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A parallel genetic algorithm for task mapping on parallel machines

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Parallel and Distributed Processing (IPPS 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1586))

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Abstract

In parallel processing systems, a fundamental consideration is the maximization of system performance through task mapping. A good allocation strategy may improve resource utilization and increase significantly the throughput of the system. We demonstrate how to map the tasks among the processors to meet performance criteria, such as minimizing execution time or communication delays. We review the Local Neighborhhod Search (LNS) strategy for the mapping problem. We base our approach on LNS since it was shown that this method outperforms a large number of heuristic-based algorithms. We call our mapping algorithm, that is based on LNS, Genetic Local Neighborhood Search (GLNS), and its parallel version, Parallel Genetic Local Neighborhood Search (P-GLNS). We implemented and compared all three of these mapping strategies. The experimental results demonstrate that 1) GLNS algorithm was better performance than LNS and, 2) The P-GLNS algorithm achieves near linear speedup.

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Author information

Authors and Affiliations

  1. Florida Institute of Technology, Melbourne, Florida, USA

    S. Mounir Alaoui

  2. Illinois Institute of Technology, Chicago, Illinois, USA

    O. Frieder

  3. George Mason University, Washington D.C, USA

    T. El-Ghazawi

Authors
  1. S. Mounir Alaoui
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  2. O. Frieder
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  3. T. El-Ghazawi
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Editor information

José Rolim Frank Mueller Albert Y. Zomaya Fikret Ercal Stephan Olariu Binoy Ravindran Jan Gustafsson Hiroaki Takada Ron Olsson Laxmikant V. Kale Pete Beckman Matthew Haines Hossam ElGindy Denis Caromel Serge Chaumette Geoffrey Fox Yi Pan Keqin Li Tao Yang G. Chiola G. Conte L. V. Mancini Domenique Méry Beverly Sanders Devesh Bhatt Viktor Prasanna

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© 1999 Springer-Verlag

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Alaoui, S.M., Frieder, O., El-Ghazawi, T. (1999). A parallel genetic algorithm for task mapping on parallel machines. In: Rolim, J., et al. Parallel and Distributed Processing. IPPS 1999. Lecture Notes in Computer Science, vol 1586. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0097901

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  • DOI: https://doi.org/10.1007/BFb0097901

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65831-3

  • Online ISBN: 978-3-540-48932-0

  • eBook Packages: Springer Book Archive

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