GenericArpack.jl
RecursiveArrayTools.jl
| GenericArpack.jl | RecursiveArrayTools.jl | |
|---|---|---|
| 1 | 3 | |
| 24 | 202 | |
| - | 2.5% | |
| 3.2 | 9.4 | |
| 6 months ago | 6 days ago | |
| Julia | Julia | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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.
GenericArpack.jl
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Julia 1.8 has been released
For some examples of people porting existing C++ Fortran libraries to julia, you should check out https://github.com/JuliaLinearAlgebra/Octavian.jl, https://github.com/dgleich/GenericArpack.jl, https://github.com/apache/arrow-julia (just off the top of my head). These are all ports of C++ or Fortran libraries that match (or exceed) performance of the original, and in the case of Arrow.jl is faster, more general, and 10x less code.
RecursiveArrayTools.jl
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Julia's latency: Past, present and future
You're not really supposed to be using StaticArraysCore anymore, but here's a somewhat older PR that shows the siginificance of moving StaticArray functionality on a smaller library, moving it from 6228ms to 292ms load time (https://github.com/SciML/RecursiveArrayTools.jl/pull/217).
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Julia 1.8 has been released
> > This gives the package authors a tool to basically "profile" the loading time of their package, which will help them optimize the loading time. So there _will_ be downstream improvement to package loading for us users too.
It lead to https://github.com/SciML/RecursiveArrayTools.jl/pull/217 . 6228.5 ms to 292.7 ms isn't too shabby.
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“Why I still recommend Julia”
The load times on some core packages were reduced by an order of magnitude this month. For example, RecursiveArrayTools went from 6228.5 ms to 292.7 ms. This was due to the new `@time_imports` in the Julia v1.8-beta helping to isolate load time issues. See https://github.com/SciML/RecursiveArrayTools.jl/pull/217 . This of course doesn't mean load times have been solved everywhere, but we now have the tooling to identify the root causes and it's actively being worked on from multiple directions.
What are some alternatives?
arrow-julia - Official Julia implementation of Apache Arrow
ITensors.jl - A Julia library for efficient tensor computations and tensor network calculations
SciMLStyle - A style guide for stylish Julia developers
ProtoStructs.jl - Easy prototyping of structs
Lux.jl - Explicitly Parameterized Neural Networks in Julia
Octavian.jl - Multi-threaded BLAS-like library that provides pure Julia matrix multiplication
ObjectOriented.jl - Conventional object-oriented programming in Julia without breaking Julia's core design ideas
SciMLSensitivity.jl - A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.
SciPy - SciPy library main repository