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Systolic genetic search, a systolic computing-based metaheuristic

  • Methodologies and Application
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Abstract

In this paper, we propose a new parallel optimization algorithm that combines ideas from the fields of metaheuristics and Systolic Computing. The algorithm, called Systolic Genetic Search (SGS), is designed to explicitly exploit the high degree of parallelism available in modern Graphics Processing Unit (GPU) architectures. In SGS, solutions circulate synchronously through a grid of processing cells, which apply adapted evolutionary operators on their inputs to compute their outputs that are then ejected from the cells and continue moving through the grid. Four different variants of SGS are experimentally studied for solving two classical benchmarking problems and a real-world application. An extensive experimental analysis, which considered several instances for each problem, shows that three of the SGS variants designed are highly effective since they can obtain the optimal solution in almost every execution for the instances and problems studied, as well as they outperform a Random Search (sanity check) and two Genetic Algorithms. The parallel implementation on GPU of the proposed algorithm has achieved a high performance obtaining runtime reductions from the sequential implementation that, depending on the instance considered, can arrive to around a hundred times, and have also exhibited a good scalability behavior when solving highly dimensional problem instances.

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Notes

  1. The B stands for Both flows.

  2. The random number generation on the CPU guarantees that, using the same seed, the results obtained by a stochastic algorithm in a CPU and in a GPU are the same.

  3. The two solutions are read from the first memory space of the GPU global memory, one from the array that stores the solutions moving horizontally and the other from the array that stores the solutions moving vertically.

  4. It should be noted that the two solutions are written in the second memory space of the GPU global memory, one in the array that stores the solutions moving horizontally and the other in the array that stores the solutions moving vertically.

  5. We made this decision, rather than making each thread calculate these values redundantly, in order to reduce the number of registers used by the block.

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Acknowledgments

Martín Pedemonte acknowledges support from Programa de Desarrollo de las Ciencias Básicas, Universidad de la República, and Agencia Nacional de Investigación e Innovación, Uruguay. Francisco Luna and Enrique Alba acknowledge partial support from the Spanish Ministry of Economy and Competitiveness and FEDER under contract TIN2011-28194. Francisco Luna also acknowledges partial support from TIN2011-28336. The authors would like to thank to M.Sc. Leonella Luzardo for her valuable comments and suggestions to improve the description of the biological phenomenon that inspires Systolic Computing and systolic based metaheuristics. The authors would also like to thank to the anonymous reviewers for their insightful and constructive suggestions.

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Authors and Affiliations

  1. Instituto de Computación, Facultad de Ingeniería, Universidad de la República, Julio Herrera y Reissig 565, 11300 , Montevideo, Uruguay

    Martín Pedemonte

  2. Depto. de Ingeniería de Sistemas Informáticos y Telemáticos, Centro Universitario de Mérida, Universidad de Extremadura, Santa Teresa de Jornet, 28, 06800 , Mérida, Spain

    Francisco Luna

  3. Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, E.T.S. Ingeniería Informática, Campus de Teatinos, 29071 , Málaga, Spain

    Enrique Alba

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  1. Martín Pedemonte
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  2. Francisco Luna
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Correspondence to Martín Pedemonte.

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Communicated by V. Loia.

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Pedemonte, M., Luna, F. & Alba, E. Systolic genetic search, a systolic computing-based metaheuristic. Soft Comput 19, 1779–1801 (2015). https://doi.org/10.1007/s00500-014-1363-0

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  • DOI: https://doi.org/10.1007/s00500-014-1363-0

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