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Parallel Model Reduction of Large Linear Descriptor Systems via Balanced Truncation

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High Performance Computing for Computational Science - VECPAR 2004 (VECPAR 2004)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3402))

Abstract

In this paper we investigate the use of parallel computing to deal with the high computational cost of numerical algorithms for model reduction of large linear descriptor systems. The state-space truncation methods considered here are composed of iterative schemes which can be efficiently implemented on parallel architectures using existing parallel linear algebra libraries. Our experimental results on a cluster of Intel Pentium processors show the performance of the parallel algorithms.

P. Benner was supported by the DFG Research Center “Mathematics for key technologies” (FZT 86) in Berlin. E.S. Quintana-Ortí and G. Quintana-Ortí were supported by the CICYT project No. TIC2002-004400-C03-01 and FEDER.

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Author information

Authors and Affiliations

  1. Fakultät für Mathematik, Technische Universität Chemnitz, D-09107, Chemnitz, Germany

    Peter Benner

  2. Depto. de Ingeniería y Ciencia de Computadores, Universidad Jaume I, 12.071, Castellón, Spain

    Enrique S. Quintana-Ortí & Gregorio Quintana-Ortí

Authors
  1. Peter Benner
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  2. Enrique S. Quintana-Ortí
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  3. Gregorio Quintana-Ortí
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Editor information

Editors and Affiliations

  1. INP (ENSEEIHT); IRIT, University of Toulouse, rue Charles-Camichel, 31071, Toulouse CEDEX 7, France

    Michel Daydé

  2. Computer Science Department, University of Tennessee, 37996-3450, Knoxville, TN, USA

    Jack Dongarra

  3. EUIT Telecomunicación - DIATEL, Universidad Politécnica de Madrid, Ctra. Valencia, Km. 7, 28031, Madrid, Spain

    Vicente Hernández

  4. Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias s/n, 4200-465, Porto, Portugal

    José M. L. M. Palma

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Benner, P., Quintana-Ortí, E.S., Quintana-Ortí, G. (2005). Parallel Model Reduction of Large Linear Descriptor Systems via Balanced Truncation. In: Daydé, M., Dongarra, J., Hernández, V., Palma, J.M.L.M. (eds) High Performance Computing for Computational Science - VECPAR 2004. VECPAR 2004. Lecture Notes in Computer Science, vol 3402. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11403937_27

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  • DOI: https://doi.org/10.1007/11403937_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25424-9

  • Online ISBN: 978-3-540-31854-5

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