Uncertainty Quantification of Reduced-Precision Time Series in Turbulent Channel Flow-Reference-Cited by-同舟云学术

Uncertainty Quantification of Reduced-Precision Time Series in Turbulent Channel Flow

Published:2023-11-12 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis
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Author:

Karp Martin¹^ORCID,Liu Felix²^ORCID,Stanly Ronith¹^ORCID,Rezaeiravesh Saleh³^ORCID,Jansson Niclas¹^ORCID,Schlatter Philipp⁴^ORCID,Markidis Stefano¹^ORCID

Affiliation:

1. KTH Royal Institute of Technology, Sweden

2. KTH Royal Institute of Technology, Sweden and Raysearch Laboratories, Sweden

3. University of Manchester, United Kingdom

4. Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Germany and KTH Royal Institute of Technology, Sweden

Funder

Swedish e-Science Research Centre

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3624062.3624105

Reference15 articles.

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3. CPFloat: A C Library for Simulating Low-precision Arithmetic

4. N. Jansson , M. Karp , A. Podobas , S. Markidis , and P. Schlatter . 2021 . Neko: A modern, portable, and scalable framework for high-fidelity computational fluid dynamics. arXiv preprint 2107.01243 (2021). N. Jansson, M. Karp, A. Podobas, S. Markidis, and P. Schlatter. 2021. Neko: A modern, portable, and scalable framework for high-fidelity computational fluid dynamics. arXiv preprint 2107.01243 (2021).

5. IEEE standard 754 for binary floating-point arithmetic;Kahan W.;Lecture Notes on the Status of IEEE,1996