Are you certain that the story is as simple as "CUDA performs better"? It's common folklore, but I have seen little evidence. The only situations I know of wh

Are you certain that the story is as simple as "CUDA performs better"? It's common folklore, but I have seen little evidence. The only situations I know of when CUDA performs better is when CUDA-specific features are used (if they are relevant for whatever problem is at hand). Also, CUDA libraries (like cuBLAS or cuFFT) tend to be more efficient than their OpenCL equivalent, which is likely because much more work has gone into them. I have also noted that the CUDA compiler is willing to use less accurate (but faster) floating-point instructions by default (for things like e.g. inverse square root), where you need to pass options to the OpenCL compiler for it to do the same. This will matter for some programs. In fact, I have run tens of thousands of lines of essentially equivalent CUDA and OpenCL code (automatically generated) on the same hardware, and performance was in all cases very similar[0]. If anything, CUDA was actually slower than average (but in the cases I investigated, this was down to arbitrary differences like the CUDA compiler not unrolling some loops as aggressively and such). [0]: https://futhark-lang.org/blog/2019-02-08-futhark-0.9.1-released.html#the-cuda-backend https://futhark-lang.org/blog/2019-02-08-futhark-0.9.1-relea...