Reduction to condensed form for the Eigenvalue problem on distributed memory architectures

Jack J. Dongarra, Robert A. van de Geijn

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34 Scopus citations

Abstract

In this paper, we describe a parallel implementation for the reduction of general and symmetric matrices to Hessenberg and tridiagonal form, respectively. The methods are based on LAPACK sequential codes and use a panel-wrapped mapping of matrices to nodes. Results from experiments on the Intel Touchstone Delta are given.

Original languageEnglish
Pages (from-to)973-982
Number of pages10
JournalParallel Computing
Volume18
Issue number9
DOIs
StatePublished - Sep 1992

Funding

While parallel implementations of algorithms for solving linear systems have been widely studied \[4, 9\], the reduction to condensed form has not enjoyed the same attention. A parallel * This work was supported in part by the National Science Foundation Science and TechnologyC enter Cooperative Agreement No. CCR-8809615, by the Applied Mathematical Science Research Program, Office of Energy Research, US Department of Energy, under Contract DE-AC05-84OR21400, and by the Army Research Oftice under DARPA Contract DAAL 03-91-c-0046. Most of this work was performed while the second author was on leave at the University of Tennessee. Correspondence to: Jack J. Dongarra, Dept. of Computer Sciences, Univ. of Tennessee, Knoxville," IN 37996, USA.

Keywords

  • Eigenvalue problem
  • LAPACK
  • distributed memory architecture
  • linear algebra

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