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FEniCS-HPC: Automated predictive high-performance finite element computing with applications in aerodynamics
KTH, School of Computer Science and Communication (CSC), High Performance Computing and Visualization (HPCViz).ORCID iD: 0000-0003-4256-0463
KTH, School of Computer Science and Communication (CSC), High Performance Computing and Visualization (HPCViz).ORCID iD: 0000-0002-1695-8809
KTH, School of Computer Science and Communication (CSC), High Performance Computing and Visualization (HPCViz).ORCID iD: 0000-0002-5020-1631
2016 (English)In: Proceedings of the 11th International Conference on Parallel Processing and Applied Mathematics, PPAM 2015, Springer-Verlag New York, 2016, Vol. 9573, 356-365 p.Conference paper, Published paper (Refereed)
Abstract [en]

Developing multiphysics nite element methods (FEM) andscalable HPC implementations can be very challenging in terms of soft-ware complexity and performance, even more so with the addition ofgoal-oriented adaptive mesh renement. To manage the complexity we inthis work presentgeneraladaptive stabilized methods withautomatedimplementation in the FEniCS-HPCautomatedopen source softwareframework. This allows taking the weak form of a partial dierentialequation (PDE) as input in near-mathematical notation and automati-cally generating the low-level implementation source code and auxiliaryequations and quantities necessary for the adaptivity. We demonstratenew optimal strong scaling results for the whole adaptive frameworkapplied to turbulent ow on massively parallel architectures down to25000 vertices per core with ca. 5000 cores with the MPI-based PETScbackend and for assembly down to 500 vertices per core with ca. 20000cores with the PGAS-based JANPACK backend. As a demonstration ofthe high impact of the combination of the scalability together with theadaptive methodology allowing prediction of gross quantities in turbulent ow we present an application in aerodynamics of a full DLR-F11 aircraftin connection with the HiLift-PW2 benchmarking workshop with goodmatch to experiments.

Place, publisher, year, edition, pages
Springer-Verlag New York, 2016. Vol. 9573, 356-365 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743
National Category
Computational Mathematics Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-170369DOI: 10.1007/978-3-319-32149-3_34ISI: 000400134500034Scopus ID: 2-s2.0-84968610610OAI: oai:DiVA.org:kth-170369DiVA: diva2:828042
Conference
11th International Conference on Parallel Processing and Applied Mathematics
Note

QC 20151215

Available from: 2015-06-29 Created: 2015-06-29 Last updated: 2017-05-23Bibliographically approved

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Hoffman, JohanJansson, Niclas

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