Partitioning sparse rectangular matrices for parallel processing

  • Tamara G. Kolda
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DOI: 10.1007/BFb0018528

Part of the Lecture Notes in Computer Science book series (LNCS, volume 1457)
Cite this paper as:
Kolda T.G. (1998) Partitioning sparse rectangular matrices for parallel processing. In: Ferreira A., Rolim J., Simon H., Teng SH. (eds) Solving Irregularly Structured Problems in Parallel. IRREGULAR 1998. Lecture Notes in Computer Science, vol 1457. Springer, Berlin, Heidelberg

Abstract

We are interested in partitioning sparse rectangular matrices for parallel processing. The partitioning problem has been well-studied in the square symmetric case, but the rectangular problem has received very little attention. We will formalize the rectangular matrix partitioning problem and discuss several methods for solving it. We will extend the spectral partitioning method for symmetric matrices to the rectangular case and compare this method to three new methods — the alternating partitioning method and two hybrid methods. The hybrid methods will be shown to be best.

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Copyright information

© Springer-Verlag 1998

Authors and Affiliations

  • Tamara G. Kolda
    • 1
  1. 1.Computer Science and Mathematics DivisionOak Ridge National LaboratoryOak Ridge

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