vulkan-guide VS kompute

I totally agree, and so do the people working on it as well as some of the volunteers who write tutorials.

There's an ongoing effort to create beginner friendly introductory material which was discussed in the recent Vulkanised conference. And an effort to make a better documentation site that's easier to browse than the specification.

On the volunteer front, there's a Vulkan 1.3 -based introductory tutorial (work in progress) over at https://vkguide.dev/

I think there should be a Vulkan tutorial that doesn't start with the boring stuff of initialization and window creation. It's stuff that you write once and forget about, and nothing particularly interesting happens in it.

Looking at my hobby project, excluding the boring stuff (which is reusable), a "hello compute" example is around 100 LOC and a "hello triangle" around 120 LOC. GLSL shader sources included.

Maybe someday I'll get around to writing a "learn Vulkan the hard way" blog post with examples.

Also, use https://vkguide.dev/ rather than vulkan-tutorial.

Once you're beyond the "introductory" phase, resources become more specialized based on what you'd like to learn -- there are Vulkan tutorials like https://vkguide.dev/ which will teach you the API and also give a bit more insight in how modern GPU hardware is structured, there are books like the "GPU Zen" series that do deep-dives on specific techniques, and there are tons of recorded GDC and SIGGRAPH talks on interesting new techniques. :)

Personally, I'm a hands on learner who actually wants to use this stuff in my career, so I'd recommend these tutorials: https://learnopengl.com/ https://vulkan-tutorial.com/Overview https://vkguide.dev/

vkguide teaches some good practices regarding code/renderer structure, but I'm afraid it doesn't go as deep as you'd like. It's certainly deeper than most other tutorials, though.

https://vkguide.dev/ This is my favorite.

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.