Accelerating BST Methods for Model Reduction with Graphics Processors
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Altres documents de l'autoria: Benner, Peter; Ezzatti, Pablo; Quintana-Orti, Enrique S.; Remón Gómez, Alfredo
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Mostra el registre complet de l'elementcomunitat-uji-handle:10234/9
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comunitat-uji-handle3:10234/61544
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Títol
Accelerating BST Methods for Model Reduction with Graphics ProcessorsData de publicació
2012Editor
Springer Berlin HeidelbergISBN
978-3-642-31463-6ISSN
0302-9743; 1611-3349Cita bibliogràfica
Benner, Peter; Ezzatti, Pablo; Quintana-Ortí, Enrique S.; Remón, Alfredo " Accelerating BST Methods for Model Reduction with Graphics Processors". En: Parallel Processing and Applied Mathematics– 9th International Conference, PPAM 2011, Torun, Poland, September 11-14, 2011. Revised Selected Papers, Part I / Wyrzykowski, Roman [et al.] (Eds.). Berlin : Springer, 2012. (Lecture Notes in Computer Science; 7203) . ISBN 978-3-642-31463-6, pp. 549-558Tipus de document
info:eu-repo/semantics/bookPartVersió de l'editorial
http://link.springer.com/chapter/10.1007/978-3-642-31464-3_56Paraules clau / Matèries
Resum
Model order reduction of dynamical linear time-invariant system appears in many scientific and engineering applications. Numerically reliable SVD-based methods for this task require O(n3) floating-point arithmetic ... [+]
Model order reduction of dynamical linear time-invariant system appears in many scientific and engineering applications. Numerically reliable SVD-based methods for this task require O(n3) floating-point arithmetic operations, with n being in the range 103 − 105 for many practical applications. In this paper we investigate the use of graphics processors (GPUs) to accelerate model reduction of large-scale linear systems via Balanced Stochastic Truncation, by off-loading the computationally intensive tasks to this device. Experiments on a hybrid platform consisting of state-of-the-art general-purpose multi-core processors and a GPU illustrate the potential of this approach. [-]
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