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A parallel local search in CPU/GPU for scheduling independent tasks on large heterogeneous computing systems

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Abstract

This article presents the parallel implementation on CPU/GPU of two variants of a stochastic local search method to efficiently solve the scheduling problem in heterogeneous computing systems. Both methods are based on a set of simple operators to keep the computational complexity as low as possible, thus allowing large instances of the scheduling problem to be efficiently addressed. The experimental analysis demonstrates that both versions of the parallel CPU/GPU stochastic local search are able to compute accurate suboptimal schedules in significantly shorter execution times than state-of-the-art schedulers, while also outperforming a recently published GPU parallel evolutionary scheduler in terms of both efficiency and solution quality.

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Acknowledgments

The work of S. Iturriaga and S. Nesmachnow has been partially supported by ANII and PEDECIBA, Uruguay. The work of F. Luna and E. Alba has been partially funded by FEDER (TIN2011-28194). The experiments were carried out using the HPC facility of the University of Luxembourg.

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Authors and Affiliations

  1. Universidad de la República, Montevideo, Uruguay

    Santiago Iturriaga & Sergio Nesmachnow

  2. Universidad de Extremadura, Campus Universitario de Mérida, Mérida, Spain

    Francisco Luna

  3. Universidad de Málaga, Málaga, Spain

    Enrique Alba

Authors
  1. Santiago Iturriaga
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  2. Sergio Nesmachnow
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  3. Francisco Luna
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  4. Enrique Alba
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Correspondence to Sergio Nesmachnow.

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Iturriaga, S., Nesmachnow, S., Luna, F. et al. A parallel local search in CPU/GPU for scheduling independent tasks on large heterogeneous computing systems. J Supercomput 71, 648–672 (2015). https://doi.org/10.1007/s11227-014-1315-6

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  • DOI: https://doi.org/10.1007/s11227-014-1315-6

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