Basic Linear Algebra Subprograms: Difference between revisions

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Revision as of 12:44, 17 March 2024 edit Wootery (talk \| contribs) Extended confirmed users 868 edits Revert changes by 64.121.97.220 on 17:45, 23 December 2022, which degraded the explanation of the basic nature of BLAS. I also improved the (original) explanation a little. ← Previous edit		Latest revision as of 04:41, 14 August 2025 edit undo InternetArchiveBot (talk \| contribs) Bots, Pending changes reviewers 5,689,123 edits Rescuing 1 sources and tagging 0 as dead.) #IABot (v2.0.9.5
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Line 98: ; [[BLIS (software)\|BLIS]]: BLAS-like Library Instantiation Software framework for rapid instantiation. Optimized for most modern CPUs. BLIS is a complete refactoring of the GotoBLAS that reduces the amount of code that must be written for a given platform.<ref>{{Citation\|title=blis: BLAS-like Library Instantiation Software Framework\|date=2017-06-30\|url=https://github.com/flame/blis\|publisher=flame\|access-date=2017-07-07}}</ref><ref>{{Citation\|title=BLIS GitHub Repository\|date=15 October 2021\|url=https://github.com/flame/blis}}</ref> ; C++ AMP BLAS: The [[C++ AMP]] BLAS Library is an [[Open-source software\|open source]] implementation of BLAS for Microsoft's AMP language extension for Visual C++.<ref>{{Cite web\|url=http://ampblas.codeplex.com/\|title=C++ AMP BLAS Library\|website=CodePlex\|language=en\|access-date=2017-07-07\|archive-date=2017-07-08 \|archive-url=https://web.archive.org/web/20170708151515/http://ampblas.codeplex.com/\|url-status=dead}}</ref> ; cuBLAS: Optimized BLAS for ~~NVIDIA~~ Nvidia-based GPU cards, requiring few additional library calls.<ref>{{Cite news\|url=https://developer.nvidia.com/cublas\|title=cuBLAS\|date=2013-07-29\|work=NVIDIA Developer\|access-date=2017-07-07\|language=en}}</ref> ; NVBLAS: Optimized BLAS for ~~NVIDIA~~ Nvidia-based GPU cards, providing only Level 3 functions, but as direct drop-in replacement for other BLAS libraries.<ref>{{Cite news\|url=https://docs.nvidia.com/cuda/nvblas/index.htmls\|title=NVBLAS\|date=2018-05-15\|work=NVIDIA Developer\|access-date=2018-05-15\|language=en}}{{Dead link\|date=September 2023 \|bot=InternetArchiveBot \|fix-attempted=yes }}</ref> ; clBLAS: An [[OpenCL]] implementation of BLAS by AMD. Part of the AMD Compute Libraries.<ref name="github.com">{{Citation\|title=clBLAS: a software library containing BLAS functions written in OpenCL\|date=2017-07-03\|url=https://github.com/clMathLibraries/clBLAS\|publisher=clMathLibraries\|access-date=2017-07-07}}</ref> ; clBLAST: A tuned [[OpenCL]] implementation of most of the BLAS api.<ref name="https://github.com/CNugteren/CLBlast">{{Citation\|last=Nugteren\|first=Cedric\|title=CLBlast: Tuned OpenCL BLAS\|date=2017-07-05\|url=https://github.com/CNugteren/CLBlast\|access-date=2017-07-07}}</ref> Line 113: ; [[OpenBLAS]]: Optimized BLAS based on GotoBLAS, supporting [[x86]], [[x86-64]], [[MIPS architecture\|MIPS]] and [[ARM architecture\|ARM]] processors.<ref>{{Cite web\|url=https://www.openblas.net/\|title=OpenBLAS : An optimized BLAS library\|website=www.openblas.net\|access-date=2017-07-07}}</ref> ; PDLIB/SX: [[NEC Corporation\|NEC]]'s Public Domain Mathematical Library for the NEC [[NEC SX architecture\|SX-4]] system.<ref name=":0">{{cite web \|url=http://www.nec.co.jp/hpc/mediator/sxm_e/software/61.html \|title=PDLIB/SX: Business Solution \| NEC \|access-date=2007-05-20 \|url-status=dead \|archive-url=https://web.archive.org/web/20070222154031/http://www.nec.co.jp/hpc/mediator/sxm_e/software/61.html \|archive-date=2007-02-22 }}</ref> ; rocBLAS: Implementation that runs on [[AMD]] GPUs via [[ROCm]].<ref>{{Cite web\|url=https://rocmdocs.amd.com/en/latest/ROCm_Tools/rocblas.html\|title=rocBLAS\|website=rocmdocs.amd.com\|access-date=2021-05-21\|archive-date=2021-05-22 \|archive-url=https://web.archive.org/web/20210522003949/https://rocmdocs.amd.com/en/latest/ROCm_Tools/rocblas.html\|url-status=dead}}</ref> ;SCSL : [[Silicon Graphics\|SGI]]'s Scientific Computing Software Library contains BLAS and LAPACK implementations for SGI's [[Irix]] workstations.<ref>{{cite web \|url=http://www.sgi.com/products/software/scsl.html \|title=SGI - SCSL Scientific Library: Home Page \|access-date=2007-05-20 \|url-status=dead \|archive-url=https://web.archive.org/web/20070513173030/http://www.sgi.com/products/software/scsl.html \|archive-date=2007-05-13 }}</ref> ; Sun Performance Library: Optimized BLAS and LAPACK for [[SPARC]], [[Intel Core\|Core]] and [[AMD64]] architectures under [[Oracle Solaris\|Solaris]] 8, 9, and 10 as well as Linux.<ref>{{Cite web\|url=https://www.oracle.com/technetwork/server-storage/solarisstudio/overview/index.html\|title=Oracle Developer Studio\|website=www.oracle.com\|access-date=2017-07-07}}</ref> ; uBLAS: A generic [[C++]] template class library providing BLAS functionality. Part of the [[Boost library]]. It provides bindings to many hardware-accelerated libraries in a unifying notation. Moreover, uBLAS focuses on correctness of the algorithms using advanced C++ features.<ref>{{Cite web\|url=https://www.boost.org/doc/libs/1_60_0/libs/numeric/ublas/doc/index.html\|title=Boost Basic Linear Algebra - 1.60.0\|website=www.boost.org\|access-date=2017-07-07}}</ref> Line 144: The index <math>k</math> in square brackets indicates that the operation is performed for all matrices <math>k</math> in a stack. Often, this operation is implemented for a strided batched memory layout where all matrices follow concatenated in the arrays <math>A</math>, <math>B</math> and <math>C</math>. Batched BLAS functions can be a versatile tool and allow e.g. a fast implementation of [[exponential integrators]] and [[Magnus integrators]] that handle long integration periods with many time steps.<ref name="herb21">{{cite journal \|last1=Herb \|first1=Konstantin \|last2=Welter \|first2=Pol \|title=Parallel time integration using Batched BLAS (Basic Linear Algebra Subprograms) routines \|journal=Computer Physics Communications \|volume=270 \|pages=108181 \|date=2022 \|doi=10.1016/j.cpc.2021.108181 \|arxiv=2108.07126\|bibcode=2022CoPhC.27008181H \|s2cid=237091802 }}</ref> Here, the [[matrix exponentiation]], the computationally expensive part of the integration, can be implemented in parallel for all time-steps by using Batched BLAS functions. ==See also== Line 155: <ref name="Kazushige_2008">{{cite journal \|author-last1=Goto \|author-first1=Kazushige \|author-link1=Kazushige Goto \|author-last2=van de Geijn \|author-first2=Robert A. \|author-link2=Robert van de Geijn \|title=Anatomy of High-Performance Matrix Multiplication \|date=2008 \|journal=[[ACM Transactions on Mathematical Software]] \|issn=0098-3500 \|volume=34 \|issue=3 \|pages=12:1–12:25 \|doi=10.1145/1356052.1356053 \|citeseerx=10.1.1.111.3873\|s2cid=9359223 }} (25 pages) [https://www.cs.utexas.edu/~flame/web/FLAMEPublications.html#Goto]</ref> <ref name="GotoBLAS2">{{cite web \|title=GotoBLAS2 \|author-first=Kent \|author-last=Milfeld \|publisher=[[Texas Advanced Computing Center]] \|url=http://www.tacc.utexas.edu/tacc-software/gotoblas2 \|access-date=2024-03-17 \|url-status=dead \|archive-url=https://web.archive.org/web/20200323172521/https://www.tacc.utexas.edu/research-development/tacc-software/gotoblas2 \|archive-date=2020-03-23}}</ref> <ref name="Geijn_2008">{{cite journal \|author-last1=Goto \|author-first1=Kazushige \|author-link1=Kazushige Goto \|author-last2=van de Geijn \|author-first2=Robert A. \|author-link2=Robert van de Geijn \|title=High-performance implementation of the level-3 BLAS \|journal=[[ACM Transactions on Mathematical Software]] \|volume=35 \|issue=1 \|pages=1–14 \|date=2008 \|doi=10.1145/1377603.1377607 \|s2cid=14722514 \|archive-url=https://web.archive.org/web/20170706142000/ftp://ftp.cs.utexas.edu/pub/techreports/tr06-23.pdf \|archive-date=2017-07-06 \|url-status=dead \|url=ftp://ftp.cs.utexas.edu/pub/techreports/tr06-23.pdf}}</ref> }}