Minisymposium Presentation

Established numerical weather prediction (NWP) and climate modeling codes, such as ECMWF's Integrated Forecasting System (IFS), have been developed over decades and comprise a large monolithic code base. Over the course of their lifetime, compute architectures have evolved from vector computers to distributed memory multi-processors and hybrid accelerated supercomputers. Not least thanks to the systems procured by the EuroHPC JU, NWP codes are run on a larger variety of systems and have to target diverse hardware architectures today. Meanwhile, the objective of performance portability using a single programming model remains elusive and accommodating multiple bespoke and sometimes conflicting optimisations for specific hardware architectures becomes increasingly unsustainable.

We present Loki, an open-source Python package purpose-built for the IFS that offers source-to-source translation capabilities for Fortran code. It provides experts with a freely-programmable API and inter-procedural analysis features to encode custom transformations that are applied programmatically across large source trees. Loki is a cornerstone of the GPU adaptation strategy for ECMWF's IFS and has been deployed successfully to adapt multiple components of the forecast model for GPU execution. Recently, other users in the weather and climate community have started evaluating Loki for their own code adaptation work.