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Solving Large-Scale Eigenvalue Problems on Vector Parallel Processors

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Vector and Parallel Processing – VECPAR’98 (VECPAR 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1573))

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Abstract

We consider the development and implementation of eigensolvers on distributed memory parallel arrays of vector processors and show that the concomitant requirements for vectorisation and parallelisation lead both to novel algorithms and novel implementation techniques. Performance results are given for several large-scale applications and some performance comparisons made with LAPACK and ScaLAPACK.

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Authors and Affiliations

  1. Centre for Mathematics and its Applications, School of Mathematical Sciences, Australian National University, Canberra, ACT 0200, Australia

    David L. Harrar II & Michael R. Osborne

Authors
  1. David L. Harrar II
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  2. Michael R. Osborne
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Editor information

Editors and Affiliations

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

    Vicente Hernández

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

    José M. L. M. Palma

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

    Jack J. Dongarra

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© 1999 Springer-Verlag Berlin Heidelberg

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Harrar, D.L., Osborne, M.R. (1999). Solving Large-Scale Eigenvalue Problems on Vector Parallel Processors. In: Hernández, V., Palma, J.M.L.M., Dongarra, J.J. (eds) Vector and Parallel Processing – VECPAR’98. VECPAR 1998. Lecture Notes in Computer Science, vol 1573. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10703040_9

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  • DOI: https://doi.org/10.1007/10703040_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66228-0

  • Online ISBN: 978-3-540-48516-2

  • eBook Packages: Springer Book Archive

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