Minisymposium Presentation

As of today, the majority of weather and climate models are implemented in Fortran and extended with a selection of compiler directives to enable execution of multicore CPUs, vector units or graphics processing units (GPUs). The consequence is that developers struggle to keep pace with a repidly evolving HPC software and hardware ecosystem and are not able to leverage efficiently current and emergy leadership class supercomputing infrastructures. Also, developer productivity in resolving problems or introducing new features is generally low.

But what if weather and climate models were implemented in Python? We present Pace, a Python-based performance portable implementation of a subset of the x-SHiELD model of NOAA/GFDL. We demonstrate scaling Pace to 4000 GPUs on the Piz Daint supercomputer at CSCS and achieving a 3.9x speedup over the Fortran reference code on CPUs. Pace is a proof of concept that high-level languages like Python can achieve performance portability in atmopsheric models and provide a more productive development environment. Additionally, Pace enables entirely novel use cases and workflows such as easy integration of machine learning components, taking full advantage of the Python ecosystem. We finish with an outlook for the ICON model.