CUDA.jl
babashka
CUDA.jl | babashka | |
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15 | 112 | |
1,133 | 3,818 | |
1.1% | 0.9% | |
9.5 | 9.2 | |
7 days ago | 6 days ago | |
Julia | Clojure | |
GNU General Public License v3.0 or later | Eclipse Public License 1.0 |
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.
CUDA.jl
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Ask HN: Best way to learn GPU programming?
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.
- What's your main programming language?
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How is Julia Performance with GPUs (for LLMs)?
See https://juliagpu.org/
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Yann Lecun: ML would have advanced if other lang had been adopted versus Python
If you look at Julia open source projects you'll see that the projects tend to have a lot more contributors than the Python counterparts, even over smaller time periods. A package for defining statistical distributions has had 202 contributors (https://github.com/JuliaStats/Distributions.jl), etc. Julia Base even has had over 1,300 contributors (https://github.com/JuliaLang/julia) which is quite a lot for a core language, and that's mostly because the majority of the core is in Julia itself.
This is one of the things that was noted quite a bit at this SIAM CSE conference, that Julia development tends to have a lot more code reuse than other ecosystems like Python. For example, the various machine learning libraries like Flux.jl and Lux.jl share a lot of layer intrinsics in NNlib.jl (https://github.com/FluxML/NNlib.jl), the same GPU libraries (https://github.com/JuliaGPU/CUDA.jl), the same automatic differentiation library (https://github.com/FluxML/Zygote.jl), and of course the same JIT compiler (Julia itself). These two libraries are far enough apart that people say "Flux is to PyTorch as Lux is to JAX/flax", but while in the Python world those share almost 0 code or implementation, in the Julia world they share >90% of the core internals but have different higher levels APIs.
If one hasn't participated in this space it's a bit hard to fathom how much code reuse goes on and how that is influenced by the design of multiple dispatch. This is one of the reasons there is so much cohesion in the community since it doesn't matter if one person is an ecologist and the other is a financial engineer, you may both be contributing to the same library like Distances.jl just adding a distance function which is then used in thousands of places. With the Python ecosystem you tend to have a lot more "megapackages", PyTorch, SciPy, etc. where the barrier to entry is generally a lot higher (and sometimes requires handling the build systems, fun times). But in the Julia ecosystem you have a lot of core development happening in "small" but central libraries, like Distances.jl or Distributions.jl, which are simple enough for an undergrad to get productive in a week but is then used everywhere (Distributions.jl for example is used in every statistics package, and definitions of prior distributions for Turing.jl's probabilistic programming language, etc.).
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C++ is making me depressed / CUDA question
If you just want to do some numerical code that requires linear algebra and GPU, your best bet would be Julia or Python+JAX.
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Parallélisation distribuée presque triviale d’applications GPU et CPU basées sur des Stencils avec…
GitHub - JuliaGPU/CUDA.jl: CUDA programming in Julia.
- Why Fortran is easy to learn
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Generic GPU Kernels
Should have (2017) in the title.
Indeed cool to program julia directly on the GPU and Julia on GPU and this has further evolved since then, see https://juliagpu.org/
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Announcing The Rust CUDA Project; An ecosystem of crates and tools for writing and executing extremely fast GPU code fully in Rust
I'm excited to eventually see something like JuliaGPU with support for multiple backends.
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[Media] 100% Rust path tracer running on CPU, GPU (CUDA), and OptiX (for denoising) using one of my upcoming projects. There is no C/C++ code at all, the program shares a single rust crate for the core raytracer and uses rust for the viewer and renderer.
That's really cool! Have you looked at CUDA.jl for the Julia language? Maybe you could take some ideas from there. I am pretty sure it does the same thing you do here, and they support any arbitrary code with the limitations that you cannot allocate memory, I/O is disallowed, and badly-typed code(dynamic) will not compile.
babashka
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A Tour of Lisps
It also gives you access to Babashka if you want Clojure for other use-cases where start-up time is an issue
https://babashka.org/
- Babashka: Fast native Clojure scripting runtime
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What's the value proposition of meta circular interpreters?
I've tried researching this myself and can't find too much. There's this project metaes which is an mci for JS, and there's the SCI module of the Clojure babashka project, but that's about it. I also saw Triska's video on mci but it was pretty theoretical.
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Adding Dependencies on Clojure Project the Node Way: A Small Intro to neil CLI
Created by the same guy who created babashka which is a way to write bash scripts, node scripts, and even apple scripts using Clojure. A very proficient and influential developer in the Clojure community. This is how borkduke's neil helps us:
- Babashka
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Pure Bash Bible
Not what you asked for but there is Babashka for scripting in Clojure.
https://github.com/babashka/babashka
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Critique of Lazy Sequences in Clojure
Clojure's lazy sequences by default are wonderful ergonomically, but it provides many ways to use strict evaluation if you want to. They aren't really a hassle either. I've been doing Clojure for the last few years and have a few grievances, but overall it's the most coherent, well thought out language I've used and I can't recommend it enough.
There is the issue of startup time with the JVM, but you can also do AOT compilation now so that really isn't a problem. Here are some other cool projects to look at if you're interested:
Malli: https://github.com/metosin/malli
Babashka: https://github.com/babashka/babashka
Clerk: https://github.com/nextjournal/clerk
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Sharpscript: Lisp for Scripting
Being a Clojure addict, I guess I have to leave the obligatory link to Babashka too then: https://github.com/babashka/babashka (Native, fast starting Clojure interpreter for scripting)
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Rash – The Reckless Racket Shell
which is now on hiatus. babashka: https://babashka.org
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Are there any languages (that are in common use in companies) and higher-level that give you the same feeling of simplicity and standardization as C?
I've enjoyed babashka for scripting; which is close enough to clojure to allow using some/many libraries; but (probably) not for embedding.
What are some alternatives?
LoopVectorization.jl - Macro(s) for vectorizing loops.
janet - A dynamic language and bytecode vm
cunumeric - An Aspiring Drop-In Replacement for NumPy at Scale
malli - High-performance data-driven data specification library for Clojure/Script.
awesome-quant - A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
joker - Small Clojure interpreter, linter and formatter.
cudf - cuDF - GPU DataFrame Library
nbb - Scripting in Clojure on Node.js using SCI
Tullio.jl - ⅀
clojure-lsp - Clojure & ClojureScript Language Server (LSP) implementation
GPUCompiler.jl - Reusable compiler infrastructure for Julia GPU backends.
racket - The Racket repository