In this context AdaptiveCpp should also be mentioned. Started as a SYCL implementation, but recently-ish added a compiler for compiling a CUDA dialect to GPUs and CPUs from basically all vendors
There's nothing wrong to run CUDA on non-Nvidia hardware. CUDA has an interface that is reasonably well-designed, well-documented/reverse-engineered, and battle-tested for decades. What we need is not to invent another interface just under the name of 'open standard', but to implement the same interface. ROCm is exactly doing this, and so are other hardware SDKs such as MooreThread and Alibaba T-Head.
every CUDA alternative follows the same arc: bold launch, works for 3 operations, then a Discord server where the last message is 'any updates?' from 2024
Including stuff like Fortran, Haskell, Java, .NET via PTX, Python JIT, IDE tooling integration with major IDEs, graphical GPU debugging and profiling, libraries and co?
Already in 2020,
https://developer.nvidia.com/blog/cuda-refresher-the-gpu-com...
Then I guess all the best.
If you were to guess, when do you think your Nsight Compute alternative might be ready with your own toolchain?
https://github.com/vosen/ZLUDA
No reason to tie yourself to Nvidia's moat.