Title:
Performance Evaluation of A Seismic Data Analysis Kernel on The KSR Multiprocessors
Performance Evaluation of A Seismic Data Analysis Kernel on The KSR Multiprocessors
Author(s)
Gu, Weiming
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Abstract
The 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.
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Date Issued
1994
Extent
316223 bytes
Resource Type
Text
Resource Subtype
Technical Report