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Exploiting GPUs in Solving (Distributed) Constraint Optimization Problems with Dynamic Programming

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Principles and Practice of Constraint Programming (CP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9255))

Abstract

This paper proposes the design and implementation of a dynamic programming based algorithm for (distributed) constraint optimization, which exploits modern massively parallel architectures, such as those found in modern Graphical Processing Units (GPUs). The paper studies the proposed algorithm in both centralized and distributed optimization contexts. The experimental analysis, performed on unstructured and structured graphs, shows the advantages of employing GPUs, resulting in enhanced performances and scalability.

This research is partially supported by the National Science Foundation under grant number HRD-1345232. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the sponsoring organizations, agencies, or the U.S. government.

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Author information

Authors and Affiliations

  1. Department of Computer Science, New Mexico State University, Las Cruces, NM, USA

    Ferdinando Fioretto, Tiep Le, Enrico Pontelli, William Yeoh & Tran Cao Son

  2. Department of Mathematics and Computer Science, University of Udine, Udine, Italy

    Ferdinando Fioretto

Authors
  1. Ferdinando Fioretto
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  2. Tiep Le
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  3. Enrico Pontelli
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  4. William Yeoh
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  5. Tran Cao Son
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Corresponding author

Correspondence to Ferdinando Fioretto .

Editor information

Editors and Affiliations

  1. École Polytechnique de Montréal, Montréal, Québec, Canada

    Gilles Pesant

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Fioretto, F., Le, T., Pontelli, E., Yeoh, W., Son, T.C. (2015). Exploiting GPUs in Solving (Distributed) Constraint Optimization Problems with Dynamic Programming. In: Pesant, G. (eds) Principles and Practice of Constraint Programming. CP 2015. Lecture Notes in Computer Science(), vol 9255. Springer, Cham. https://doi.org/10.1007/978-3-319-23219-5_9

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  • DOI: https://doi.org/10.1007/978-3-319-23219-5_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23218-8

  • Online ISBN: 978-3-319-23219-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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