将Python动力学核心集成到ICON中
Integrating a Python Dynamical core into ICON
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中文总结 AI 辅助
本文将基于Python的ICON动力学核心集成到Fortran模拟代码，借助GT4Py DSL和DaCe框架实现无缝集成，在生产级全球模拟中性能优于Fortran+OpenACC方案，证明Python可用于可持续高效的气候建模。
中文摘要 AI 辅助
地球系统模型向百亿亿级计算的过渡常受限于僵化、整体式的Fortran代码库及维护成本高昂的编译器指令。尽管高级领域特定语言（DSL）提供了一种解决方案，但它们往往因集成繁琐而失效。本文介绍了将基于Python的ICON动力学核心集成到原始Fortran模拟代码中的工作。借助GT4Py DSL和以数据为中心的（DaCe）优化框架，我们证明高级Python代码可无缝集成到遗留基础设施中且不会损失性能。我们的结果挑战了Python编排会带来过高高性能计算（HPC）开销的假设。在生产级全球模拟中，我们的Python动力学核心相比高度优化的Fortran+OpenACC实现，性能提升了20%至30%，耦合设置的总时间提升了10%。该方法由高级数据流优化和自动内核融合驱动，通过从单一可移植Python源代码生成优化的设备代码，取代了与硬件绑定的指令。本工作证明Python可为全球气候建模提供可持续、高效且硬件无关的未来。
英文摘要
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.
