Relm4 VS Rust-CUDA

There is Relm (https://relm4.org/). Which is in Rust is just GTK (through it's Rust bindings) with a nice reactivity layer on top.

I’d recommend you checking out https://relm4.org if you know a little Rust. Relm4 is currently the easiest and most intuitive way to write GTK

Relm4 might make your migration easier. It has factories which make it easy to create lists (both sync and async) and recently added TypedListView as another useful abstraction over pure gtk-rs. There is a lot of potential in Rust + GTK, you just have to find idiomatic abstractions.

Did you try Relm? It's an implementation is the elm architecture (think react) on top of Gtk. Given your experience with Gtk it might suit you really well, as you can even take only parts of it and continue using bare Gtk for the rest. Give it a try!

What about relm4? Is it generally feature complete? Can one use relm4 for most things, and drop to gtk4 for the oddball task that is not supported?

https://relm4.org/

Using this example: https://github.com/Relm4/Relm4/blob/main/examples/simple.rs

What advantage would this Rust crate have over Relm4? It seems pretty similar to me, save for the property based syntax that Forte has, instead of the set_* ones used on Relm4.

If you like Rust I recommend Relm4, one of the best experiences I’ve had with a framework tbh, be sure to learn gtk-rs first.

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.