This is a suite of benchmarks to test the sequential CPU and GPU performance of various computational backends with Python frontends.
Specifically, we want to test which high-performance backend is best for geophysical (finite-difference based) simulations.
The scientific Python ecosystem is thriving, but high-performance computing in Python isn't really a thing yet. We try to change this with our pure Python ocean simulator Veros, but which backend should we use for computations?
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. We wanted to see whether we can profit from those advances, by (ab-)using these libraries for geophysical modelling.
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