Performance Portability Explained: Insights from accelerating ICON microphysics
Year: 2026
Type: preprint
Abstract: Scientific computing is under increasing pressure to adapt to HPC hardware that is shaped by AI workloads. In these modern ecosystems, extracting reasonable performance without investing major engineering effort is a central issue for domain scientists. Correctness constraints and intricate reference implementations represent further impediments to development. Instead of using a double-precision implementation as a reference, considering the underlying scientific problem directly opens more opportunities for performance improvements when updating and rewriting applications. We take seven different portability frameworks---covering pragma-based, DSL, and data-centric approaches---and evaluate them on a microphysics kernel of the ICON climate model. Our performance benchmarks show that each different HPC system benefits from a different combination of optimizations, so we develop the measure of framework expressivity to gauge how easily each framework can customize portable implementations in this way.
Source: arXiv (Cornell University)
Authors: Hörold, Faveo, Mania, Georgiana, Adamidis, Panagiotis, Balasubramanian, Harshada, Behrens, Jörg, Budanaz, Yakup, Calotoiu, Alexandru, Farabullini, Nicoletta, Herten, Andreas, Hoefler, Torsten, Koenneker, Yannik, Magkanaris, Ioannis, Müller, Philip, Müller, Ralf, Paone, Edoardo, Sawyer, William, Stevens, Bjorn, Vogt, Hannes
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225835266
Cites:
Cited by:
Related to: