Abstract
Search strategies such as Limited Discrepancy Search (LDS) and Depth-bounded Discrepancy Search (DDS) find solutions faster than a standard Depth-First Search (DFS) when provided with good value-selection heuristics. We propose a parallelization of DDS: Parallel Depth-bounded Discrepancy Search (PDDS). This parallel search strategy has the property to visit the nodes of the search tree in the same order as the centralized version of the algorithm. The algorithm creates an intrinsic load-balancing: pruning a branch of the search tree equally affects each worker’s workload. This algorithm is based on the implicit assignment of leaves to workers which allows the workers to operate without communication during the search. We present a theoretical analysis of DDS and PDDS. We show that PDDS scales to multiple thousands of workers. We experiment on a massively parallel supercomputer to solve an industrial problem and improve over the best known solution.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
de la Banda, M.G., Stuckey, P.J., Van Hentenryck, P., Wallace, M.: The future of optimization technology. Constraints, 1–13 (2013)
Régin, J.-C., Rezgui, M., Malapert, A.: Embarrassingly parallel search. In: Schulte, C. (ed.) CP 2013. LNCS, vol. 8124, pp. 596–610. Springer, Heidelberg (2013)
Moisan, T., Gaudreault, J., Quimper, C.-G.: Parallel discrepancy-based search. In: Schulte, C. (ed.) CP 2013. LNCS, vol. 8124, pp. 30–46. Springer, Heidelberg (2013)
Harvey, W.D., Ginsberg, M.L.: Limited discrepancy search. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI 1995), pp. 607–613 (1995)
Walsh, T.: Depth-bounded discrepancy search. In: Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI 1997), pp. 1388–1393 (1997)
Perron, L.: Search procedures and parallelism in constraint programming. In: Jaffar, J. (ed.) CP 1999. LNCS, vol. 1713, pp. 346–361. Springer, Heidelberg (1999)
Vidal, V., Bordeaux, L., Hamadi, Y.: Adaptive k-parallel best-first search: A simple but efficient algorithm for multi-core domain-independent planning. In: Proceedings of the Third International Symposium on Combinatorial Search, SOCS 2010 (2010)
Shylo, O.V., Middelkoop, T., Pardalos, P.M.: Restart strategies in optimization: Parallel and serial cases. Parallel Computing 37(1), 60–68 (2010)
Hamadi, Y., Sais, L.: ManySAT: a parallel SAT solver. Journal on Satisfiability, Boolean Modeling and Computation 6, 245–262 (2009)
Xu, L., Hutter, F., Hoos, H.H., Leyton-Brown, K.: Satzilla: Portfolio-based algorithm selection for sat. Journal of Artificial Intelligence Research (JAIR) 32, 565–606 (2008)
Michel, L., See, A., Van Hentenryck, P.: Transparent parallelization of constraint programming. INFORMS Journal on Computing 21, 363–382 (2009)
Chu, G., Schulte, C., Stuckey, P.J.: Confidence-based work stealing in parallel constraint programming. In: Gent, I.P. (ed.) CP 2009. LNCS, vol. 5732, pp. 226–241. Springer, Heidelberg (2009)
Menouer, T., Le Cun, B., Vander-Swalmen, P.: Partitioning methods to parallelize constraint programming solver using the parallel framework Bobpp. In: Nguyen, N.T., van Do, T., Thi, H.A. (eds.) ICCSAMA 2013. SCI, vol. 479, pp. 117–127. Springer, Heidelberg (2013)
Xie, F., Davenport, A.: Massively parallel constraint programming for supercomputers: Challenges and initial results. In: Lodi, A., Milano, M., Toth, P. (eds.) CPAIOR 2010. LNCS, vol. 6140, pp. 334–338. Springer, Heidelberg (2010)
Yun, X., Epstein, S.L.: A hybrid paradigm for adaptive parallel search. In: Milano, M. (ed.) CP 2012. LNCS, vol. 7514, pp. 720–734. Springer, Heidelberg (2012)
Korf, R.E.: Improved limited discrepancy search. In: Proceedings of the 30th National Conference on Artificial Intelligence and the 8th Innovative Applications of Artificial Intelligence Conference, vol. 1, pp. 286–291 (1996)
Beck, J.C., Perron, L.: Discrepancy-bounded depth first search. In: Proceedings of the Second International Workshop on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CP-AI-OR 2000), pp. 8–10 (2000)
Furcy, D., Koenig, S.: Limited discrepancy beam search. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI 2005), pp. 125–131 (2005)
Gaudreault, J., Forget, P., Frayret, J.M., Rousseau, A., Lemieux, S., D’Amours, S.: Distributed operations planning in the lumber supply chain: Models and coordination. International Journal of Industrial Engineering: Theory, Applications and Practice 17 (2010)
Gaudreault, J., Frayret, J.M., Rousseau, A., D’Amours, S.: Combined planning and scheduling in a divergent production system with co-production: A case study in the lumber industry. Computers and Operations Research 38, 1238–1250 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Moisan, T., Quimper, CG., Gaudreault, J. (2014). Parallel Depth-Bounded Discrepancy Search. In: Simonis, H. (eds) Integration of AI and OR Techniques in Constraint Programming. CPAIOR 2014. Lecture Notes in Computer Science, vol 8451. Springer, Cham. https://doi.org/10.1007/978-3-319-07046-9_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-07046-9_27
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07045-2
Online ISBN: 978-3-319-07046-9
eBook Packages: Computer ScienceComputer Science (R0)