| CUDA.jl | Tullio.jl | |
|---|---|---|
| 15 | 5 | |
| 1,266 | 622 | |
| 2.5% | 1.4% | |
| 9.5 | 4.3 | |
| 1 day ago | about 2 months ago | |
| Julia | Julia | |
| GNU General Public License v3.0 or later | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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
Posts with mentions or reviews of CUDA.jl.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-01-01.
-
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?
-
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
-
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.
Tullio.jl
Posts with mentions or reviews of Tullio.jl.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-06-16.
- NumPy 2.0.0
- A basic introduction to NumPy's einsum
- Generic GPU Kernels
-
Julia: Faster than Fortran, cleaner than Numpy
Julia ships with OpenBLAS, in some cases there are pure-Julia "blas-like" routine that can be as fast:
https://github.com/mcabbott/Tullio.jl
What are some alternatives?
When comparing CUDA.jl and Tullio.jl you can also consider the following projects:
cupynumeric - An Aspiring Drop-In Replacement for NumPy at Scale
Zygote.jl - 21st century AD
awesome-quant - A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
ForwardDiff.jl - Forward Mode Automatic Differentiation for Julia
GPUCompiler.jl - Reusable compiler infrastructure for Julia GPU backends.
TensorOperations.jl - Julia package for tensor contractions and related operations