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The computation of partial eigensolutions on a distributed memory machine using a modified lanczos method

  • Kieran Murphy
  • Maurice Clint
  • Marek Szularz
  • Jim Weston
Workshop 07 Parallel Numerical Algorithms
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1124)

Abstract

The Lanczos algorithm is one of the most widely used methods for finding a small number of the extremal eigenvalues and associated eigenvectors of large, sparse, symmetric matrices. In this paper the performance of a modified version of the algorithm which incorporates a novel convergence monitoring method is assessed. The investigation has been carried out using a 16-node Intel iPSC/860 hypercube. It is shown that a parallel implementation of the modified algorithm can efficiently exploit the facilities provided by this machine.

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References

  1. 1.
    Kim S.K., Chronopoulos A.T.:A class of Lanczos-like algorithms implemented on parallel computers. Parallel Computing 17 (1991) 767–778CrossRefGoogle Scholar
  2. 2.
    Parlett B.N.:The Symmetric Eigenvalue Problem. (1980) Prentice Hall, Engelwood Cliffs, NJ.Google Scholar
  3. 3.
    Szularz M., Weston J.S., Murphy K., Clint M.:Monitoring the convergence of the Lanczos algorithm in parallel computing environments. J. of Parallel Algorithms & Applications 6 (1995) 287–302Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Kieran Murphy
    • 1
  • Maurice Clint
    • 1
  • Marek Szularz
    • 2
  • Jim Weston
    • 2
  1. 1.Department of Computer ScienceThe Queen's University of BelfastBelfastUK
  2. 2.School of Information & Software EngineeringThe University of Ulster at ColeraineColeraineUK

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