JuliaLinearAlgebra/RecursiveFactorization.jl
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9 mentions on Hacker News by 3 people: 0 stories, 9 comments · first 2021-03-12 · latest 2023-05-02
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What about the other benchmarks on the same site? https://docs.sciml.ai/SciMLBenchmarksOutput/stable/Bio/BCR/ BCR takes about a hundred seconds and is pretty indicative of systems biological models, coming from 1122 ODEs with 24388 terms…
comment by ChrisRackauckas · 2023-05-02 · in Can Fortran survive another 15 years? -
They do, just not if they're using python. In Julia they absolutely do. Here's a matmul and LU respectively that are SOTA (especially for small matrices) https://github.com/JuliaLinearAlgebra/Octavian.jl and…
comment by adgjlsfhk1 · 2023-02-22 · in ML would have advanced faster if another front-end language had been adopted? -
Ask them to download Julia and try it, and file an issue if it is not fast enough. We try to have the latest available. See for example: https://github.com/JuliaLinearAlgebra/RecursiveFactorization...
comment by ViralBShah · 2022-04-14 · in Doing small network scientific machine learning in Julia faster than PyTorch -
…The recursive comparison is against OpenBLAS/LAPACK and MKL, see this PR for some (older) details: https://github.com/YingboMa/RecursiveFactorization.jl/pull/2... . What it really comes down to in the end is that OpenBLAS is rather bad,…
comment by ChrisRackauckas · 2022-01-08 · in Fortran is easy to learn -
…the instances we use it for (mostly LU-factorization) and those are all written in pure Julia. See https://github.com/YingboMa/RecursiveFactorization.jl , https://github.com/JuliaSIMD/TriangularSolve.jl , and…
comment by ChrisRackauckas · 2022-01-08 · in Fortran is easy to learn -
…of OpenBLAS and MKL (because they tend to outperform standard BLAS's for the operations we use https://github.com/YingboMa/RecursiveFactorization.jl/pull/2... ) also generate good code for M1, so that was giving us some powerful use cases…
comment by ChrisRackauckas · 2021-12-01 · in Julia 1.7 Highlights -
…because the pure Julia BLAS tools outperform OpenBLAS and MKL in certain areas. For example see: https://github.com/YingboMa/RecursiveFactorization.jl/pull/2... So a stiff ODE solve is pure Julia, LU-factorizations and all. This is what…
comment by ChrisRackauckas · 2021-09-25 · in Why I Use Nim instead of Python for Data Processing -
…githubusercontent.com/JuliaLinearAlgebra/Octavia... https://github.com/JuliaLinearAlgebra/Octavian.jl https://github.com/YingboMa/RecursiveFactorization.jl
comment by ChrisRackauckas · 2021-05-23 · in An early look at Postgres 14: Performance and monitoring Improvements -
…numerical linear algebra libraries which greatly outperform OpenBLAS in the regime they are in: https://github.com/YingboMa/RecursiveFactorization.jl#perfor... And there are hooks in the differential equation solvers to not use OpenBLAS in…
comment by ChrisRackauckas · 2021-03-12 · in Julia receives DARPA award to accelerate electronics simulation