wrapper for clFFT ‒https://github.com/geggo/gpyfft The community wrote a Julia wrapper for clFFT ‒https://github.com/JuliaGPU/CLFFT.jl API
- 10. 10 | HETEROGENEOUS MATH LIBRARIES | DECEMBER 16, 2014 CLFFT - HTTPS://GITHUB.COM/CLMATHLIBRARIES/CLFFT • Users set all FFT state in an FFT plan object when initializing • Call ‘BakePlan’ using the plan object to tell the library to JIT and compile the kernel outside of performance sensitive loops • Reuse those plans as much as possible! clFFT contains the concept of ‘plans’, which allows the library to tune OpenCL kernels at runtime
- 11. 11 | HETEROGENEOUS MATH LIBRARIES | DECEMBER 16, 2014 CLFFT - HTTPS://GITHUB.COM/CLMATHLIBRARIES/CLFFT PERFORMANCE clFFT v2.3.1 included in ACML v6.1 This version contains optimizations not yet pushed into public github repo You can use the clFFT.h header file from GitHub to compile your application, then use the binary from ACML Benchmark system 64bit Linux FirePro W9100 Catalyst Pro 14.301.1010 AMD A10-7850K
- 12. ACML 6
- 13. 13 | HETEROGENEOUS MATH LIBRARIES | DECEMBER 16, 2014 ACML 6 INTRODUCES HETEROGENEOUS COMPUTE OpenCL can be a difficult language to learn ‒ There exists legacy applications that won’t be ported to OpenCL ‒ They might be willing to sacrifice peak performance for program portability ACML 6 includes clBLAS & clFFT as new backends ‒ ACML hides all OpenCL programming from end users ‒ Client programs do not need to change at all; they only relink ACML 6 When ACML determines that a particular BLAS or FFT call will gain benefit from offloading computation, it will do so without knowledge of the client program LEVERAGING CLMATH LIBRARIES TO ACCELERATE WITH OPENCL ACML 6 keeps the same API!
- 14. 14 | HETEROGENEOUS MATH LIBRARIES | DECEMBER 16, 2014 NEW FFTW WRAPPER ACML 6 now ships with fftw.h FFTW programs could link with ACML 6 to offload computation onto OpenCL devices No changes in host code required!
- 15. 15 | HETEROGENEOUS MATH LIBRARIES | DECEMBER 16, 2014 ACMLSCRIPT • The scripting language uses Lua, with custom ACML callback functions • http://www.lua.org/ • Refer to chapter 7 of the ACML documentation for more information on how to modify or create your own scripts ACML includes a new scripting language that expresses the logic ACML uses to offload computation
- 16. 16 | HETEROGENEOUS MATH LIBRARIES | DECEMBER 16, 2014 ACMLSCRIPT: 3-PART VIDEO TUTORIALS ACMLScript: Part 1 ACMLScript: Part 2 ACMLScript: Part 3 HTTPS://WWW.YOUTUBE.COM/USER/AMDDEVCENTRAL
- 17. 17 | HETEROGENEOUS MATH LIBRARIES | DECEMBER 16, 2014 ACML- HTTPS://GITHUB.COM/CLMATHLIBRARIES/CLFFT PERFORMANCE ACML v6.0 sgemm Slightly old at this time Notice that the green line is equivalent to Max( blue, red ) ACML loads the host processor if the problem is too small to benefit from GPU acceleration Benchmark system AMD A10-7850K CPU & GPU 64bit Linux Catalyst 14.301.1001
- 19. 19 | HETEROGENEOUS MATH LIBRARIES | DECEMBER 16, 2014 CLMAGMA clMAGMA implements LAPACK functionality with OpenCL acceleration https://bitbucket.org/icl/clmagma Maintained by the University of Tennessee Knoxville
- 20. 20 | HETEROGENEOUS MATH LIBRARIES | DECEMBER 16, 2014 CLMAGMA The newest v1.3 Supports ‒ LU, QR and Cholesky factorizations ‒ Linear and least squares solvers ‒ Reductions to Hessenberg, bidiagonal and tridiagonal forms ‒ Eigen and singular value problem solvers ‒ Orthogonal transformation routines clMagma uses clBLAS as the GPU compute backend ‒ It currently provides static load balancing between CPU & GPU cores Multi-GPU support LEVERAGING CLMATH LIBRARIES TO ACCELERATE WITH OPENCL v1.3 adds support for Windows and Mac OSX
- 21. Open Source Bolt
- 22. 22 | HETEROGENEOUS MATH LIBRARIES | DECEMBER 16, 2014 BOLT Bolt implements parallel C++ STL functionality with AMP & OpenCL acceleration Bolt on GitHub Maintained by AMD
- 23. 23 | HETEROGENEOUS MATH LIBRARIES | DECEMBER 16, 2014 BOLT Bolt provides containers and algorithms that enable clients to accelerate C++ code with minimal GPU knowledge ‒ Sorts ‒ Reductions ‒ Transforms ‒ Scans Through control structures, clients control where data is allocated and computed (minimal knowledge of AMP or OpenCL is helpful here) Bolt provides support for both OpenCL & C++ AMP paths PARALLEL STL Bolt provides containers such as bolt::device_vector<>
- 24. 24 | HETEROGENEOUS MATH LIBRARIES | DECEMBER 16, 2014 BOLT #include <bolt/cl/device_vector.h> #include <bolt/cl/scan.h> #include <vector> #include <numeric> int main() { size_t length = 1024; // Create device_vector and initialize it to 1 bolt::cl::device_vector< int > boltInput( length, 1 ); // Calculate the inclusive_scan of the device_vector bolt::cl::inclusive_scan(boltInput.begin(),boltInput.end(),boltInput.begin( ) ); // Create an std vector and initialize it to 1 std::vector< int > stdInput( length, 1 ); // Calculate the inclusive_scan of the std vector bolt::cl::inclusive_scan(stdInput.begin( ),stdInput.end( ),stdInput.begin( ) ); return 0; } EXAMPLE CODE
- 25. 25 | HETEROGENEOUS MATH LIBRARIES | DECEMBER 16, 2014 Q&A & CONTACT INFO For More Info: Follow us on Twitter: @AMDDevCentral Visit our forums: http://devgurus.amd.com/welcome Visit our website: www.developer.amd.com Watch the replay: www.youtube.com/user/AMDDevCentral Download the presentation: www.slideshare.net/DevCentralAMD
- 26. 26 | HETEROGENEOUS MATH LIBRARIES | DECEMBER 16, 2014 DISCLAIMER & ATTRIBUTION The information presented in this document is for informational purposes only and may contain technical inaccuracies, omissions and typographical errors. The information contained herein is subject to change and may be rendered inaccurate for many reasons, including but not limited to product and roadmap changes, component and motherboard version changes, new model and/or product releases, product differences between differing manufacturers, software changes, BIOS flashes, firmware upgrades, or the like. AMD assumes no obligation to update or otherwise correct or revise this information. However, AMD reserves the right to revise this information and to make changes from time to time to the content hereof without obligation of AMD to notify any person of such revisions or changes. AMD MAKES NO REPRESENTATIONS OR WARRANTIES WITH RESPECT TO THE CONTENTS HEREOF AND ASSUMES NO RESPONSIBILITY FOR ANY INACCURACIES, ERRORS OR OMISSIONS THAT MAY APPEAR IN THIS INFORMATION. AMD SPECIFICALLY DISCLAIMS ANY IMPLIED WARRANTIES OF MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE. IN NO EVENT WILL AMD BE LIABLE TO ANY PERSON FOR ANY DIRECT, INDIRECT, SPECIAL OR OTHER CONSEQUENTIAL DAMAGES ARISING FROM THE USE OF ANY INFORMATION CONTAINED HEREIN, EVEN IF AMD IS EXPRESSLY ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. ATTRIBUTION © 2014 Advanced Micro Devices, Inc. All rights reserved. AMD, the AMD Arrow logo and combinations thereof are trademarks of Advanced Micro Devices, Inc. in the United States and/or other jurisdictions. Other names are for informational purposes only and may be trademarks of their respective owners.