NVidia is already doing contributions for GPGPU programming with Graal.
https://github.com/NVIDIA/grcuda
This is why OpenCL got so little love, Khronos just kept focusing on C until it was too late.
https://github.com/NVIDIA/grcuda
This is why OpenCL got so little love, Khronos just kept focusing on C until it was too late.
I think the major problem with OpenCL was that the primary author pushing it, Apple, had no presence in either the HPC nor high-end gaming industries (and still doesn't). Keep in mind OpenCL wasn't a Khronos creation, it was Apple's. Khronos adopted it in version 1.1, but the original 1.0 release was Apple-only as part of the Snow Leopard release.
As such, nobody that can drive GPU hardware sales was pushing it. Therefore OpenCL support from AMD & Nvidia was slow and bad, because it didn't help their bottom lines. It really wasn't because it was C instead of C++. It was because the drivers were bad and it was made "generic" too early on. There was no good programming guide for it, and it's super critical for GPGPU to know things like warp width and other programming information that OpenCL just doesn't have, because it's generic-ish. This generic-ish constraint is still a problem today, and is a major reason CUDA outperforms OpenCL so badly.
By contrast Nvidia drove CUDA hard, because it sold GPUs to the HPC crowd. And HPC crowds adopted it because it actually let them go faster with real programming guides on how to achieve performance from the GPU, along with actual documentation on what you should & shouldn't expect from actual hardware.
Incidentally most OpenCL drivers still aren't properly 2.0 compliant, and the best way is to get something like computecpp, the SYSCL implementation being done by CodePlay.