Integrating a Python Dynamical core into ICON

2026-08-21

AI Summary

本文解决了地球系统模型向exascale过渡时遇到的代码维护和性能问题，通过将基于Python的ICON动力核心集成到Fortran代码中，并利用GT4Py DSL与DaCe优化框架，实现了高效能。

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.

Problem

• Earth-system models
• exascale
• Fortran codebases
• maintenance-heavy compiler directives
• high-level DSLs

Innovation

• GT4Py DSL
• DaCe optimization framework
• data-flow optimizations
• automated kernel fusion
• hardware-agnostic