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

  • Ferdinando FiorettoEmail author
  • Tiep Le
  • Enrico Pontelli
  • William Yeoh
  • Tran Cao Son
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Multiagent System Global Memory Constraint Graph Constraint Optimization Problem Distribute Constraint Optimization Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Abdennadher, S., Schlenker, H.: Nurse scheduling using constraint logic programming. In: Proceedings of the Conference on Innovative Applications of Artificial Intelligence (IAAI), pp. 838–843 (1999)Google Scholar
  2. 2.
    Apt, K.: Principles of constraint programming. Cambridge University Press (2003)Google Scholar
  3. 3.
    Arbelaez, A., Codognet, P.: A GPU implementation of parallel constraint-based local search. In: Proceedings of the Euromicro International Conference on Parallel, Distributed and network-based Processing (PDP), pp. 648–655 (2014)Google Scholar
  4. 4.
    Barabási, A.-L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Boyer, V., El Baz, D., Elkihel, M.: Solving knapsack problems on GPU. Computers & Operations Research 39(1), 42–47 (2012)zbMATHMathSciNetCrossRefGoogle Scholar
  6. 6.
    Brito, I., Meseguer, P.: Improving DPOP with function filtering. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 141–158 (2010)Google Scholar
  7. 7.
    Burke, D., Brown, K.: Efficiently handling complex local problems in distributed constraint optimisation. In: Proceedings of the European Conference on Artificial Intelligence (ECAI), pp. 701–702 (2006)Google Scholar
  8. 8.
    Burke, E.K., De Causmaecker, P., Berghe, G.V., Van Landeghem, H.: The state of the art of nurse rostering. Journal of scheduling 7(6), 441–499 (2004)zbMATHMathSciNetCrossRefGoogle Scholar
  9. 9.
    Campeotto, F., Dovier, A., Fioretto, F., Pontelli, E.: A GPU implementation of large neighborhood search for solving constraint optimization problems. In: Proceedings of the European Conference on Artificial Intelligence (ECAI), pp. 189–194 (2014)Google Scholar
  10. 10.
    Dechter, R.: Bucket elimination: a unifying framework for probabilistic inference. In: Learning in graphical models, pp. 75–104. Springer (1998)Google Scholar
  11. 11.
    Dechter, R.: Constraint Processing. Morgan Kaufmann Publishers Inc., San Francisco (2003)Google Scholar
  12. 12.
    Dechter, R., Pearl, J.: Network-based heuristics for constraint-satisfaction problems. Springer (1988)Google Scholar
  13. 13.
    Farinelli, A., Rogers, A., Petcu, A., Jennings, N.: Decentralised coordination of low-power embedded devices using the Max-Sum algorithm. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 639–646 (2008)Google Scholar
  14. 14.
    Fioretto, F., Le, T., Yeoh, W., Pontelli, E., Son, T.C.: Improving DPOP with branch consistency for solving distributed constraint optimization problems. In: O’Sullivan, B. (ed.) CP 2014. LNCS, vol. 8656, pp. 307–323. Springer, Heidelberg (2014) Google Scholar
  15. 15.
    Gaudreault, J., Frayret, J.-M., Pesant, G.: Distributed search for supply chain coordination. Computers in Industry 60(6), 441–451 (2009)CrossRefGoogle Scholar
  16. 16.
    Hamadi, Y., Bessière, C., Quinqueton, J.: Distributed intelligent backtracking. In: Proceedings of the European Conference on Artificial Intelligence (ECAI), pp. 219–223 (1998)Google Scholar
  17. 17.
    Kumar, A., Faltings, B., Petcu, A.: Distributed constraint optimization with structured resource constraints. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 923–930 (2009)Google Scholar
  18. 18.
    Lalami, M.E., El Baz, D., Boyer, V.: Multi GPU implementation of the simplex algorithm. Proceedings of the International Conference on High Performance Computing and Communication (HPCC) 11, 179–186 (2011)Google Scholar
  19. 19.
    Léauté, T., Faltings, B.: Distributed constraint optimization under stochastic uncertainty. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pp. 68–73 (2011)Google Scholar
  20. 20.
    Maheswaran, R., Tambe, M., Bowring, E., Pearce, J., Varakantham, P.: Taking DCOP to the real world: Efficient complete solutions for distributed event scheduling. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 310–317 (2004)Google Scholar
  21. 21.
    Modi, P., Shen, W.-M., Tambe, M., Yokoo, M.: ADOPT: Asynchronous distributed constraint optimization with quality guarantees. Artificial Intelligence 161(1–2), 149–180 (2005)zbMATHMathSciNetCrossRefGoogle Scholar
  22. 22.
    Montanari, U.: Networks of constraints: Fundamental properties and applications to picture processing. Information sciences 7, 95–132 (1974)zbMATHMathSciNetCrossRefGoogle Scholar
  23. 23.
    Pawłowski, K., Kurach, K., Michalak, T., Rahwan, T.: Coalition structure generation with the graphic processor unit. Technical Report CS-RR-13-07, Department of Computer Science, University of Oxford (2014)Google Scholar
  24. 24.
    Pesant, G.: A regular language membership constraint for finite sequences of variables. In: Proceedings of the International Conference on Principles and Practice of Constraint Programming (CP), pp. 482–495 (2004)Google Scholar
  25. 25.
    Petcu, A., Faltings, B.: A scalable method for multiagent constraint optimization. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 1413–1420 (2005)Google Scholar
  26. 26.
    Quimper, C.-G., Walsh, T.: Global grammar constraints. In: Benhamou, F. (ed.) CP 2006. LNCS, vol. 4204, pp. 751–755. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  27. 27.
    Rodrigues, L.C.A., Magatão, L.: Enhancing supply chain decisions using constraint programming: a case study. In: Gelbukh, A., Kuri Morales, Á.F. (eds.) MICAI 2007. LNCS (LNAI), vol. 4827, pp. 1110–1121. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  28. 28.
    Rossi, F., van Beek, P., Walsh, T. (eds.) Handbook of Constraint Programming. Elsevier (2006)Google Scholar
  29. 29.
    Sanders, J., Kandrot, E.: CUDA by Example. An Introduction to General-Purpose GPU Programming. Addison Wesley (2010)Google Scholar
  30. 30.
    Sultanik, E., Modi, P.J., Regli, W.C.: On modeling multiagent task scheduling as a distributed constraint optimization problem. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 1531–1536 (2007)Google Scholar
  31. 31.
    Trick, M.A.: A dynamic programming approach for consistency and propagation for knapsack constraints. Annals of Operations Research 118(1–4), 73–84 (2003)zbMATHMathSciNetCrossRefGoogle Scholar
  32. 32.
    Yeoh, W., Felner, A., Koenig, S.: BnB-ADOPT: An asynchronous branch-and-bound DCOP algorithm. Journal of Artificial Intelligence Research 38, 85–133 (2010)zbMATHGoogle Scholar
  33. 33.
    Yeoh, W., Yokoo, M.: Distributed problem solving. AI Magazine 33(3), 53–65 (2012)Google Scholar
  34. 34.
    Yokoo, M. (ed.): Distributed Constraint Satisfaction: Foundation of Cooperation in Multi-agent Systems. Springer (2001)Google Scholar
  35. 35.
    Zivan, R., Okamoto, S., Peled, H.: Explorative anytime local search for distributed constraint optimization. Artificial Intelligence 212, 1–26 (2014)zbMATHMathSciNetCrossRefGoogle Scholar
  36. 36.
    Zivan, R., Yedidsion, H., Okamoto, S., Glinton, R., Sycara, K.: Distributed constraint optimization for teams of mobile sensing agents. Journal of Autonomous Agents and Multi-Agent Systems 29(3), 495–536 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ferdinando Fioretto
    • 1
    • 2
    Email author
  • Tiep Le
    • 1
  • Enrico Pontelli
    • 1
  • William Yeoh
    • 1
  • Tran Cao Son
    • 1
  1. 1.Department of Computer ScienceNew Mexico State UniversityLas CrucesUSA
  2. 2.Department of Mathematics and Computer ScienceUniversity of UdineUdineItaly

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