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Basic Linear Algebra Subprograms

BLAS
Stable release
3.11.0 / 11 November 2022 (2022-11-11)
Written independs on implementation
PlatformCross-platform
TypeLibrary
Websitewww.netlib.org/blas/

Basic Linear Algebra Subprograms (BLAS) is a specification that prescribes a set of low-level routines for performing common linear algebra operations such as vector addition, scalar multiplication, dot products, linear combinations, and matrix multiplication. They are the de facto standard low-level routines for linear algebra libraries; the routines have bindings for both C ("CBLAS interface") and Fortran ("BLAS interface"). Although the BLAS specification is general, BLAS implementations are often optimized for speed on a particular machine, so using them can bring substantial performance benefits. BLAS implementations will take advantage of special floating point hardware such as vector registers or SIMD instructions.

It originated as a Fortran library in 1979[1] and its interface was standardized by the BLAS Technical (BLAST) Forum, whose latest BLAS report can be found on the netlib website.[2] This Fortran library is known as the reference implementation (sometimes confusingly referred to as the BLAS library) and is not optimized for speed but is in the public domain.[3][4]

Most libraries that offer linear algebra routines conform to the BLAS interface, allowing library users to develop programs that are indifferent to the BLAS library being used.

Many BLAS libraries have been developed, targeting various different hardware platforms. Examples includes cuBLAS (NVIDIA GPU, GPGPU), rocBLAS (AMD GPU), and OpenBLAS. Examples of CPU-based BLAS library branches include: OpenBLAS, BLIS (BLAS-like Library Instantiation Software), Arm Performance Libraries,[5] ATLAS, and Intel Math Kernel Library (iMKL). AMD maintains a fork of BLIS that is optimized for the AMD platform.[6] ATLAS is a portable library that automatically optimizes itself for an arbitrary architecture. iMKL is a freeware[7] and proprietary[8] vendor library optimized for x86 and x86-64 with a performance emphasis on Intel processors.[9] OpenBLAS is an open-source library that is hand-optimized for many of the popular architectures. The LINPACK benchmarks rely heavily on the BLAS routine gemm for its performance measurements.

Many numerical software applications use BLAS-compatible libraries to do linear algebra computations, including LAPACK, LINPACK, Armadillo, GNU Octave, Mathematica,[10] MATLAB,[11] NumPy,[12] R, Julia and Lisp-Stat.

  1. ^ *Lawson, C. L.; Hanson, R. J.; Kincaid, D.; Krogh, F. T. (1979). "Basic Linear Algebra Subprograms for FORTRAN usage". ACM Trans. Math. Softw. 5 (3): 308–323. doi:10.1145/355841.355847. hdl:2060/19780018835. S2CID 6585321. Algorithm 539.
  2. ^ "BLAS Technical Forum". netlib.org. Retrieved 2017-07-07.
  3. ^ blaseman Archived 2016-10-12 at the Wayback Machine "The products are the implementations of the public domain BLAS (Basic Linear Algebra Subprograms) and LAPACK (Linear Algebra PACKage), which have been developed by groups of people such as Prof. Jack Dongarra, University of Tennessee, USA and all published on the WWW (URL: https://www.netlib.org/)."[permanent dead link]
  4. ^ Jack Dongarra; Gene Golub; Eric Grosse; Cleve Moler; Keith Moore. "Netlib and NA-Net: building a scientific computing community" (PDF). netlib.org. Retrieved 2016-02-13. The Netlib software repository was created in 1984 to facilitate quick distribution of public domain software routines for use in scientific computation.
  5. ^ "Arm Performance Libraries". Arm. 2020. Retrieved 2020-12-16.
  6. ^ "BLAS Library".
  7. ^ "No Cost Options for Intel Math Kernel Library (MKL), Support yourself, Royalty-Free". Intel. 2015. Retrieved 2015-08-31.
  8. ^ "Intel Math Kernel Library (Intel MKL)". Intel. 2015. Retrieved 2015-08-25.
  9. ^ "Optimization Notice". Intel. 2012. Retrieved 2013-04-10.
  10. ^ Douglas Quinney (2003). "So what's new in Mathematica 5.0?" (PDF). MSOR Connections. 3 (4). The Higher Education Academy. Archived from the original (PDF) on 2013-10-29.
  11. ^ Cleve Moler (2000). "MATLAB Incorporates LAPACK". MathWorks. Retrieved 2013-10-26.
  12. ^ Stéfan van der Walt; S. Chris Colbert & Gaël Varoquaux (2011). "The NumPy array: a structure for efficient numerical computation". Computing in Science and Engineering. 13 (2): 22–30. arXiv:1102.1523. Bibcode:2011CSE....13b..22V. doi:10.1109/MCSE.2011.37. S2CID 16907816.
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