Accelerating Model Reduction of Large Linear Systems with Graphics Processors
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7036
comunitat-uji-handle3:10234/61544
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INVESTIGACIONMetadata
Title
Accelerating Model Reduction of Large Linear Systems with Graphics ProcessorsAuthor (s)
Date
2012Publisher
Springer Berlin HeidelbergISBN
978-3-642-28144-0ISSN
0302-9743; 1611-3349Bibliographic citation
Benner, Peter; Ezzatti, Pablo; Kressner, Daniel; Quintana-Ortí, Enrique S.; Remón, Alfredo " Accelerating Model Reduction of Large Linear Systems with Graphics Processors". En: Applied Parallel and Scientific Computing– 10th International Conference, PARA 2010, Reykjavík, Iceland, June 6-9, 2010, Revised Selected Papers, Part II / Jónasson, Kristen [et al.] (Eds.). Berlin : Springer, 2012. (Lecture Notes in Computer Science; 7134) . ISBN 978-3-642-28144-0, pp. 88-97Type
info:eu-repo/semantics/bookPartPublisher version
http://link.springer.com/chapter/10.1007/978-3-642-28145-7_9#Subject
Abstract
Model order reduction of a dynamical linear time-invariant system appears in many applications from science and engineering. Numerically reliable SVD-based methods for this task require in general O(n3) floating-point ... [+]
Model order reduction of a dynamical linear time-invariant system appears in many applications from science and engineering. Numerically reliable SVD-based methods for this task require in general 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 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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