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In a shameless plug, I want to note that running these sorts of workloads on CPU using Pytorch got much faster (some results on a benchmark from this post’s aut
by bertr4nd 5y ago
In a shameless plug, I want to note that running these sorts of workloads on CPU using Pytorch got much faster (some results on a benchmark from this post’s author’s suite in [0]) in the most recent torch release thanks to the addition of a JIT compiler. Obviously there’s much to recommend Jax (the XLA compiler is quite excellent), but it’s nice to have some choice in the space.
[0] https://www.linkedin.com/feed/update/activity:6864010621457977344 https://www.linkedin.com/feed/update/activity:68640106214579...
- dionhaefner 5y agoTrue, but unfortunately Pytorch is not quite there yet when it comes to more complex benchmarks: https://github.com/dionhaefner/pyhpc-benchmarks#example-results https://github.com/dionhaefner/pyhpc-benchmarks#example-resu... JAX really is the only library that comes close to low-level code on CPU, almost always (that I've tried).
- bertr4nd 5y agoInteresting, I thought pytorch was a bit more competitive on those other benchmarks (but admittedly it’s been a while since I looked). Slicing shouldn’t be a fundamental problem, but perhaps there are some important details that have been overlooked. Thanks for pointing it out!