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Domain Decomposition Models for Parallel Monte Carlo Transport

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Abstract

We present a strategy for parallelizing computations that use the transport method. It combines spatial domain decomposition with domain replication to realize the scaling benefits of replication while allowing for problems whose computational mesh will not fit in a single processor's memory. The mesh is decomposed into a small number of spatial domains—typically fewer domains than there are processors—and heuristics are used to estimate the computational effort required to generate the solution in each subdomain using Monte Carlo. That work estimate determines the number of times a subdomain is replicated relative to the others. Timing of runs for two problems show that the new method scales better than traditional domain decomposition.

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Authors and Affiliations

  1. Los Alamos National Laboratory, Mail Stop B265, Los Alamos, NM, 87545

    Henry J. Alme

  2. Department of Applied Science, University of California, Davis, PO Box 808, L-561, Livermore, CA, 94550

    Garry H. Rodrigue

  3. X-Division, Lawrence Livermore National Laboratory, PO Box 808. L-030, Livermore, CA, 94550

    George B. Zimmerman

Authors
  1. Henry J. Alme
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  2. Garry H. Rodrigue
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  3. George B. Zimmerman
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Alme, H.J., Rodrigue, G.H. & Zimmerman, G.B. Domain Decomposition Models for Parallel Monte Carlo Transport. The Journal of Supercomputing 18, 5–23 (2001). https://doi.org/10.1023/A:1008196906753

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  • DOI: https://doi.org/10.1023/A:1008196906753

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