Next-generation geophysical modelling
<div>So far, our pure Python, primitive equation ocean model Veros has been</div><div>about 50% slower than a corresponding Fortran implementation. But recent</div><div>benchmarks show that, thanks to a thriving scientific and machine</div><div>learning library ecosystem, tremendous speed-ups on GPU, and to a lesser</div><div>degree CPU, are within reach. On GPU, we find that the same model code</div><div>can reach a 2-5 times higher energy efficiency compared to a traditional</div><div>Fortran model.</div><div>We thus propose a new generation of geophysical models. One that</div><div>combines high-level abstractions and user friendliness on one hand, and</div><div>that leverages modern developments in high-performance computing on the</div><div>other hand.</div><div>We discuss what there is to gain from building models in high-level</div><div>programming languages, what we have achieved, and what the future holds</div><div>for us and the modelling community.</div>