OpenBLAS Alternatives

OpenBLAS reviews and mentions

• Comparing OpenBLAS and Accelerate on Apple Silicon for BLAS Routines
  2 projects | dev.to | 18 Nov 2025

  There are several CPU-based BLAS libraries, some of them developed by hardware manifacturers, such as Intel's MKL, AMD's BLIS and Apple's Accelerate. OpenBLAS is an open source library that has the broadest coverage of supported hardware and can be a solid default choice.

• Python performance myths and fairy tales
  2 projects | news.ycombinator.com | 6 Aug 2025

  Sure they do.

  https://github.com/OpenMathLib/OpenBLAS

  Plenty of assembly in that project but no mention of it in the README.

• LAPACK in your web browser
  9 projects | dev.to | 20 Dec 2024

  To take NumPy as an example, NumPy is a single monolithic library, where all of its components, outside of optional third-party dependencies such as OpenBLAS, form a single, indivisible unit. One cannot simply install NumPy routines for array manipulation without installing all of NumPy. If you are deploying an application which only needs NumPy's ndarray object and a couple of its manipulation routines, installing and bundling all of NumPy means including a considerable amount of "dead code". In web development parlance, we'd say that NumPy is not "tree shakeable". For a normal NumPy installation, this implies at least 30MB of disk space, and at least 15MB of disk space for a customized build which excludes all debug statements. For SciPy, those numbers can balloon to 130MB and 50MB, respectively. Needless to say, shipping a 15MB library in a web application for just a few functions is a non-starter, especially for developers needing to deploy web applications to devices with poor network connectivity or memory constraints.

• LLaMA Now Goes Faster on CPUs
  16 projects | news.ycombinator.com | 31 Mar 2024

  The Fortran implementation is just a reference implementation. The goal of reference BLAS [0] is to provide relatively simple and easy to understand implementations which demonstrate the interface and are intended to give correct results to test against. Perhaps an exceptional Fortran compiler which doesn't yet exist could generate code which rivals hand (or automatically) tuned optimized BLAS libraries like OpenBLAS [1], MKL [2], ATLAS [3], and those based on BLIS [4], but in practice this is not observed.

  Justine observed that the threading model for LLaMA makes it impractical to integrate one of these optimized BLAS libraries, so she wrote her own hand-tuned implementations following the same principles they use.

  [0] https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprogra...

  [1] https://github.com/OpenMathLib/OpenBLAS

  [2] https://www.intel.com/content/www/us/en/developer/tools/onea...

  [3] https://en.wikipedia.org/wiki/Automatically_Tuned_Linear_Alg...

  [4]https://en.wikipedia.org/wiki/BLIS_(software)

• Assume I'm an idiot - oogabooga LLaMa.cpp??!
  4 projects | /r/LocalLLaMA | 23 Jun 2023
• Learn x86-64 assembly by writing a GUI from scratch
  11 projects | news.ycombinator.com | 1 Jun 2023

  Yeah. I'm going to be helping to work on expanding CI for OpenBlas and have been diving into this stuff lately. See the discussion in this closed OpenBlas issue gh-1968 [0] for instance. OpenBlas's Skylake kernels do rely on intrinsics [1] for compilers that support them, but there's a wide range of architectures to support, and when hand-tuned assembly kernels work better, that's what are used. For example, [2].

  [0] https://github.com/xianyi/OpenBLAS/issues/1968

  [1] https://github.com/xianyi/OpenBLAS/blob/develop/kernel/x86_6...

  [2] https://github.com/xianyi/OpenBLAS/blob/23693f09a26ffd8b60eb...

• AI’s compute fragmentation: what matrix multiplication teaches us
  4 projects | news.ycombinator.com | 23 Mar 2023

  We'll have to wait until part 2 to see what they are actually proposing, but they are trying to solve a real problem. To get a sense of things check out the handwritten assembly kernels in OpenBlas [0]. Note the level of granularity. There are micro-optimized implementations for specific chipsets.

  If progress in ML will be aided by a proliferation of hyper-specialized hardware, then there really is a scalability issue around developing optimized matmul routines for each specialized chip. To be able to develop a custom ASIC for a particular application and then easily generate the necessary matrix libraries without having to write hand-crafted assembly for each specific case seems like it could be very powerful.

  [0] https://github.com/xianyi/OpenBLAS/tree/develop/kernel

• Trying downloading BCML
  1 project | /r/learnpython | 18 Jan 2023

  libraries mkl_rt not found in ['C:\python\lib', 'C:\', 'C:\python\libs'] ``` Install this and try again. Might need to reboot, never know with Windows https://www.openblas.net/

• The Bitter Truth: Python 3.11 vs Cython vs C++ Performance for Simulations
  2 projects | /r/programming | 27 Dec 2022

  There isn't any fortran code in the repo there itself but numpy itself can be linked with several numeric libraries. If you look through the wheels for numpy available on pypi, all the latest ones are packaged with OpenBLAS which uses Fortran quite a bit: https://github.com/xianyi/OpenBLAS

• Optimizing compilers reload vector constants needlessly
  7 projects | news.ycombinator.com | 6 Dec 2022
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