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Uncertainty Quantification of Reduced-Precision Time Series in Turbulent Channel Flow
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).ORCID iD: 0000-0003-3374-8093
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST). Raysearch Laboratories..ORCID iD: 0000-0001-6865-9379
KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Fluid Mechanics and Engineering Acoustics.ORCID iD: 0000-0002-3814-7919
The University of Manchester, Manchester, United Kingdom.
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2023 (English)In: Proceedings of 2023 SC Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023, Association for Computing Machinery (ACM) , 2023, p. 387-390Conference paper, Published paper (Refereed)
Abstract [en]

With increased computational power through the use of arithmetic in low-precision, a relevant question is how lower precision affects simulation results, especially for chaotic systems where analytical round-off estimates are non-trivial to obtain. In this work, we consider how the uncertainty of the time series of a direct numerical simulation of turbulent channel flow at Ret = 180 is affected when restricted to a reduced-precision representation. We utilize a non-overlapping batch means estimator and find that the mean statistics can, in this case, be obtained with significantly fewer mantissa bits than conventional IEEE-754 double precision, but that the mean values are observed to be more sensitive in the middle of the channel than in the near-wall region. This indicates that using lower precision in the near-wall region, where the majority of the computational efforts are required, may benefit from low-precision floating point units found in upcoming computer hardware.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM) , 2023. p. 387-390
National Category
Fluid Mechanics and Acoustics
Identifiers
URN: urn:nbn:se:kth:diva-341470DOI: 10.1145/3624062.3624105Scopus ID: 2-s2.0-85178155242OAI: oai:DiVA.org:kth-341470DiVA, id: diva2:1825228
Conference
2023 International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023, Denver, United States of America, Nov 12 2023 - Nov 17 2023
Note

QC 20240109

Part of ISBN 979-840070785-8

Available from: 2024-01-09 Created: 2024-01-09 Last updated: 2024-01-09Bibliographically approved

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Karp, MartinLiu, FelixStanly, RonithJansson, NiclasSchlatter, PhilippMarkidis, Stefano

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