There are some great replies to my comment - my original comment was too reductive. However, I still think that entrenching CUDA as the de-facto language for heterogeneous computing is a mistake. We need an open ecosystem for AI and HPC, where vendors compete on producing the best hardware.
The problem with open standards is that someone has to write them.
And that someone usually isn't a manufacturer, lest the committee be accused of bias.
Consequently, you get (a) outdated features that SotA has already moved beyond, (b) designed in a way that doesn't correspond to actual practice, and (c) that are overly generalized.
There are some notable exceptions (e.g. IETF), but the general rule has been that open specs please no one, slowly.
IMHO, FRAND and liberal cross-licensing produce better results.
Vulkan already has some standard compute functionality. Not sure if it's low level enough to be able to e.g. recompile and run CUDA kernels, but I think if people were looking for a vendor-neutral standard to build GPGPU compute features on top of, I mean, that seems to be the obvious modern choice.
There is already a work-in-progress implementation of HIP on top of OpenCL https://github.com/CHIP-SPV/chipStar and the Mesa RustiCL folks are quite interested in getting that to run on top of Vulkan.
(To be clear, HIP is about converting CUDA source code not running CUDA-compiled binaries but the Zluda project discussed in OP heavily relies on it.)
