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A MIMD Interpreter for Genetic Programming

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Applications of Evolutionary Computation (EvoApplications 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12104))

Abstract

Most Genetic Programming implementations use an interpreter to execute an individual, in order to obtain its outcome. Usually, such interpreter is the main bottleneck of the algorithm, since a single individual may contain thousands of instructions that must be executed on a dataset made of a large number of samples. Although one can use SIMD (Single Instruction Multiple Data) intrinsics to execute a single instruction on a few samples at the same time, multiple passes on the dataset are necessary to calculate the result. To speed up the process, we propose using MIMD (Multiple Instruction Multiple Data) instruction sets. This way, in a single pass one can execute several instructions on the dataset. We employ AVX2 intrinsics to improve the performance even further, reaching a median peak of 7.5 billion genetic programming operations per second in a single CPU core.

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Notes

  1. 1.

    https://software.intel.com/en-us/node/523876.

  2. 2.

    https://software.intel.com/sites/landingpage/IntrinsicsGuide/#techs=FMA&expand=2549.

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Acknowledgements

This work was supported by Grant #2016/07095-5, São Paulo Research Foundation (FAPESP).

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Authors and Affiliations

  1. Federal University of São Paulo, São José dos Campos, São Paulo, Brazil

    Vinícius Veloso de Melo, Álvaro Luiz Fazenda & Léo Françoso Dal Piccol Sotto

  2. Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 9, 38123, Povo, Italy

    Giovanni Iacca

Authors
  1. Vinícius Veloso de Melo
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  2. Álvaro Luiz Fazenda
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  3. Léo Françoso Dal Piccol Sotto
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  4. Giovanni Iacca
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Corresponding author

Correspondence to Giovanni Iacca .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Pedro A. Castillo

  2. Université Le Havre Normandie, Le Havre, France

    Juan Luis Jiménez Laredo

  3. Universidad de Extremadura, Mérida, Spain

    Francisco Fernández de Vega

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de Melo, V.V., Fazenda, Á.L., Sotto, L.F.D.P., Iacca, G. (2020). A MIMD Interpreter for Genetic Programming. In: Castillo, P.A., Jiménez Laredo, J.L., Fernández de Vega, F. (eds) Applications of Evolutionary Computation. EvoApplications 2020. Lecture Notes in Computer Science(), vol 12104. Springer, Cham. https://doi.org/10.1007/978-3-030-43722-0_41

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  • DOI: https://doi.org/10.1007/978-3-030-43722-0_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-43721-3

  • Online ISBN: 978-3-030-43722-0

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