BLIS.jl
MPI.jl
| BLIS.jl | MPI.jl | |
|---|---|---|
| 1 | 3 | |
| 26 | 362 | |
| - | 1.4% | |
| 1.3 | 7.8 | |
| about 1 year ago | 7 days ago | |
| Julia | Julia | |
| BSD 3-clause "New" or "Revised" License | The Unlicense |
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BLIS.jl
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Why Fortran is easy to learn
It doesn't look like you're measuring factorization performance? OpenBLAS matrix-matrix multiplication is fine, it just falls apart when going to things like Cholesky and LU.
> not the default, I've now checked
Whatever the Julia default build is doing, so probably not the recursive LAPACK routines then if that's how it's being built. If there's a better default that's worth an issue.
> That said, I don't understand why people avoid AMD's BLAS/LAPACK
There just isn't a BLIS wrapper into Julia right now, and it's much easier to just write new BLAS tools than to build wrappers IMO. It makes it very easy to customize to nonstandard Julia number types too. But I do think that BLIS is a great project and I would like to see it replace OpenBLAS as the default. There's been some discussion to make it as easy as MKL (https://github.com/JuliaLinearAlgebra/BLIS.jl/issues/3).
MPI.jl
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Parallélisation distribuée presque triviale d’applications GPU et CPU basées sur des Stencils avec…
GitHub - JuliaParallel/MPI.jl: MPI wrappers for Julia
- Why Fortran is easy to learn
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MPI and HPC libraries
I have actually had good success with Julia for this: https://github.com/JuliaParallel/MPI.jl. I acknowledge this community may not appreciate me sharing that.
What are some alternatives?
SuiteSparse.jl - Development of SuiteSparse.jl, which ships as part of the Julia standard library.
ImplicitGlobalGrid.jl - Almost trivial distributed parallelization of stencil-based GPU and CPU applications on a regular staggered grid
18337 - 18.337 - Parallel Computing and Scientific Machine Learning
DataFrames.jl - In-memory tabular data in Julia
GPUCompiler.jl - Reusable compiler infrastructure for Julia GPU backends.
Makie.jl - Interactive data visualizations and plotting in Julia
quickjs - Public repository of the QuickJS Javascript Engine.
SciMLBenchmarks.jl - Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
TriangularSolve.jl - rdiv!(::AbstractMatrix, ::UpperTriangular) and ldiv!(::LowerTriangular, ::AbstractMatrix)
CUDA.jl - CUDA programming in Julia.
Fortran-code-on-GitHub - Directory of Fortran codes on GitHub, arranged by topic