@inproceedings{efef1ba6faf047cbb6ae19334bd6f8f3,
title = "Toward Automated Precision Tuning of Weather and Climate Models: A Case Study",
abstract = "Floating-point precision tuning (FPPT) searches target programs for computations amenable to reduced-precision, thereby trading accuracy for performance. FPPT does so by searching the mixed-precision design space for program variants maximizing performance constrained by some correctness criteria. Given their computational intensity and complexity, weather and climate models present prime FPPT targets. However, past attempts at FPPT in this domain are limited by manual efforts of domain experts (tedious) and low-precision emulation (obscures speedup). Automated and performance-guided techniques are naturally of interest but have not been explored at this scale. Facilitated by a bespoke Fortran transformation tool, this paper presents a first-of-its-kind case study: based on the varied results of applying FPPT to computational hotspots in three real-world weather and climate models (MPAS-A, ADCIRC, and MOM6), we identify and discuss important lessons learned and offer insights into best practices for feasible FPPT that targets large programs in complex domains such as this.",
keywords = "climate, correctness, floating-point, fortran, hpc, performance, precision, tuning, weather",
author = "Jackson Vanover and Alper Altuntas and Cindy Rubio-Gonzalez",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024 ; Conference date: 17-11-2024 Through 22-11-2024",
year = "2024",
doi = "10.1109/SCW63240.2024.00026",
language = "English",
series = "Proceedings of SC 2024-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "148--159",
booktitle = "Proceedings of SC 2024-W",
address = "United States",
}