[mhh__]

CUDA is for GPGPU (general purpose GPU) which includes machine learning.

Vulkan is a primarily for graphics but does have options for GPGPU too. Vulkan is however not like OpenGL in that it's fairly close to the hardware in terms of abstraction.

[my123]

Vulkan has a very atrocious developer experience by GPGPU standards.

The chance of it winning over CUDA is at zero. And that's _before_ considering its API gaps compared to modern OpenCL.

(Yes, even OpenCL is a much better compute API choice than Vulkan. Vulkan does not even have SVM)

[VHRanger]

You should think more of vulkan as an IR endpoint than the actually usable API here.

Vulkan is well supported by most GPUs because it's so low level. Performance tends to be good everywhere.

What would make vulkan succesful is having APIs that "compile" to this IR. Stuff like vulkan Kompute are good ideas in this direction.

[my123]

Vulkan is not a suitable API for even implementing Khronos's very own SYCL on top of. SYCL requires shared virtual memory capabilities that Vulkan just doesn't have.

[mhh__]

Vulkan is fairly atrocious, GPGPU or not IMO. Obviously it tries to do something intrinsically complex but I've never enjoyed working with it.

[lalaland1125]

Does CUDA have SVM either? Seems like a pretty niche feature IMHO

[my123]

Yes CUDA does, since Kepler, under the CUDA Unified Memory naming.

It's not a niche feature at all, but one that is essential to lower the barrier for developer adoption.