Uncertainty Quantification of Reduced-Precision Time Series in Turbulent Channel Flow
Karp M, Liu F, Stanly R, Rezaeiravesh S, Jansson N, Schlatter P, Markidis S (2023)
Publication Type: Conference contribution
Publication year: 2023
Publisher: Association for Computing Machinery
Pages Range: 387-390
Conference Proceedings Title: ACM International Conference Proceeding Series
Event location: Denver, CO, USA
ISBN: 9798400707858
Abstract
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.
Authors with CRIS profile
Involved external institutions
Royal Institute of Technology / Kungliga Tekniska Högskolan (KTH)
Sweden (SE)
University of Manchester
United Kingdom (GB)
Sweden (SE)
University of Manchester
United Kingdom (GB)
How to cite
APA:
Karp, M., Liu, F., Stanly, R., Rezaeiravesh, S., Jansson, N., Schlatter, P., & Markidis, S. (2023). Uncertainty Quantification of Reduced-Precision Time Series in Turbulent Channel Flow. In ACM International Conference Proceeding Series (pp. 387-390). Denver, CO, USA: Association for Computing Machinery.
MLA:
Karp, Martin, et al. "Uncertainty Quantification of Reduced-Precision Time Series in Turbulent Channel Flow." Proceedings of the 2023 International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023, Denver, CO, USA Association for Computing Machinery, 2023. 387-390.
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