MPM-Julia
CUDA.jl


MPM-Julia | CUDA.jl | |
---|---|---|
1 | 15 | |
32 | 1,245 | |
- | 1.8% | |
0.0 | 9.4 | |
almost 5 years ago | 3 days ago | |
Julia | Julia | |
- | 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.
MPM-Julia
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Suggest me a Good library for scientific computing in Julia with good support for multi-core CPUs and GPUs.
Sounds like an interesting project, please post how you go! I found this with library, may be useful https://github.com/vinhphunguyen/MPM-Julia
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/
- Yann Lecun: ML would have advanced if other lang had been adopted versus Python
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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?
DifferentialEquations.jl - Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
cupynumeric - An Aspiring Drop-In Replacement for NumPy at Scale
GPUCompiler.jl - Reusable compiler infrastructure for Julia GPU backends.
CudaPy - CudaPy is a runtime library that lets Python programmers access NVIDIA's CUDA parallel computation API.
awesome-quant - A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
Tullio.jl - ⅀
cudf - cuDF - GPU DataFrame Library
LoopVectorization.jl - Macro(s) for vectorizing loops.
Rust-CUDA - Ecosystem of libraries and tools for writing and executing fast GPU code fully in Rust.
numba - NumPy aware dynamic Python compiler using LLVM
FiniteDiff.jl - Fast non-allocating calculations of gradients, Jacobians, and Hessians with sparsity support

