MPI.jl VS TriangularSolve.jl

Compare MPI.jl vs TriangularSolve.jl and see what are their differences.

MPI.jl

MPI wrappers for Julia (by JuliaParallel)
Mpi Julia HPC julia-language mpich openmpi microsoft-mpi
Source Code
juliaparallel.org
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TriangularSolve.jl

rdiv!(::AbstractMatrix, ::UpperTriangular) and ldiv!(::LowerTriangular, ::AbstractMatrix) (by JuliaSIMD)
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MPI.jl TriangularSolve.jl
3 1
359 12
0.6% -
7.8 6.6
9 days ago 6 days ago
Julia Julia
The Unlicense MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

MPI.jl

Posts with mentions or reviews of MPI.jl. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-04-30.

TriangularSolve.jl

Posts with mentions or reviews of TriangularSolve.jl. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-01-07.
  • Why Fortran is easy to learn
    19 projects | news.ycombinator.com | 7 Jan 2022
    > 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

What are some alternatives?

When comparing MPI.jl and TriangularSolve.jl you can also consider the following projects:

ImplicitGlobalGrid.jl - Almost trivial distributed parallelization of stencil-based GPU and CPU applications on a regular staggered grid

Pkg.jl - Pkg - Package manager for the Julia programming language

DataFrames.jl - In-memory tabular data in Julia

SuiteSparse.jl - Development of SuiteSparse.jl, which ships as part of the Julia standard library.

Makie.jl - Interactive data visualizations and plotting in Julia

GPUCompiler.jl - Reusable compiler infrastructure for Julia GPU backends.

SciMLBenchmarks.jl - Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R

BLIS.jl - This repo plans to provide a low-level Julia wrapper for BLIS typed interface.

CUDA.jl - CUDA programming in Julia.

rr - Record and Replay Framework

Fortran-code-on-GitHub - Directory of Fortran codes on GitHub, arranged by topic

18335 - 18.335 - Introduction to Numerical Methods course

MPI.jl vs ImplicitGlobalGrid.jl TriangularSolve.jl vs Pkg.jl MPI.jl vs DataFrames.jl TriangularSolve.jl vs SuiteSparse.jl MPI.jl vs Makie.jl TriangularSolve.jl vs GPUCompiler.jl MPI.jl vs SciMLBenchmarks.jl TriangularSolve.jl vs BLIS.jl MPI.jl vs CUDA.jl TriangularSolve.jl vs rr MPI.jl vs Fortran-code-on-GitHub TriangularSolve.jl vs 18335