Optimizing MPI Runtime Parameter Settings by Using Machine Learning

  • Simone Pellegrini
  • Jie Wang
  • Thomas Fahringer
  • Hans Moritsch
Conference paper

DOI: 10.1007/978-3-642-03770-2_26

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5759)
Cite this paper as:
Pellegrini S., Wang J., Fahringer T., Moritsch H. (2009) Optimizing MPI Runtime Parameter Settings by Using Machine Learning. In: Ropo M., Westerholm J., Dongarra J. (eds) Recent Advances in Parallel Virtual Machine and Message Passing Interface. EuroPVM/MPI 2009. Lecture Notes in Computer Science, vol 5759. Springer, Berlin, Heidelberg

Abstract

Manually tuning MPI runtime parameters is a practice commonly employed to optimise MPI application performance on a specific architecture. However, the best setting for these parameters not only depends on the underlying system but also on the application itself and its input data. This paper introduces a novel approach based on machine learning techniques to estimate the values of MPI runtime parameters that tries to achieve optimal speedup for a target architecture and any unseen input program. The effectiveness of our optimization tool is evaluated against two benchmarks executed on a multi-core SMP machine.

Keywords

MPI optimization runtime parameter tuning multi-core 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Simone Pellegrini
    • 1
  • Jie Wang
    • 1
  • Thomas Fahringer
    • 1
  • Hans Moritsch
    • 1
  1. 1.Distributed and Parallel Systems GroupUniversity of InnsbruckInnsbruckAustria

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