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Optimizing a parameterized message-passing metaheuristic scheme on a heterogeneous cluster

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Abstract

This paper studies the development of message-passing parameterized schemes of metaheuristics and the use of auto-tuning techniques to optimize their execution time. Previous parameterized schemes on shared-memory are extended with new metaheuristic-parallelism parameters representing the migration frequency, the size of the migration and the number of processes. An optimization Problem of Electricity Consumption in Exploitation of Wells is used as test case. Experimental results in heterogeneous systems are reported for this problem, and the influence of the parallelism parameters is studied. The message-passing scheme proves to be preferable to the shared-memory scheme in terms of execution time, giving similar results for the goodness of the solutions. In the executions in a heterogeneous cluster, the best experimental results are obtained in terms of speed-up and quality of the solution by mapping a number of processes close to the value of the population size, and considering the relative speeds of the components of the heterogeneous system. Furthermore, optimized execution times can be achieved with auto-tuning techniques based on theoretical–empirical models of the execution time.

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Acknowledgments

This work was supported by the Spanish MINECO, as well as European Commission FEDER funds, under Grant TIN2015-66972-C5-3-R.

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Authors and Affiliations

  1. Departamento de Informática y Sistemas, University of Murcia, 30071, Murcia, Spain

    José-Matías Cutillas-Lozano & Domingo Giménez

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  1. José-Matías Cutillas-Lozano
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  2. Domingo Giménez
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Correspondence to José-Matías Cutillas-Lozano.

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The authors declare that they have no conflict of interest.

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Communicated by C.M. Vide, A.H. Dediu.

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Cutillas-Lozano, JM., Giménez, D. Optimizing a parameterized message-passing metaheuristic scheme on a heterogeneous cluster. Soft Comput 21, 5557–5572 (2017). https://doi.org/10.1007/s00500-016-2371-z

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