Reduction to Condensed Forms for Symmetric Eigenvalue Problems on Multi-core Architectures
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7036
comunitat-uji-handle3:10234/8620
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http://dx.doi.org/10.1007/978-3-642-14390-8_40 |
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Title
Reduction to Condensed Forms for Symmetric Eigenvalue Problems on Multi-core ArchitecturesDate
2010Publisher
Springer Berlin HeidelbergISBN
978-3-642-14389-2ISSN
0302-9743Bibliographic citation
Parallel Processing and Applied Mathematics. Berlin: Springer Berlin Heidelberg, 2010, p. 387-395Type
info:eu-repo/semantics/bookPartPublisher version
http://link.springer.com/chapter/10.1007/978-3-642-14390-8_40#Abstract
We investigate the performance of the routines in LAPACK and the Successive Band Reduction (SBR) toolbox for the reduction of a dense matrix to tridiagonal form, a crucial preprocessing stage in the solution of the ... [+]
We investigate the performance of the routines in LAPACK and the Successive Band Reduction (SBR) toolbox for the reduction of a dense matrix to tridiagonal form, a crucial preprocessing stage in the solution of the symmetric eigenvalue problem, on general-purpose multi-core processors. In response to the advances of hardware accelerators, we also modify the code in SBR to accelerate the computation by off-loading a significant part of the operations to a graphics processor (GPU). Performance results illustrate the parallelism and scalability of these algorithms on current high-performance multi-core architectures. [-]
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