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Earth system models are developed with a tight coupling to target hardware,often containing specialized code predicated on processor characteristics. Thiscoupling stems from using imperative languages that hard-code computationschedules and layout. We present a detailed account of optimizing the FiniteVolume Cubed-Sphere Dynamical Core (FV3), improving productivity andperformance. By using a declarative Python-embedded stencil domain-specificlanguage and data-centric optimization, we abstract hardware-specific detailsand define a semi-automated workflow for analyzing and optimizing weather andclimate applications. The workflow utilizes both local and full-programoptimization, as well as user-guided fine-tuning. To prune the infeasibleglobal optimization space, we automatically utilize repeating code motifs via anovel transfer tuning approach. On the Piz Daint supercomputer, we scale to2,400 GPUs, achieving speedups of up to 3.92x over the tuned productionimplementation at a fraction of the original code.

Productive Performance Engineering for Weather and Climate Modeling with Python