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Parallel algorithms for the partial eigensolution of large sparse matrices on novel architecture computers

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Parallel Scientific Computing (PARA 1994)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 879))

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Abstract

In this paper a two year project whose principal goal is the development and implementation of efficient parallel algorithms for the computation of subsets of eigenvalues and associated eigenvectors of large, usually sparse, matrices is described. The eigenvalues may be the numerically largest, the numerically smallest, or those closest to a specified value. The main aims are to investigate and assess the comparative usefulness of new and existing algorithms for the solution of the partial eigenproblem, and to identify suitable parallel architectures on which they may be efficiently implemented. The project is currently in its first phase in which a detailed investigation of a number of variations of the basic Lanczos algorithm has been undertaken. A□standard version of the algorithm has been identified and implemented on two parallel machines and possibilities for enhancement of its performance based on the efficient monitoring of its computational progress are being studied. In this context a new version of the algorithm for the computation of the largest eigenvalue is proposed and evaluated.

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Author information

Authors and Affiliations

  1. Department of Computing Science, University of Ulster, BT52 1SA, Coleraine

    M. Szularz & J. Weston

  2. Department of Computer Science, The Queen's University of Belfast, BT7 1NN, Belfast

    K. Murphy & M. Clint

Authors
  1. M. Szularz
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  2. J. Weston
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  3. K. Murphy
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  4. M. Clint
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Editor information

Jack Dongarra Jerzy Waśniewski

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© 1994 Springer-Verlag Berlin Heidelberg

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Szularz, M., Weston, J., Murphy, K., Clint, M. (1994). Parallel algorithms for the partial eigensolution of large sparse matrices on novel architecture computers. In: Dongarra, J., Waśniewski, J. (eds) Parallel Scientific Computing. PARA 1994. Lecture Notes in Computer Science, vol 879. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0030174

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  • DOI: https://doi.org/10.1007/BFb0030174

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-58712-5

  • Online ISBN: 978-3-540-49050-0

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