blis
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blis | juliaup | |
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16 | 10 | |
2,018 | 874 | |
4.5% | 4.2% | |
7.1 | 9.3 | |
about 22 hours ago | 10 days ago | |
C | Rust | |
GNU General Public License v3.0 or later | MIT 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.
juliaup
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How to tell Quarto where the julia-1.8 kernel is?
There is juliaup, available in the Microsoft store, for managing julia versions: https://github.com/JuliaLang/juliaup
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Julia 1.8 released
But it’s considered prerelease for Mac and Linux according to its git repository. So presumably it won’t become the default until it’s not experimental any more.
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Créer simplement un cluster k8s dans PhoenixNAP avec Rancher en quelques clics …
GitHub - JuliaLang/juliaup: Julia installer and version multiplexer GitHub - JuliaPluto/PlutoUI.jl GitHub - fonsp/Pluto.jl: 🎈 Simple reactive notebooks for Julia
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I don't want to abandon Rust for Julia
Use the right tool for the job! I would never write a system utility or OS or user application or compiler in Julia, for example. juliaup is a Julia utility being written in Rust for this exact reason!
- Small Neural networks in Julia 5x faster than PyTorch
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What's the standard procedure for package requests?
And yes, I know about jill.py and have considered using it as well. It seems fairly straightforwad to use. A similar project that I found interesting is juliaup. Still, I would definitely prefer just using zypper instead of relying on third party tools. That's why I figured I might as well ask on here.
What are some alternatives?
tiny-cuda-nn - Lightning fast C++/CUDA neural network framework
jill.py - A cross-platform installer for the Julia programming language
diffrax - Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
vectorflow
jill - Command line installer of the Julia Language.
Pluto.jl - 🎈 Simple reactive notebooks for Julia
sundials - Official development repository for SUNDIALS - a SUite of Nonlinear and DIfferential/ALgebraic equation Solvers. Pull requests are welcome for bug fixes and minor changes.
n - Node version management
how-to-optimize-gemm
xtensor - C++ tensors with broadcasting and lazy computing