Next-generation geophysical modelling

So far, our pure Python, primitive equation ocean model Veros has been

about 50% slower than a corresponding Fortran implementation. But recent

benchmarks show that, thanks to a thriving scientific and machine

learning library ecosystem, tremendous speed-ups on GPU, and to a lesser

degree CPU, are within reach. On GPU, we find that the same model code

can reach a 2-5 times higher energy efficiency compared to a traditional

Fortran model.

We thus propose a new generation of geophysical models. One that

combines high-level abstractions and user friendliness on one hand, and

that leverages modern developments in high-performance computing on the

other hand.

We discuss what there is to gain from building models in high-level

programming languages, what we have achieved, and what the future holds

for us and the modelling community.