A team of software engineers at Facebook, led by Software Engineer Bertrand Maher, recently released a JIT compiler for CPUs based on LLVM, called NNC, for “Neural Network Compiler.” The result was derived from the pyhpc-benchmark suite.
The benchmark itself is a wonderland for an ML compiler, with lots of opportunities for loop fusion meaning you can get really astonishing speedups. While the original benchmark forces single-thread execution (and NNC does well, yielding 23x over NumPy), it really shines when you lift the threading cap. On a 96-core development box, it saw 150x over PyTorch 1.9, and 300x over NumPy.
Tremendous amounts of time and resources go into the development of Python frontends to high-performance backends, but those are usually tailored towards deep learning. The development team wanted to see whether it can profit from those advances, by using these libraries for geophysical modelling, using a pure Python ocean simulator Veros.
By assembling benchmark results, the team learned the following: (one’s mileage may vary)
Veros is the versatile ocean simulator which aims to be a powerful tool that makes high-performance ocean modeling approachable and fun. Veros supports a NumPy backend for small-scale problems and a high-performance JAX backend with CPU and GPU support. It is fully parallelized via MPI and supports distributed execution. Veros is currently being developed at Niels Bohr Institute, Copenhagen University..
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