this post was submitted on 22 Nov 2023
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Machine Learning
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As someone who writes CUDA code professionally, these are my two cents on the matter: The reported speed enhancements, particularly the claimed 117.83x speedup, might be somewhat misleading. Consider, for example, the comparison of CUDA speedups. The authors contrast their CUDA Fast Feed Forward (CUDA fff) implementation with their own highly unoptimized version of a CUDA Fast Forward (CUDA ff).
In an effort to ensure a fair comparison, they maintained the same code structure for both CUDA fff and CUDA ff. However, this approach resulted in the CUDA ff not utilizing any shared memory and caused significant memory divergence due to the use of threadIdx.x for indexing the outer dimensions of matrices.
If you compare the best implementation of FFF on CUDA to the best implementation of FF on CUDA, then the speed-up they got is 3.15x:
See Page 5 Further comparisons: "On GPU, the PyTorch BMM implementation of FFF delivers a 3.15x speedup over the fastest (Native fused) implementation of FF"
The 40x that u/lexected mentioned seems to apply only when comparing to an apparently much slower FF version.
It's a pretty cool paper regardless, as far as I can tell from skimming it. But it could benefit from stating more clearly what has been achieved.