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024 7 _ |2 DOI
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|a Mathematics, Applied
100 1 _ |a Aliaga, J.I.
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245 _ _ |a Solving dense generalized eigenproblems on multi-threaded architectures
260 _ _ |a New York, NY
|b Elsevier
|c 2012
300 _ _ |a 11279 - 11289
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440 _ 0 |a Applied Mathematics and Computation
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520 _ _ |a We compare two approaches to compute a fraction of the spectrum of dense symmetric definite generalized eigenproblems: one is based on the reduction to tridiagonal form, and the other on the Krylov-subspace iteration. Two large-scale applications, arising in molecular dynamics and material science, are employed to investigate the contributions of the application, architecture, and parallelism of the method to the performance of the solvers. The experimental results on a state-of-the-art 8-core platform, equipped with a graphics processing unit (GPU), reveal that in realistic applications, iterative Krylov-subspace methods can be a competitive approach also for the solution of dense problems. (C) 2012 Elsevier Inc. All rights reserved.
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|a Eigenvalues
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|a Tridiagonal form
653 2 0 |2 Author
|a Krylov-subspace iteration
653 2 0 |2 Author
|a Multi-core architectures
653 2 0 |2 Author
|a Graphics processors
700 1 _ |a Bientinesi, P.
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700 1 _ |a Davidovic, D.
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700 1 _ |a Di Napoli, E.
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700 1 _ |a Igual, F.D.
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700 1 _ |a Quintana-Orti­, E.S.
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773 _ _ |a 10.1016/j.amc.2012.05.020
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|t Applied mathematics and computation
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|x 0096-3003
856 7 _ |u http://dx.doi.org/10.1016/j.amc.2012.05.020
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