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Top 23 Julia HacktoberFest Projects
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SaaSHub
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Optimization.jl
Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.
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OrdinaryDiffEq.jl
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
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ChainRules.jl
forward and reverse mode automatic differentiation primitives for Julia Base + StdLibs
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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.
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DiffEqBase.jl
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
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StochasticDiffEq.jl
Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem
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SaaSHub
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From my jolly Julia days I’m used to julia-vterm. This emacs package runs a Julia REPL using a full terminal emulator (emacs-libvterm). So in the pursuit of a nice hack, I M-x replace-string’d the word juliawith python and gave it a shot. Remarkably, the whole thing just worked without much tweaking and you can enjoy the result by checking out the GitHub repo.
It would also mean learning Julia, but you can write GPU kernels in Julia and then compile for NVidia CUDA, AMD ROCm or IBM oneAPI.
https://juliagpu.org/
I've written CUDA kernels and I knew nothing about it going in.
There has been a lot of research in Runge Kutta methods in the last couple decades which resulted in all kind of specialized Runge Kutta methods. You have high order ones, RK methods for stiff problems, embedded RK methods which benefit from adaprive step size control, RK-Nystrom methods for second order Problems, symplectic RK methods which preserve energy (eg. hamiltonian) ando so on. If you are interested in the numerics and the use cases I highly recommend checking out the Julia Libary OrdinaryDiffEq (https://github.com/SciML/OrdinaryDiffEq.jl). If you look into the documentation you find A LOT of implemented RK methods for all kind of use cases.
Project mention: Potential of the Julia programming language for high energy physics computing | news.ycombinator.com | 2023-12-04Thats for an entry point, you can search `Base.@main` to see a little summary of it. Later it will be able to be callable with `juliax` and `juliac` i.e. `~juliax test.jl` in shell.
DynamicalSystems looks like a heavy project. I don't think you can do much more on your own. There have been recent features in 1.10 that lets you just use the portion you need (just a weak dependency), and there is precompiletools.jl but these are on your side.
You can also look into https://github.com/dmolina/DaemonMode.jl for running a Julia process in the background and do your stuff in the shell without startup time until the standalone binaries are there.
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Julia HacktoberFest related posts
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Dart 3.3
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Ask HN: Best way to learn GPU programming?
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Potential of the Julia programming language for high energy physics computing
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What Apple hardware do I need for CUDA-based deep learning tasks?
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Julia 1.9.0 lives up to its promise
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jlrs v0.18: export types and functions written in Rust to Julia, improved version and platform support, and more!
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Julia 1.9 Highlights
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A note from our sponsor - InfluxDB
www.influxdata.com | 1 Jun 2024
Index
What are some of the best open-source HacktoberFest projects in Julia? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | julia | 44,707 |
| 2 | Gadfly.jl | 1,894 |
| 3 | Plots.jl | 1,804 |
| 4 | DataFrames.jl | 1,704 |
| 5 | CUDA.jl | 1,144 |
| 6 | Javis.jl | 816 |
| 7 | Agents.jl | 697 |
| 8 | Optimization.jl | 679 |
| 9 | OrdinaryDiffEq.jl | 511 |
| 10 | DataFramesMeta.jl | 475 |
| 11 | Graphs.jl | 443 |
| 12 | ChainRules.jl | 414 |
| 13 | BinaryBuilder.jl | 381 |
| 14 | www.julialang.org | 351 |
| 15 | SciMLSensitivity.jl | 316 |
| 16 | DiffEqBase.jl | 300 |
| 17 | HypothesisTests.jl | 289 |
| 18 | DaemonMode.jl | 269 |
| 19 | StochasticDiffEq.jl | 237 |
| 20 | oneAPI.jl | 177 |
| 21 | GPUCompiler.jl | 148 |
| 22 | KittyTerminalImages.jl | 89 |
| 23 | AlphaVantage.jl | 83 |
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