GLSL VS Rust-CUDA

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You have access to physical (u64) global memory pointers through buffer_reference in GLSL, note you should be using buffer_reference2

On April 27, 2023 the Vulkan® Ray Tracing TSG released the VK_KHR_ray_tracing_position_fetch extension, which exposes the ability to fetch vertex positions from an acceleration structure hit when tracing rays. The SPIR-V SPV_KHR_ray_tracing_position_fetch and GLSL GL_EXT_ray_tracing_position_fetch extensions have also been released to provide SPIR-V and GLSL support for this functionality.

You can also have general extensions that are only supported on some hardware, for instance NVIDIA supports all of these extensions on OpenGL that add support for Vulkan's thread subgroup operations while AMD only supports these extensions and a few others that add support for similar features to the extension that NVIDIA supports, but with way more restrictions.

GL_NV_ray_tracing is a vendor extension, but it does exist.

According to this document, one should be able to extract those values through

The reports of my death are greatly exaggerated

https://github.com/KhronosGroup/GLSL/blob/master/extensions/...

I don't think there's away. For Vulkan, there's GL_EXT_nonuniform_qualifier which allows you to index with gl_InstanceIndex but AFAIK it's not available for OpenGL.

AMD hardware works a little differently, and for that you have to write a waterfall loop with subgroup instructions to get the correct behavior (since GL_EXT_nonuniform_qualifier doesn't exist in OpenGL GLSL). The loop might look like this (you would use it the same way as NonUniformEXT in Vulkan GLSL).

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.