Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Predicting application run times using historical information

  • Conference paper
  • First Online:
Job Scheduling Strategies for Parallel Processing (JSSPP 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1459))

Included in the following conference series:

Abstract

We 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 our 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. We 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 our approach. We show that on these workloads our techniques achieve predictions that are between 14 and 60 percent better than those achieved by other researchers; our approach achieves mean prediction errors that are between 40 and 59 percent of mean application run times.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. C. Catlett and L. Smarr. Metacomputing. Communications of the ACM, 35(6):44–52, 1992.

    Article  Google Scholar 

  2. K. Czajkowski, I. Foster, C. Kesselman, S. Martin, W. Smith, and S. Tuecke. A Resource Management Architecture for Metasystems. Lecture Notes on Computer Science, 1998.

    Google Scholar 

  3. Murthy Devarakonda and Ravishankar Iyer. Predictability of Process Resource Usage: A Measurement-Based Study on UNIX. IEEE Transactions on Software Engineering, 15(12):1579–1586, December 1989.

    Article  Google Scholar 

  4. Allen Downey. Predicting Queue Times on Space-Sharing Parallel Computers. In International Parallel Processing Symposium, 1997.

    Google Scholar 

  5. N. R. Draper and H. Smith. Applied Regression Analysis, 2nd Edition. John Wiley and Sons, 1981.

    Google Scholar 

  6. Dror Feitelson and Bill Nitzberg. Job Characteristics of a Production Parallel Scientific Workload on the NASA Ames iPSC/860. Lecture Nodes on Computer Science, 949, 1995.

    Google Scholar 

  7. Ian Foster and Carl Kesselman. Globus: A Metacomputing Infrastructure Toolkit. International Journal of Supercomputing Applications, 11(2):115–128, 1997.

    Article  Google Scholar 

  8. Richard Gibbons. A Historical Application Profiler for Use by Parallel Scheculers. Lecture Notes on Computer Science, pages 58–75, 1997.

    Google Scholar 

  9. Richard Gibbons. A Historical Profiler for Use by Parallel Schedulers. Master's thesis, University of Toronto, 1997.

    Google Scholar 

  10. David E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, 1989.

    Google Scholar 

  11. Neil Weiss and Matthew Hassett. Introductory Statistics. Addison-Wesley, 1982.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Mathematics and Computer Science Division, Argonne National Laboratory, 60439, Argonne, IL

    Warren Smith & Ian Foster

  2. Electrical and Computer Engineering Department, Northwestern University, 60208, Evanston, IL

    Warren Smith & Valerie Taylor

Authors
  1. Warren Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ian Foster
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Valerie Taylor
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Dror G. Feitelson Larry Rudolph

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Smith, W., Foster, I., Taylor, V. (1998). Predicting application run times using historical information. In: Feitelson, D.G., Rudolph, L. (eds) Job Scheduling Strategies for Parallel Processing. JSSPP 1998. Lecture Notes in Computer Science, vol 1459. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0053984

Download citation

  • DOI: https://doi.org/10.1007/BFb0053984

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64825-3

  • Online ISBN: 978-3-540-68536-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation