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Using accurate AIC-based performance models to improve the scheduling of parallel applications

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Abstract

Predictions based on analytical performance models can be used on efficient scheduling policies in order to select adequate resources for an optimal execution in terms of throughput and response time. However, developing accurate analytical models of parallel applications is a hard issue. The TIA (Tools for Instrumenting and Analysis) modeling framework provides an easy to use modeling method for obtaining analytical models of MPI applications. This method is based on modeling selection techniques and, in particular, on Akaike’s information criterion (AIC). In this paper, first the AIC-based performance model of the HPL benchmark is obtained using the TIA modeling framework. Then the use of this model for assessing the runtime estimation on different backfilling policies is analyzed in the GridSim simulator. The behavior of these simulations is compared with the equivalent simulations based on the theoretical model of the HPL provided by its developers.

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Authors and Affiliations

  1. Dept. of Electronic and Computer Science, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain

    Diego R. Martínez, Julio L. Albín, Tomás F. Pena, José C. Cabaleiro & Francisco F. Rivera

  2. Dept. Statistic and Computer Science, La Laguna University, 38271, La Laguna, Spain

    Vicente Blanco

Authors
  1. Diego R. Martínez
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  2. Julio L. Albín
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  3. Tomás F. Pena
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  4. José C. Cabaleiro
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  5. Francisco F. Rivera
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  6. Vicente Blanco
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Corresponding author

Correspondence to Diego R. Martínez.

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Martínez, D.R., Albín, J.L., Pena, T.F. et al. Using accurate AIC-based performance models to improve the scheduling of parallel applications. J Supercomput 58, 332–340 (2011). https://doi.org/10.1007/s11227-011-0589-1

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  • DOI: https://doi.org/10.1007/s11227-011-0589-1

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