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A Task-Based Algorithm for Reordering the Eigenvalues of a Matrix in Real Schur Form
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0002-3689-0899
2018 (English)In: Parallel Processing and Applied Mathematics: PPAM 2017 / [ed] Roman Wyrzykowski, Jack Dongarra, Ewa Deelman, Konrad Karczewski, 2018, p. 207-216Conference paper, Published paper (Refereed)
Abstract [en]

A task-based parallel algorithm for reordering the eigenvalues of a matrix in real Schur form is presented.The algorithm is realized on top of the StarPU runtime system.Only the aspects which are relevant for shared memory machines are discussed here, but the implementation can be configured to run on distributed memory machines as well.Various techniques to reduce the overhead and the core idle time are discussed.Computational experiments indicate that the new algorithm is between 1.5 and 6.6 times faster than a state of the art MPI-based implementation found in ScaLAPACK.With medium to large matrices, strong scaling efficiencies above 60\% up to 28 CPU cores are reported.The overhead and the core idle time are shown to be negligible with the exception of the smallest matrices and highest core counts.

Place, publisher, year, edition, pages
2018. p. 207-216
Series
Lecture Notes in Computer Science ; 10777
Keywords [en]
Eigenvalue reordering problem, Task based programming, Shared memory machines
National Category
Computer Sciences Computational Mathematics
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-145987DOI: 10.1007/978-3-319-78024-5_19ISBN: 978-3-319-78023-8 (print)ISBN: 978-3-319-78024-5 (electronic)OAI: oai:DiVA.org:umu-145987DiVA, id: diva2:1192964
Conference
12th International Conference on Parallel Processing and Applied Mathematics (PPAM 2017)
Available from: 2018-03-24 Created: 2018-03-24 Last updated: 2018-06-09Bibliographically approved

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Myllykoski, Mirko

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CiteExportLink to record
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Citation style
  • apa
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  • de-DE
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  • nn-NB
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More languages
Output format
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