blis
sundials
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| blis | sundials | |
|---|---|---|
| 16 | 1 | |
| 2,073 | 449 | |
| 4.1% | 2.0% | |
| 7.1 | 8.6 | |
| 8 days ago | 6 days ago | |
| C | C | |
| GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
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blis
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Optimize sgemm on RISC-V platform
There is a recent update to the blis alternative to BLAS that includes a number of RISC-V performance optimizations.
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BLIS: Portable basis for high-performance BLAS-like linear algebra libs
https://github.com/flame/blis/blob/master/docs/Performance.m...
It seems that the selling point is that BLIS does multi-core quite well. I am especially impressed that it does as well as the highly optimized Intel's MKL on Intel's CPUs.
I do not see the selling point of BLIS-specific APIs, though. The whole point of having an open BLAS API standard is that numerical libraries should be drop-in replaceable, so when a new library (such as BLIS here) comes along, one could just re-link the library and reap the performance gain immediately.
What is interesting is that numerical algebra work, by nature, is mostly embarrassingly parallel, so it should not be too difficult to write multi-core implementations. And yet, BLIS here performs so much better than some other industry-leading implementations on multi-core configurations. So the question is not why BLIS does so well; the question is why some other implementations do so poorly.
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Benchmarking 20 programming languages on N-queens and matrix multiplication
First we can use Laser, which was my initial BLAS experiment in 2019. At the time in particular, OpenBLAS didn't properly use the AVX512 VPUs. (See thread in BLIS https://github.com/flame/blis/issues/352 ), It has made progress since then, still, on my current laptop perf is in the same range
Reproduction:
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The Art of High Performance Computing
https://github.com/flame/blis/
Field et al, recent winners of the James H. Wilkinson Prize for Numerical Software.
Field and Goto both worked with Robert van de Geijn. Lots of TACC interaction in that broader team.
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[D] Which BLAS library to choose for apple silicon?
BLIS is fine too~ https://github.com/flame/blis
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Small Neural networks in Julia 5x faster than PyTorch
The article asks "Which Micro-optimizations matter for BLAS3?", implying small dimensions, but doesn't actually tell me. The problem is well-studied, depending on what you consider "small". The most important thing is to avoid the packing step below an appropriate threshold. Implementations include libxsmm, blasfeo, and the "sup" version in blis (with papers on libxsmm and blasfeo). Eigen might also be relevant.
- Eigen: A C++ template library for linear algebra
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Matrix Multiplication Inches Closer To Mythic Goal
However, on recent CPUs 4x4 is small for the innermost block size of the non-trivial hierarchy you need. You can see examples under https://github.com/flame/blis/tree/master/config with an a priori procedure for determining them in https://www.cs.utexas.edu/users/flame/pubs/TOMS-BLIS-Analyti... (but compare with what's actually used for SKX, in particular). OpenBLAS will normally be similar, though it may come out somewhat faster, but it's easier to see in BLIS.
sundials
What are some alternatives?
tiny-cuda-nn - Lightning fast C++/CUDA neural network framework
Fastor - A lightweight high performance tensor algebra framework for modern C++
DAGSfM - Distributed and Graph-based Structure from Motion. This project includes the official implementation of our Pattern Recognition 2020 paper: Graph-Based Parallel Large Scale Structure from Motion.
vectorflow
how-to-optimize-gemm
DirectXMath - DirectXMath is an all inline SIMD C++ linear algebra library for use in games and graphics apps
xtensor - C++ tensors with broadcasting and lazy computing
diffrax - Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
blasfeo - Basic linear algebra subroutines for embedded optimization
LeNetTorch - PyTorch implementation of LeNet for fitting MNIST for benchmarking.
slas - Static Linear Algebra System
juliaup - Julia installer and version multiplexer