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Top 23 Julia HacktoberFest Projects
The Julia Programming LanguageProject mention: Dart 3.3 | news.ycombinator.com | 2024-02-15
3. dispatch on all the arguments
the first solution is clean, but people really like dispatch.
the second makes calling functions in the function call syntax weird, because the first argument is privileged semantically but not syntactically.
the third makes calling functions in the method call syntax weird because the first argument is privileged syntactically but not semantically.
the closest things to this i can think of off the top of my head in remotely popular programming languages are: nim, lisp dialects, and julia.
nim navigates the dispatch conundrum by providing different ways to define free functions for different dispatch-ness. the tutorial gives a good overview: https://nim-lang.org/docs/tut2.html
lisps of course lack UFCS.
see here for a discussion on the lack of UFCS in julia: https://github.com/JuliaLang/julia/issues/31779
so to sum up the answer to the original question: because it's only obvious how to make it nice and tidy like you're wanting if you sacrifice function dispatch, which is ubiquitous for good reason!
Crafty statistical graphics for Julia.
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Powerful convenience for Julia visualizations and data analysis
In-memory tabular data in JuliaProject mention: Julia's latency: Past, present and future | news.ycombinator.com | 2023-04-01
I don't think we've seen the final state of it though. v1.9 really gives people the proper tools for solving latency problems. Before, invalidations hurt a little, but it was also kind of a wash because LLVM code didn't precompile, so you could spend time setting up a Snoopprecompile and fix some invalidations and end up LLVM bound saving 1 second out of 20. But with package images it's almost always better to fix precompilation. The only thing hampering time much now is the `using` time went up, but as mentioned in the Reddit post there's a lot of ideas for what to do there. The other thing is package extensions, which cut down the amount of code to load. There's tons of PRs floating around the ecosystem turning things into extensions, and thus cutting down the overall code that is actually ran and loaded.
This means that in a few months, people will start to see some major tangible benefits from following the compilation improvement practices laid out here and https://sciml.ai/news/2022/09/21/compile_time/. I think then you'll have a lot more people start to take all of these new tools seriously and it will be standard to incorporate them into packages. Right now they are still kind of niche things for packages with known TTFX problems, but I think come v1.9 you'll see every major package use all of these methods.
> Different packages are affected differently
I think this is one of the pieces that's effected by this. I don't think "Julia has become optimised for running Plots.jl" is quite correct. Julia's compilation and runtime is much more optimized for well-inferred code, which Plots.jl is not. However, the compiler developers have been using Plots.jl as a test case for all of these new tools, and therefore its SnoopPrecompile and invalidations have gotten some dramatic improvements because those required ecosystem changes I mentioned are being done by the compiler team for this specific package. Plots.jl had things like precompilation snooping way back before there was even a package for it, the earliest I know of was around v1.0. Meanwhile, DataFrames.jl only setup its precompilation snooping 7 months ago (https://github.com/JuliaData/DataFrames.jl/pull/3182), which I would presume was just in time for the v1.8 mark on your plot and is one of the big reasons for having a sudden drop (which continues into v1.9 because of package images).
What I mean to say then is that, I think all packages will get the improvements we've seen from Plots.jl, but package authors will need to update their packages in order for that to happen. Some packages have already done this, many have not.
CUDA programming in Julia.Project mention: Ask HN: Best way to learn GPU programming? | news.ycombinator.com | 2024-01-01
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.
I've written CUDA kernels and I knew nothing about it going in.
Julia Animations and Visualizations
Agent-based modeling framework in Julia
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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.Project mention: SciPy: Interested in adopting PRIMA, but little appetite for more Fortran code | news.ycombinator.com | 2023-05-18
Interesting response. I develop the Julia SciML organization https://sciml.ai/ and we'd be more than happy to work with you to get wrappers for PRIMA into Optimization.jl's general interface (https://docs.sciml.ai/Optimization/stable/). Please get in touch and we can figure out how to set this all up. I personally would be curious to try this out and do some benchmarks against nlopt methods.
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)Project mention: Modern Numerical Solving methods | /r/DifferentialEquations | 2023-07-06
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.
Metaprogramming tools for DataFramesProject mention: Pandas vs. Julia – cheat sheet and comparison | news.ycombinator.com | 2023-05-17
An optimized graphs package for the Julia programming language
forward and reverse mode automatic differentiation primitives for Julia Base + StdLibs
Binary Dependency Builder for Julia
Julia Project websiteProject mention: GPU vendor-agnostic fluid dynamics solver in Julia | news.ycombinator.com | 2023-05-08
The release was just cut 9 hours ago, as shown on the releases part of the Github page (https://github.com/JuliaLang/julia/releases/tag/v1.9.0). That then starts the jobs for the creation and deployment of the final binaries, and when that's done the Julialang.org website gets updated to state it's the release, and when that's done the blog post for the new release goes out. You can even follow the last step of the process here (https://github.com/JuliaLang/www.julialang.org/pull/1875), since it all occurs on the open source organization.
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.
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
Hypothesis tests for Julia
Client-Daemon workflow to run faster scripts in JuliaProject mention: Potential of the Julia programming language for high energy physics computing | news.ycombinator.com | 2023-12-04
Thats 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.
Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystemProject mention: Writing unit tests in scientific computing | /r/Julia | 2023-03-21
For stochastic processes you have to work a little bit more. However maybe the StochasticDiffEq.jl package can give some guiding there https://github.com/SciML/StochasticDiffEq.jl/tree/master/test
Reusable compiler infrastructure for Julia GPU backends.
A package that allows Julia to display images in the kitty terminal editor
A Julia wrapper for the Alpha Vantage API.
An open-source framework written in Julia for global integrated assessment models.
The modern API for authentication & user identity. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
Julia HacktoberFest related posts
2 projects | news.ycombinator.com | 15 Feb 2024
Ask HN: Best way to learn GPU programming?
2 projects | news.ycombinator.com | 1 Jan 2024
Potential of the Julia programming language for high energy physics computing
10 projects | news.ycombinator.com | 4 Dec 2023
What Apple hardware do I need for CUDA-based deep learning tasks?
3 projects | /r/macbook | 27 May 2023
Julia 1.9.0 lives up to its promise
3 projects | news.ycombinator.com | 14 May 2023
jlrs v0.18: export types and functions written in Rust to Julia, improved version and platform support, and more!
3 projects | /r/rust | 13 May 2023
Julia 1.9 Highlights
9 projects | news.ycombinator.com | 10 May 2023
A note from our sponsor - WorkOS
workos.com | 28 Feb 2024
What are some of the best open-source HacktoberFest projects in Julia? This list will help you: