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A Dynamic Programming-Based MCMC Framework for Solving DCOPs with GPUs

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Principles and Practice of Constraint Programming (CP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9892))

Abstract

The field of Distributed Constraint Optimization (DCOP) has gained momentum in recent years, thanks to its ability to address various applications related to multi-agent coordination. Nevertheless, solving DCOPs is computationally challenging. Thus, in large scale, complex applications, incomplete DCOP algorithms are necessary. Recently, researchers have introduced a promising class of incomplete DCOP algorithms, based on sampling. However, this paradigm requires a multitude of samples to ensure convergence. This paper exploits the property that sampling is amenable to parallelization, and introduces a general framework, called Distributed MCMC (DMCMC), that is based on a dynamic programming procedure and uses Markov Chain Monte Carlo (MCMC) sampling algorithms to solve DCOPs. Additionally, DMCMC harnesses the parallel computing power of Graphical Processing Units (GPUs) to speed-up the sampling process. The experimental results show that DMCMC can find good solutions up to two order of magnitude faster than other incomplete DCOP algorithms.

This research is partially supported by the National Science Foundation under grants 1345232 and 1550662. 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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Notes

  1. 1.

    It is a distributed variant of Bucket Elimination [2].

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Authors and Affiliations

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

    Ferdinando Fioretto, William Yeoh & Enrico Pontelli

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

    Ferdinando Fioretto

Authors
  1. Ferdinando Fioretto
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  2. William Yeoh
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  3. Enrico Pontelli
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Correspondence to Ferdinando Fioretto .

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Editors and Affiliations

  1. University of Nice Sophia Antipolis , Sophia Antipolis, France

    Michel Rueher

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Fioretto, F., Yeoh, W., Pontelli, E. (2016). A Dynamic Programming-Based MCMC Framework for Solving DCOPs with GPUs. In: Rueher, M. (eds) Principles and Practice of Constraint Programming. CP 2016. Lecture Notes in Computer Science(), vol 9892. Springer, Cham. https://doi.org/10.1007/978-3-319-44953-1_51

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  • DOI: https://doi.org/10.1007/978-3-319-44953-1_51

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