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Aggregation methods for solving sparse triangular systems on multiprocessors

Journal Article · · SIAM Journal on Scientific and Statistical Computing (Society for Industrial and Applied Mathematics); (USA)
DOI:https://doi.org/10.1137/0911008· OSTI ID:6502514
 [1]
  1. Dept. of Computer Science, Yale Univ., New Haven, CT (US)
Efficient methods are presented for solving large sparse triangular systems on multiprocessors. These methods use heuristics for the aggregation, mapping, and scheduling of relatively fine-grained computations whose data dependencies are specified by directed acyclic graphs. Results of experiments run on the Encore Multimax, as well as model problem analysis, measure the performance of the partitioning strategies on shared-memory architectures with varying synchronization costs.
OSTI ID:
6502514
Journal Information:
SIAM Journal on Scientific and Statistical Computing (Society for Industrial and Applied Mathematics); (USA), Journal Name: SIAM Journal on Scientific and Statistical Computing (Society for Industrial and Applied Mathematics); (USA) Vol. 11:1; ISSN 0196-5204; ISSN SIJCD
Country of Publication:
United States
Language:
English

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