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Decision Trees and MPI Collective Algorithm Selection Problem

  • Jelena Pješivac-Grbović
  • George Bosilca
  • Graham E. Fagg
  • Thara Angskun
  • Jack J. Dongarra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4641)

Abstract

Selecting the close-to-optimal collective algorithm based on the parameters of the collective call at run time is an important step for achieving good performance of MPI applications. In this paper, we explore the applicability of C4.5 decision trees to the MPI collective algorithm selection problem. We construct C4.5 decision trees from the measured algorithm performance data and analyze both the decision tree properties and the expected run time performance penalty.

In cases we considered, results show that the C4.5 decision trees can be used to generate a reasonably small and very accurate decision function. For example, the broadcast decision tree with only 21 leaves was able to achieve a mean performance penalty of 2.08%. Similarly, combining experimental data for reduce and broadcast and generating a decision function from the combined decision trees resulted in less than 2.5% relative performance penalty. The results indicate that C4.5 decision trees are applicable to this problem and should be more widely used in this domain.

Keywords

Decision Tree Message Passing Interface Decision Function Message Size Performance Penalty 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Jelena Pješivac-Grbović
    • 1
  • George Bosilca
    • 1
  • Graham E. Fagg
    • 1
  • Thara Angskun
    • 1
  • Jack J. Dongarra
    • 1
  1. 1.Innovative Computing Laboratory, The University of Tennessee Computer Science Department, 1122 Volunteer Blvd., Knoxville, TN 37996-3450USA

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