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.
This work was supported by the Applied Mathematical Sciences Research Program, Office of Energy Research, U.S. Department of Energy, under contract DE-AC05-960R22464 with Lockheed Martin Energy Research Corporation.
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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. https://doi.org/10.1007/BFb0018528
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DOI: https://doi.org/10.1007/BFb0018528
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