Massively Parallel Finite Element Programming

Timo Heister, Martin Kronbichler, Wolfgang Bangerth

Research output: Chapter in Book/Report/Conference proceedingChapter

7 Scopus citations


Today's large finite element simulations require parallel algorithms to scale on clusters with thousands or tens of thousands of processor cores. We present data structures and algorithms to take advantage of the power of high performance computers in generic finite element codes. Existing generic finite element libraries often restrict the parallelization to parallel linear algebra routines. This is a limiting factor when solving on more than a few hundreds of cores. We describe routines for distributed storage of all major components coupled with efficient, scalable algorithms. We give an overview of our effort to enable the modern and generic finite element library deal.II to take advantage of the power of large clusters. In particular, we describe the construction of a distributed mesh and develop algorithms to fully parallelize the finite element calculation. Numerical results demonstrate good scalability. © 2010 Springer-Verlag.
Original languageEnglish (US)
Title of host publicationRecent Advances in the Message Passing Interface
PublisherSpringer Nature
Number of pages10
ISBN (Print)9783642156458
StatePublished - 2010
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: Timo Heister is partly supported by the German ResearchFoundation (DFG) through GK 1023. Martin Kronbichler is supported by theGraduate School in Mathematics and Computation (FMB). Wolfgang Bangerthwas partially supported by Award No. KUS-C1-016-04 made by King Abdullah University of Science and Technology (KAUST), by a grant from the NSF-funded Computational Infrastructure in Geodynamics initiative through AwardNo. EAR-0426271, and by an Alfred P. Sloan Research Fellowship.The computations were done on the Hurr cluster of the Institute for AppliedMathematics and Computational Science(IAMCS)atTexasA&MUniversity.Hurr is supported by Award No. KUS-C1-016-04 made by King Abdullah Uni-versity of Science and Technology (KAUST).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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