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A Task-Based Algorithm for Reordering the Eigenvalues of a Matrix in Real Schur Form
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.ORCID-id: 0000-0002-3689-0899
2018 (engelsk)Inngår i: Parallel Processing and Applied Mathematics: PPAM 2017 / [ed] Roman Wyrzykowski, Jack Dongarra, Ewa Deelman, Konrad Karczewski, Springer, 2018, s. 207-216Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Springer, 2018. s. 207-216
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10777
Emneord [en]
Eigenvalue reordering problem, Task based programming, Shared memory machines
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datalogi
Identifikatorer
URN: urn:nbn:se:umu:diva-145987DOI: 10.1007/978-3-319-78024-5_19ISI: 000458563300019ISBN: 978-3-319-78023-8 (tryckt)ISBN: 978-3-319-78024-5 (digital)OAI: oai:DiVA.org:umu-145987DiVA, id: diva2:1192964
Konferanse
12th International Conference on Parallel Processing and Applied Mathematics (PPAM 2017)
Tilgjengelig fra: 2018-03-24 Laget: 2018-03-24 Sist oppdatert: 2019-04-16bibliografisk kontrollert

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