Parallel Model Reduction of Large Linear Descriptor Systems via Balanced Truncation

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 implemente...

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Bibliographic Details
Published inHigh Performance Computing for Computational Science - VECPAR 2004 pp. 340 - 353
Main Authors Benner, Peter, Quintana-Ortí, Enrique S., Quintana-Ortí, Gregorio
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2005
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
balanced truncation
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Exact sciences and technology
linear descriptor systems
Model reduction
parallel algorithms
Software
High performance
Parallel algorithm
Descriptor system
Iterative method
State space method
Distributed computing
Linear model
Truncation
Algorithm performance
System reduction
Linear algebra
Reduced order systems
Numerical algorithm
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Summary: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.
Bibliography: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.
ISBN:9783540254249
3540254242
ISSN:0302-9743
1611-3349
DOI:10.1007/11403937_27