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Enabling mixed-precision with the help of tools: a nekbone case study
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0009-0003-5512-254X
Université Paris-Saclay, UVSQ, LI-PaRAD, Gif-sur-Yvette, France.
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0002-4972-7097
Umeå University, Faculty of Science and Technology, Department of Computing Science. Uppsala University, Uppsala, Sweden.ORCID iD: 0000-0003-2414-700X
2025 (English)In: Parallel processing and applied mathematics: 15Th International Conference, Ppam 2024, Ostrava, Czech Republic, September 8–11, 2024, Revised Selected Papers, Part I / [ed] Roman Wyrzykowski, Jack Dongarra, Ewa Deelman, Konrad Karczewski, Cham: Springer Nature, 2025, p. 34-50Conference paper, Published paper (Refereed)
Abstract [en]

Mixed-precision computing has the potential to significantly reduce the cost of exascale computations, but determining when and how to implement it in programs can be challenging. In this article, we consider Nekbone, a mini-application for the Computational Fluid Dynamics (CFD) solver Nek5000, as a case study, and propose a methodology for enabling mixed-precision with the help of computer arithmetic tools and roofline model. We evaluate the derived mixed-precision program by combining metrics in three dimensions: accuracy, time-to-solution, and energy-to-solution. Notably, the introduction of mixed-precision in Nekbone, reducing time-to-solution by 40.7% and energy-to-solution by 47% on 128 MPI ranks without sacrificing the accuracy.

Place, publisher, year, edition, pages
Cham: Springer Nature, 2025. p. 34-50
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 15579
Keywords [en]
computer arithmetic tool, Conjugate Gradient, energy-to-solution, Mixed-precision, Nekbone, roofline model, Verificarlo
National Category
Computer Sciences Computational Mathematics
Identifiers
URN: urn:nbn:se:umu:diva-238100DOI: 10.1007/978-3-031-85697-6_3Scopus ID: 2-s2.0-105002711656ISBN: 9783031856969 (print)OAI: oai:DiVA.org:umu-238100DiVA, id: diva2:1956113
Conference
15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024, Ostrava, Czech Republic, September 8–11, 2024
Available from: 2025-05-05 Created: 2025-05-05 Last updated: 2025-05-05Bibliographically approved

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Chen, YanxiangBientinesi, PaoloIakymchuk, Roman

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Total: 136 hits
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