FLoops.jl
Fast sequential, threaded, and distributed for-loops for Julia—fold for humans™ (by JuliaFolds)
| jochen.gitlab.io | FLoops.jl | |
|---|---|---|
| 1 | 3 | |
| - | 302 | |
| - | 0.3% | |
| - | 0.0 | |
| - | 9 days ago | |
| Julia | ||
| - | 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.
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.
jochen.gitlab.io
Posts with mentions or reviews of jochen.gitlab.io.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-01-19.
-
DSP Performance Comparison Numpy vs. Cython vs. Numba vs. Pythran vs. Julia
The repository is here https://gitlab.com/Jochen/jochen.gitlab.io
(you can find it under code in the navigation bar).
Regarding dummy arrays, you can just generate random arrays of complex values, that should normally not cause issues (although obviously the filter does not converge to anything). The size I used for the demo is (2, 200 000), i.e. 2 polarisations and 100 000 symbols 2 times oversampled
FLoops.jl
Posts with mentions or reviews of FLoops.jl.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-01-19.
- 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?
When comparing jochen.gitlab.io and FLoops.jl you can also consider the following projects:
FoldsCUDA.jl - Data-parallelism on CUDA using Transducers.jl and for loops (FLoops.jl)
ThreadsX.jl - Parallelized Base functions
Transducers.jl - Efficient transducers for Julia
DPMMSubClusters.jl - Distributed MCMC Inference in Dirichlet Process Mixture Models (High Performance Machine Learning Workshop 2019)