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dc.contributor.authorGu, Weimingen_US
dc.date.accessioned2005-06-17T17:59:48Z
dc.date.available2005-06-17T17:59:48Z
dc.date.issued1994en_US
dc.identifier.urihttp://hdl.handle.net/1853/6733
dc.description.abstractThe paper investigates the effective performance attainable for a specific class of application programs on shared memory supercomputers. Specifically, we are to investigate how seismic data analysis applications behave on the Kendall Square Research Inc.'s KSR multiprocessors. The computational kernel of seismic computation algorithms is parallelized and its performance is analyzed. Three approaches for parallelizing the g5 kernel are analyzed: column-based, row-based, and grid-based parallelizations. All three approaches result in well balanced decompositions, but differ significantly in data locality. In general, the column-based approach has the best data locality, while the small grid-based approach has the worst. These results clearly indicate that data locality is one of the critical factors for attaining high performance for the g5 kernel. The best parallelized g5 kernel code achieves about 44% of both the KSR-1 and KSR-2 machines' peak computational performance.en_US
dc.format.extent316223 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.relation.ispartofseriesCC Technical Report; GIT-CC-94-43en_US
dc.subjectApplication programs
dc.subjectComputational kernels
dc.subjectComputational performance
dc.subjectData locality
dc.subjectG5 kernel
dc.subjectMultiprocessors
dc.subjectParallelization
dc.subjectPerformance studies
dc.subjectSeismic computation algorithms
dc.subjectShared memory computers
dc.subjectKSR computers
dc.titlePerformance Evaluation of A Seismic Data Analysis Kernel on The KSR Multiprocessorsen_US
dc.typeTechnical Reporteng_US


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