Predicting application run times using historical information.

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The authors present a technique for deriving predictions for the run times of parallel applications from the run times of similar applications that have executed in the past. The novel aspect of the work is the use of search techniques to determine those application characteristics that yield the best definition of similarity for the purpose of making predictions. They use four workloads recorded from parallel computers at Argonne National Laboratory, the Cornell Theory Center, and the San Diego Supercomputer Center to evaluate the effectiveness of the approach.They show that on these workloads the techniques achieve predictions that are between 14 ... continued below

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16 p.

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Foster, I.; Smith, W. & Taylor, V. June 25, 1999.

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Description

The authors present a technique for deriving predictions for the run times of parallel applications from the run times of similar applications that have executed in the past. The novel aspect of the work is the use of search techniques to determine those application characteristics that yield the best definition of similarity for the purpose of making predictions. They use four workloads recorded from parallel computers at Argonne National Laboratory, the Cornell Theory Center, and the San Diego Supercomputer Center to evaluate the effectiveness of the approach.They show that on these workloads the techniques achieve predictions that are between 14 and 60% better than those achieved by other researchers; the approach achieves mean prediction errors that are between 41 and 65% of mean application run times.

Physical Description

16 p.

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OSTI as DE00011861

Medium: P; Size: 16 pages

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  • 12th International Parallel Processing Symposium (IPPS '98), Orlando, FL (US), 03/30/1998--04/03/1998

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  • Report No.: ANL/MCS/CP-99347
  • Grant Number: W-31109-ENG-38
  • Office of Scientific & Technical Information Report Number: 11861
  • Archival Resource Key: ark:/67531/metadc625695

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Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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  • June 25, 1999

Added to The UNT Digital Library

  • June 16, 2015, 7:43 a.m.

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  • April 7, 2017, 3:22 p.m.

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Foster, I.; Smith, W. & Taylor, V. Predicting application run times using historical information., article, June 25, 1999; Illinois. (digital.library.unt.edu/ark:/67531/metadc625695/: accessed October 23, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.