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Large-scale direct numerical simulations of turbulence using GPUs and modern Fortran
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST). Division of Computational Science and Technology, EECS, KTH Royal Institute of Technology, Stockholm, Sweden.ORCID iD: 0000-0003-3374-8093
KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Fluid Mechanics and Engineering Acoustics. SimEx/FLOW, Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden.ORCID iD: 0000-0002-6712-8944
KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC. PDC Centre for High Performance Computing, EECS, KTH Royal Institute of Technology, Stockholm, Sweden.ORCID iD: 0000-0002-5020-1631
Hewlett Packard Enterpise (HPE), UK.
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2023 (English)In: The international journal of high performance computing applications, ISSN 1094-3420, E-ISSN 1741-2846Article in journal (Refereed) Published
Abstract [en]

We present our approach to making direct numerical simulations of turbulence with applications in sustainable shipping. We use modern Fortran and the spectral element method to leverage and scale on supercomputers powered by the Nvidia A100 and the recent AMD Instinct MI250X GPUs, while still providing support for user software developed in Fortran. We demonstrate the efficiency of our approach by performing the world’s first direct numerical simulation of the flow around a Flettner rotor at Re = 30,000 and its interaction with a turbulent boundary layer. We present a performance comparison between the AMD Instinct MI250X and Nvidia A100 GPUs for scalable computational fluid dynamics. Our results show that one MI250X offers performance on par with two A100 GPUs and has a similar power efficiency based on readings from on-chip energy sensors.

Place, publisher, year, edition, pages
2023.
Keywords [en]
CFD, Turbulence, Fortran, GPU, HPC, DNS
National Category
Fluid Mechanics and Acoustics Software Engineering
Research subject
Computer Science; Engineering Mechanics
Identifiers
URN: urn:nbn:se:kth:diva-330154DOI: 10.1177/10943420231158616OAI: oai:DiVA.org:kth-330154DiVA, id: diva2:1775521
Funder
Swedish Research Council, 2018-05973Swedish Research Council, 2019- 04723Available from: 2023-06-27 Created: 2023-06-27 Last updated: 2023-06-27

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Karp, MartinMassaro, DanieleJansson, NiclasSchlatter, Philipp
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The international journal of high performance computing applications
Fluid Mechanics and AcousticsSoftware Engineering

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