> CUDA can now easily compile it to target NVIDA and AMD GPUs, as well as x86-64 CPUs
Are these solutions "legal"? I.e. is there a risk NVIDIA can crack down on tools like this from an IP standpoint if they want to at some point in the future?
Are these solutions "legal"? I.e. is there a risk NVIDIA can crack down on tools like this from an IP standpoint if they want to at some point in the future?
- NVIDIA themselves ship multiple CUDA to multi-core x86-64 compilers for different programming languages CUDA C/C++/Fortran
- AMD has been shiping hip for a while, advertising it as production ready, without any lawsuits (AFAIK). hip is also being used in production.
- LLVM and clang have had open-source CUDA (and PTX) support for years, and nvidia have not enforced any kind of patents there.
Hip is not Cuda and even remove most mention if cuda from its API. It just mimic cuda API to try to provide compatibility.
And the result is that almost nobody use it today.
According to https://www.techquila.co.in/nvidia-cloud-ai-gpu-share/, AMD is at ~1%.
ROCm and HIP [^1] or OpenCL.
OpenCL is the hated solution of GPGPU. It is low level, incomplete and definitively not with a mature ecosystem of tools like Cuda. But it is currently the only one existing if you want your program to run on something else than Nvidia GPU ( Meaning AMD, Intel, phone GPUs or FPGA accelerators).
I'm sure NVIDIA IP includes the CUDA trademark, their own compilers, SDKs and so on... but the language itself ?
Using open standards avoids this risk, though some companies are unwilling to work with Khronos, citing (not publicly detailed) legal concerns.