A Parallel QZ Algorithm for Distributed Memory HPC Systems

Appearing frequently in applications, generalized eigenvalue problems represent one of the core problems in numerical linear algebra. The QZ algorithm of Moler and Stewart is the most widely used algorithm for addressing such problems. Despite its importance, little attention has been paid to the pa...

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Veröffentlicht in:SIAM journal on scientific computing Jg. 36; H. 5; S. C480 - C503
Hauptverfasser: Adlerborn, Björn, Kågström, Bo, Kressner, Daniel
Format: Journal Article
Sprache:Englisch
Veröffentlicht: 01.01.2014
Schlagworte:
1993
ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE
aggressive early deflation
Algorithms
bulge chasing
Computation
Distributed memory
Eigenvalues
generalized eigenvalue problem
infinite genvalues
Ingredients
level 3 performance
Linear algebra
MMEL J
multishift
nonsymmetric QZ algorithm
P175
parallel algorithms
Parallel processing
Strategy
V19
ISSN:1064-8275, 1095-7197, 1095-7197
Online-Zugang:Volltext
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Zusammenfassung:Appearing frequently in applications, generalized eigenvalue problems represent one of the core problems in numerical linear algebra. The QZ algorithm of Moler and Stewart is the most widely used algorithm for addressing such problems. Despite its importance, little attention has been paid to the parallelization of the QZ algorithm. The purpose of this work is to fill this gap. We propose a parallelization of the QZ algorithm that incorporates all modern ingredients of dense eigensolvers, such as multishift and aggressive early deflation techniques. To deal with (possibly many) infinite eigenvalues, a new parallel deflation strategy is developed. Numerical experiments for several random and application examples demonstrate the effectiveness of our algorithm on two different distributed memory HPC systems.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1064-8275
1095-7197
1095-7197
DOI:10.1137/140954817