TriangularSolve.jl
MPI.jl
TriangularSolve.jl | MPI.jl | |
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1 | 3 | |
12 | 362 | |
- | 1.4% | |
6.6 | 7.8 | |
20 days ago | 16 days ago | |
Julia | Julia | |
MIT License | The Unlicense |
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TriangularSolve.jl
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Why Fortran is easy to learn
> But in the end, it's FORTRAN all the way down. Even in Julia.
That's not true. None of the Julia differential equation solver stack is calling into Fortran anymore. We have our own BLAS tools that outperform OpenBLAS and MKL in 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 https://github.com/JuliaLinearAlgebra/Octavian.jl. And this is one part of the DiffEq performance story. The performance of this of course is all validated on https://github.com/SciML/SciMLBenchmarks.jl
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?
Pkg.jl - Pkg - Package manager for the Julia programming language
ImplicitGlobalGrid.jl - Almost trivial distributed parallelization of stencil-based GPU and CPU applications on a regular staggered grid
SuiteSparse.jl - Development of SuiteSparse.jl, which ships as part of the Julia standard library.
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
BLIS.jl - This repo plans to provide a low-level Julia wrapper for BLIS typed interface.
SciMLBenchmarks.jl - Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
rr - Record and Replay Framework
CUDA.jl - CUDA programming in Julia.
18335 - 18.335 - Introduction to Numerical Methods course
Fortran-code-on-GitHub - Directory of Fortran codes on GitHub, arranged by topic