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Large-scale direct numerical simulations of turbulence using GPUs and modern Fortran
KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Beräkningsvetenskap och beräkningsteknik (CST). Division of Computational Science and Technology, EECS, KTH Royal Institute of Technology, Stockholm, Sweden.ORCID-id: 0000-0003-3374-8093
KTH, Skolan för teknikvetenskap (SCI), Teknisk mekanik, Strömningsmekanik och Teknisk Akustik. SimEx/FLOW, Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden.ORCID-id: 0000-0002-6712-8944
KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Parallelldatorcentrum, 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.
Vise andre og tillknytning
2023 (engelsk)Inngår i: The international journal of high performance computing applications, ISSN 1094-3420, E-ISSN 1741-2846Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
2023.
Emneord [en]
CFD, Turbulence, Fortran, GPU, HPC, DNS
HSV kategori
Forskningsprogram
Datalogi; Teknisk mekanik
Identifikatorer
URN: urn:nbn:se:kth:diva-330154DOI: 10.1177/10943420231158616OAI: oai:DiVA.org:kth-330154DiVA, id: diva2:1775521
Forskningsfinansiär
Swedish Research Council, 2018-05973Swedish Research Council, 2019- 04723Tilgjengelig fra: 2023-06-27 Laget: 2023-06-27 Sist oppdatert: 2023-06-27

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Karp, MartinMassaro, DanieleJansson, NiclasSchlatter, Philipp
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