Metrics and models for reordering transformations

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Metrics and models for reordering transformations
Irregular applications frequently exhibit poor performance on contemporary computer architectures, in large part because of their inefficient use of the memory hierarchy. Runtime data- and iteration-reordering transformations have been shown to improve the locality and therefore the performance of irregular benchmarks. This paper describes models for determining which combination of run-time data- and iterationreordering heuristics will result in the best performance for a given dataset. We propose that the data- and iterationreordering transformations be viewed as approximating minimal linear arrangements on two separate hypergraphs: a spatial locality hypergraph and a temporal locality hypergraph. Our results measure the efficacy of locality metrics based on these hypergraphs in guiding the selection of dataand iteration-reordering heuristics. We also introduce new iteration- and data-reordering heuristics based on the hypergraph models that result in better performance than do prev...
Michelle Mills Strout, Paul D. Hovland
Added 30 Jun 2010
Updated 30 Jun 2010
Type Conference
Year 2004
Authors Michelle Mills Strout, Paul D. Hovland
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