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.
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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
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DOI: https://doi.org/10.1007/978-3-319-07046-9_27
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