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GPU for Monte Carlo Search

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Learning and Intelligent Optimization (LION 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14286))

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Abstract

Monte Carlo Search algorithms can give excellent results for some combinatorial optimization problems and for some games. They can be parallelized efficiently on high-end CPU servers. Nested Monte Carlo Search is an algorithm that parallelizes well. We take advantage of this property to obtain large speedups running it on low cost GPUs. The combinatorial optimization problem we use for the experiments is the Snake-in-the-Box. It is a graph theory problem for which Nested Monte Carlo Search previously improved lower bounds. It has applications in electrical engineering, coding theory, and computer network topologies. Using a low cost GPU, we obtain speedups as high as 420 compared to a single CPU.

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Authors and Affiliations

  1. LIGM, Universite Gustave Eiffel, CNRS, 77454, Marne-la-Vallee, France

    Lilian Buzer

  2. LAMSADE, Universite Paris Dauphine - PSL, CNRS, Paris, France

    Tristan Cazenave

Authors
  1. Lilian Buzer
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  2. Tristan Cazenave
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Corresponding author

Correspondence to Lilian Buzer .

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Editors and Affiliations

  1. InsideOpt, Dover, DE, USA

    Meinolf Sellmann

  2. Bielefeld University, Bielefeld, Germany

    Kevin Tierney

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Buzer, L., Cazenave, T. (2023). GPU for Monte Carlo Search. In: Sellmann, M., Tierney, K. (eds) Learning and Intelligent Optimization. LION 2023. Lecture Notes in Computer Science, vol 14286. Springer, Cham. https://doi.org/10.1007/978-3-031-44505-7_13

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  • DOI: https://doi.org/10.1007/978-3-031-44505-7_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-44504-0

  • Online ISBN: 978-3-031-44505-7

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