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Comment by lloeki | Hacker News Reader

This is totally not my field so it may very well be the stupidest question ever: is there technically anything that prevents implementing CUDA on top of non-NVIDIA hardware?

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We are working on an OpenCL based layer that enables AMD GPUs, see these links for more info https://developer.codeplay.com/computecppce/latest/getting-s... https://github.com/tensorflow/tensorflow/issues/22

AMD is working on a tool to convert CUDA code: https://github.com/ROCm-Developer-Tools/HIP

No there isn't, Google developed a CUDA frontend for clang/llvm which targets PTX (nvidias virtual machine instruction set), in principle someone could come along and implement a different backend for this frontend targeting for example AMDs gpu instruction architecture or something different. The other ingredient missing is a reimplementation of CUDAs runtime libraries.

The last part is particularly important. Most deep learning libs rely on things like cuDNN which implement by hand basic operations very efficiently.

The ROCm initiative by AMD already "transpile" most CUDA kernels to being AMD compatible, but it seems performance is not yet comparable in real word benchmarks.

CUDA is made by nvidia

Not sure why you were down voted. CUDA is developed by NVIDIA.

CUDA is a registered trademark by NVIDIA - https://trademarks.justia.com/850/30/cuda-85030071.html

Here is the CUDA webpage

https://developer.nvidia.com/cuda-zone

Because it's a pointless answer that basically just repeats part of the question, providing absolutely no new information.

Well I think it makes a short answer NVIDIA doesn't let their proprietary CUDA technology work on non-NVIDIA devices.

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