naga VS kompute

That wouldn't have been all that different from WGSL though, the most important thing is that whatever WebGPU uses for its shaders can be translated to and from SPRIV (and WGSL does that too (e.g. via https://dawn.googlesource.com/tint and https://github.com/gfx-rs/naga).

Hey all, wanted to put this link in here, where I'm proposing changing the API for errors in naga, so Naga can take ownership of error presentation and actually Make Shader Compilation Messages Comfy™: https://github.com/gfx-rs/naga/issues/2317

EDIT: There is also naga but it does not take HLSL as input: https://github.com/gfx-rs/naga but you can use DirectXShaderCompiler to compile to SpirV, then use naga to compile to Metal.

And it seems that naga https://github.com/gfx-rs/naga Already has a working front/backend for wgsl.

This is basically Ray Tracing support in Blade. So far, only ray queries are supported. Unlike prior work on ray tracing in Rust, this is original due to all shader code being WGSL, see the Naga PR.

There's a compiler that can translate from WGSL to SPIR-V called naga. Having such a compiler is essential, since WebGPU is planned to use WGSL and browsers are expected to implement rendering via Vulkan (and probably Metal and DX12).

Not sure about on the CPU, but naga is a shading language transpiler you can write custom front/backends for.

The spec docs are actually pretty useful https://www.w3.org/TR/WGSL/ besides that I was using naga's tests for reference https://github.com/gfx-rs/naga/tree/master/tests

For cross-platform support look at WebGPU and Vulkan (e.g,: [0] [1]. Essentially, you would need to write the func in WGSL or GLSL, HLSL or MSL. Each of these can be cross-compiled to SPIR-V (what Vulkan needs) with cross-compilers such as spirv-cross and naga.

The two I know of are IREE and Kompute[1]. I'm not sure how much momentum the latter has, I don't see it referenced much. There's also a growing body of work that uses Vulkan indirectly through WebGPU. This is currently lagging in performance due to lack of subgroups and cooperative matrix mult, but I see that gap closing. There I think wonnx[2] has the most momentum, but I am aware of other efforts.

[1]: https://kompute.cc/

[2]: https://github.com/webonnx/wonnx

Great to see the positive momentum of this framework! Best wishes and upvotes from the Vulkan Kompute team :)

To a first approximation, Kompute[1] is that. It doesn't seem to be catching on, I'm seeing more buzz around WebGPU solutions, including wonnx[2] and more hand-rolled approaches, and IREE[3], the latter of which has a Vulkan back-end.

[1]: https://kompute.cc/

[2]: https://github.com/webonnx/wonnx

[3]: https://github.com/openxla/iree

I look in an example and see similar instructions, stating that the build should be quite simple. But again, it doesn't work. It generates a bunch of folders with Visual Studio stuff, but no executables, no libraries, or anything like that.

I can't figure out how, and there are no tutorials. According to https://kompute.cc/overview/build-system.html I should simply run "cmake -Bbuild". But this doesn't output what I need, and when I look in the Makefile I get the sense that this is more an example Makefile... but then that contradicts the above tutorial.

Kompute is in my opinion good example to take inspiration for abstractions.

Currently, there's a few Vulkan compute frameworks floating around (like Kompute). I would work with those. Kompute simplifies a lot of the biolerplate and seems like you could benefit from using it.

Try Kompute, a project from the Linux foundation. It is quite simple to use, and does not require deep knowledge of graphics API. It’s a bit painful to setup, but it kinda works well (and I have a project going on on it)

This so much.

This makes alot of sense considering the Linux Foundation is also in charge of Kompute which is likely to be the basis of vendor agnostic GPGPU, and thus the basis of vendor agnostic GPU-based machine learning.