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GPU-acceleration of A High Order Finite Difference Code Using Curvilinear Coordinates
KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.ORCID iD: 0000-0002-3859-9480
KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.ORCID iD: 0000-0002-6175-3466
KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.ORCID iD: 0000-0002-9901-9857
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2020 (English)In: Proceedings of the 2020 International Conference on Computing, Networks and Internet of Things, Association for Computing Machinery (ACM) , 2020, p. 41-47Conference paper, Published paper (Refereed)
Abstract [en]

GPU-accelerated computing is becoming a popular technology due to the emergence of techniques such as OpenACC, which makes it easy to port codes in their original form to GPU systems using compiler directives, and thereby speeding up computation times relatively simply. In this study we have developed an OpenACC implementation of the high order finite difference CFD solver ESSENSE for simulating compressible flows. The solver is based on summation-by-part form difference operators, and the boundary and interface conditions are weakly implemented using simultaneous approximation terms. This case study focuses on porting code to GPUs for the most time-consuming parts namely sparse matrix vector multiplications and the evaluations of fluxes. The resulting OpenACC implementation is used to simulate the Taylor-Green vortex which produces a maximum speed-up of 61.3 on a single V100 GPU by compared to serial CPU version.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM) , 2020. p. 41-47
Keywords [en]
Computational fluid dynamics, GPU programming, High order finite difference method, OpenACC
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:kth:diva-273805DOI: 10.1145/3398329.3398336Scopus ID: 2-s2.0-85086223863OAI: oai:DiVA.org:kth-273805DiVA, id: diva2:1447607
Conference
the 2020 International Conference on Computing, Networks and Internet of Things
Note

QC 20200819

Available from: 2020-06-26 Created: 2020-06-26 Last updated: 2023-03-30Bibliographically approved

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Gong, JingAxner, LilitLaure, Erwin

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