Year: 2026
Type: preprint
Abstract: The transition of Earth-system models to exascale is often hindered by rigid, monolithic Fortran codebases and maintenance-heavy compiler directives. While high-level DSLs offer a solution, they frequently fail due to cumbersome integration. We present the integration of a Python-based ICON dynamical core into the original Fortran simulation code. Leveraging the GT4Py DSL and the Data-Centric (DaCe) optimization framework, we demonstrate that high-level Python can be seamlessly integrated into legacy infrastructure without performance loss. Our results challenge the assumption that Python orchestration introduces prohibitive HPC overhead. In production-grade global simulations, our Python dynamical core achieves a 20--30\% performance improvement over the highly-optimized Fortran+OpenACC implementation, with a 10\% improvement on the total time for a coupled setup. Driven by advanced data-flow optimizations and automated kernel fusion, this approach replaces hardware-entangled directives by generating optimized device code from a single, portable Python source. This work proves that Python can provide a sustainable, efficient, and hardware-agnostic future for global climate modeling.
Source: arXiv (Cornell University)
Authors: Bianco, Mauro, Ehrengruber, Till, Paredes, Enrique González, Jocksch, Andreas, Kotsalos, Christos, Magkanaris, Ioannis, Müller, Philip, Paone, Edoardo, Simberg, Mikael, Vogt, Hannes, Canton, Jacopo, Chen, Yilu, Dipankar, Anurag, Farabullini, Nicoletta, Jähn, Michael, Leclair, Matthieu, Rui, Ong Chia, Beech, Nathan, Gruber, Nicolas, Müller, Christoph, Hupp, Daniel, Lapillonne, Xavier
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: green
OpenAlex ID: W7204134865
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