HPCS HPCchallenge Benchmark Suite

Abstract

The Defense Advanced Research Projects Agency (DARPA) High Productivity Computing Systems (HPCS) HPCchallenge Benchmarks examine the performance of High Performance Computing (HPC) architectures using kernels with more challenging memory access patterns than just the High Performance LINPACK (HPL) benchmark used in the Top 500 list. The HPCchallenge Benchmarks build on the HPL framework and augment the Top 500 list by providing benchmarks that bound the performance of many real applications as a function of memory access locality characteristics. The real utility of the HPCchallenge benchmarks are that architectures can be described with a wider range of metrics than just Flop/s from HPL. Even a small percentage of random memory accesses in real applications can significantly affect the overall performance of that application on architectures not designed to minimize or hide memory latency. The HPCchallenge Benchmarks includes a new metric Giga UPdates per Second and a new benchmark RandomAccess to measure the ability of an architecture to access memory randomly, i.e., with no locality. When looking only at HPL performance and the Top 500 List, inexpensive build-your-own clusters appear to be much more cost effective than more sophisticated HPC architectures. HPCchallenge Benchmarks provide users with additional information to justify policy and purchasing decisions. We will compare the measured HPCchallenge Benchmark performance on various HPC architectures from Cray XIs to Beowulf clusters in the presentation and paper. Additional information on the HPCchallenge Benchmarks can be found at http://icl. cs.utk.edu/hpcc/

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 2005
Accession Number
ADA433090

Entities

People

  • David Koester
  • Jack Dongarra
  • Piotr Luszczek

Organizations

  • MITRE Corporation

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  • Energy and Power Technologies
  • Weapons Technologies

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  • Abstracts
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Fields of Study

  • Computer science

Readers

  • Parallel and Distributed Computing.