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> but all the alternatives require significant redesign in languages and tools people are unfamiliar with and we can't afford that overhead
Where I work, we've made it a principle to stay OpenCL-compatible even while going with NVIDIA due to their better-performing GPUs. I even go as far as writing kernels that can be compiled as either CUDA C++ or OpenCL-C, with a bit of duct-tape adapter headers:
https://github.com/eyalroz/gpu-kernel-runner/blob/main/kerne...
https://github.com/eyalroz/gpu-kernel-runner/blob/main/kerne...
of course, if you're working with higher-level frameworks then it's more difficult, and you depend on whether or not they provided different backends. So, no thrust for AMD GPUs, for example, but pytorch and TensorFlow do let you use them.
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eyalroz/gpu-kernel-runner is an open source project licensed under BSD 3-clause "New" or "Revised" License which is an OSI approved license.
The primary programming language of gpu-kernel-runner is C++.
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