ThreadsX.jl
FLoops.jl
| ThreadsX.jl | FLoops.jl | |
|---|---|---|
| 1 | 3 | |
| 309 | 303 | |
| - | 0.7% | |
| 0.0 | 0.0 | |
| 9 days ago | 3 days ago | |
| Julia | Julia | |
| MIT License | MIT License |
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ThreadsX.jl
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Pmap alternative with multithreading
I think ThreadsX.map will do the trick: https://github.com/tkf/ThreadsX.jl
FLoops.jl
- Floops.jl: unified system for safe threaded, distributed and GPU loops in Julia
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Any R::parallel like workflow for multiprocessing?
There is a number of good packages in https://github.com/JuliaFolds organization. You need to browse a little too find one, which is most suitable for your needs. I suppose https://github.com/JuliaFolds/FLoops.jl is the most applicable for your needs. As an additional bonus, you'll be able to switch from single thread, to multithread, distributed and even CUDA version with a change of a single executor.
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DSP Performance Comparison Numpy vs. Cython vs. Numba vs. Pythran vs. Julia
Are the Cython and Pythran codes running in parallel? To do that with Julia: https://docs.julialang.org/en/v1/manual/multi-threading/
Or https://github.com/JuliaFolds/FLoops.jl
What are some alternatives?
Pluto.jl - 🎈 Simple reactive notebooks for Julia
FoldsCUDA.jl - Data-parallelism on CUDA using Transducers.jl and for loops (FLoops.jl)
DifferentialEquations.jl - Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
jochen.gitlab.io
julia - The Julia Programming Language
Transducers.jl - Efficient transducers for Julia
DataFrames.jl - In-memory tabular data in Julia
DPMMSubClusters.jl - Distributed MCMC Inference in Dirichlet Process Mixture Models (High Performance Machine Learning Workshop 2019)