rust-gpu VS Rust-CUDA

Sounds cool, but this requires yet another language to learn[0]. As someone who only has limited knowledge in this space, could someone tell me how comparable is the compute functionality of rust-gpu[1], where I can just write rust?

[0] https://github.com/Hugobros3/shady#language-syntax

[1] https://github.com/EmbarkStudios/rust-gpu

I don't do anything related to data science, but I feel like doing it in Rust would be nice.

You get operator overloading, so you can have ergonomic matrix operations that are typed also. Processing data on the CPU is fast, and crates like https://github.com/EmbarkStudios/rust-gpu make it very ergonomic to leverage the GPU.

I like this library for creating typed coordinate spaces for graphics programming (https://github.com/servo/euclid), I imagine something similar could be done to create refined types for matrices so you don't do matrix multiplication matrices of invalid sizes

Do you mean rust-gpu?

And https://github.com/EmbarkStudios/rust-gpu/tree/main/examples with the wgpu runner (here it runs the compute shader)

I don't know how major they are considered, but Embark Studios is doing quite a bit of Rust in the open source space, most notably (IMO) rust-gpu and kajiya

The examples in the rust-gpu repository are a good place to start

There's another project that's similar that's being used by an actual game company: https://github.com/EmbarkStudios/rust-gpu

They see specific advantages here that would outweigh that negative. It's not my space (I play games, but know next to nothing about graphics programming), but there's at least one argument in the other direction.

Could this approach work for compute shaders (GPGPU) as well? So far, I think https://github.com/EmbarkStudios/rust-gpu is the state of the art in that area, but it adds a specific Rust compiler backend for generating SPIR-V rather than leaving that up to the driver. That seems more complicated than it needs to be... but maybe it has advantages too? Thoughts?

https://github.com/embarkstudios/rust-gpu Allows you to create shaders (kernals) in Rust.

Also Embark Studios (formers DICE people) is doing a lot of work with Rust, all open source like Rust GPU https://github.com/EmbarkStudios/rust-gpu

Source code [here](https://github.com/ihawn/RTracer) if anyone is interested in taking a look or giving feedback. As a side question, does anyone have any general advise on getting GPU compute working with rust? I tried [this project](https://github.com/Rust-GPU/Rust-CUDA) but had a bunch of issues (And it doesn't look like an active repo anyways)

You have this https://github.com/Rust-GPU/Rust-CUDA

What I'm trying to do is check out https://github.com/Rust-GPU/Rust-CUDA for a class project.

Be warned, NON_BLOCKING streams do not fully synchronize with sync host to device copies. They are not guaranteed to actually finish by the time they return. Meaning its possible to initiate a copy, then initiate a kernel launch, and have the copy be unfinished by the time the kernel is launched. This caused so many confusing bugs that i personally decided to stop using NON_BLOCKING altogether in rust-cuda. https://github.com/Rust-GPU/Rust-CUDA/issues/15

- Cuda is not doing by FFI linking, instead is compiling CUDA code natively in Rust https://github.com/Rust-GPU/Rust-CUDA and even if it not complete as the C++ SDK is more than a toy

Given your criteria, you might want to consider (modern) C++.

* Fast - in many cases faster than Rust, although the difference is inconsequential relative to Python-to-Rust improvement I guess.

* _Really_ utilize CUDA, OpenCL, Vulcan etc. Specifically, Rust GPU is limited in its supported features, see: https://github.com/Rust-GPU/Rust-CUDA/blob/master/guide/src/... ...

* Host-side use of CUDA is at least as nice, and probably nicer, than what you'll get with Rust. That is, provided you use my own Modern C++ wrappers for the CUDA APIs: https://github.com/eyalroz/cuda-api-wrappers/ :-) ... sorry for the shameless self-plug.

* ... which brings me to another point: Richer offering of libraries for various needs than Rust, for you to possibly utilize.

* Easier to share than Rust. A target system is less likely to have an appropriate version of Rust and the surrounding ecosystem.

There are downsides, of course, but I was just applying your criteria.

Technically, yes. There are crates for OpenCL and CUDA, although official ROCm support does not exist yet.