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A component infrastructure for performance and power modeling of parallel scientific applications
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Source Compframe/Hpc-Geco Workshop archive
Proceedings of the 2008 compFrame/HPC-GECO workshop on Component based high performance table of contents
Karlsruhe, Germany
SESSION: Portability and parallel performance table of contents
Article No. 6  
Year of Publication: 2008
ISBN:978-1-60558-311-2
Authors
Van Bui  University of Houston, Houston, TX
Boyana Norris  Argonne National Laboratory, Argonne, IL
Kevin Huck  University of Oregon, Eugene, OR
Lois Curfman McInnes  Argonne National Laboratory, Argonne, IL
Li Li  Argonne National Laboratory, Argonne, IL
Oscar Hernandez  University of Houston, Houston, TX
Barbara Chapman  University of Houston, Houston, TX
Sponsor
SIGPLAN: ACM Special Interest Group on Programming Languages
Publisher
ACM  New York, NY, USA
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ABSTRACT

Characterizing the performance of scientific applications is essential for effective code optimization, both by compilers and by high-level adaptive numerical algorithms. While maximizing power efficiency is becoming increasingly important in current high-performance architectures, little or no hardware or software support exists for detailed power measurements. Hardware counter-based power models are a promising method for guiding software-based techniques for reducing power. We present a component-based infrastructure for performance and power modeling of parallel scientific applications. The power model leverages on-chip performance hardware counters and is designed to model power consumption for modern multiprocessor and multicore systems. Our tool infrastructure includes application components as well as performance and power measurement and analysis components. We collect performance data using the TAU performance component and apply the power model in the performance and power analysis of a PETSc-based parallel fluid dynamics application by using the PerfExplorer component.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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Collaborative Colleagues:
Van Bui: colleagues
Boyana Norris: colleagues
Kevin Huck: colleagues
Lois Curfman McInnes: colleagues
Li Li: colleagues
Oscar Hernandez: colleagues
Barbara Chapman: colleagues