tweetnacl | CUDA.jl | |
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2 | 15 | |
21 | 1,137 | |
- | 1.5% | |
0.0 | 9.5 | |
about 7 years ago | 4 days ago | |
C | Julia | |
MIT License | 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.
tweetnacl
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Why Lisp? (2015)
Because that product was an embedded system running on a very small SoC. It only had 1MB of flash and 192k of SRAM. It's theoretically possible to run CL on a system that small -- Coral Common Lisp ran on a Mac Plus with 1MB of RAM back in the 1980s -- but nothing off-the-shelf will do that today.
(I did, however, put a little Scheme interpreter on it as an easter egg :-)
I do have some CL code that supports the crypto project. The back-end for this:
https://stage.sc4.us/sc4/sc4tk.html
is written in CL (though all the actual encryption is done client-side in Javascript). I also have some prototype crypto code that I don't really use for anything, including this double-ratchet implementation:
https://github.com/rongarret/tweetnacl/blob/master/ratchet.l...
and some elliptic curve code:
http://www.flownet.com/ron/lisp/djbec.lisp
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Teaching Compilers Backward
Of course. There are many. Any binary format. Any ASN.1 format. DEF and LEF for hardware descriptions. The output of mysqldump.
Here's another example:
https://github.com/rongarret/tweetnacl/blob/master/ratchet.l...
starting at line 82. (That's one that I designed.)
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.
What are some alternatives?
LoopVectorization.jl - Macro(s) for vectorizing loops.
cl4py - Common Lisp for Python
cunumeric - An Aspiring Drop-In Replacement for NumPy at Scale
lang
awesome-quant - A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
awesome-compilers - :sunglasses: Curated list of awesome resources on Compilers, Interpreters and Runtimes
cudf - cuDF - GPU DataFrame Library
hissp - It's Python with a Lissp.
Tullio.jl - ⅀
aws-api - AWS, data driven
GPUCompiler.jl - Reusable compiler infrastructure for Julia GPU backends.