Parallelization offers the opportunity to accelerate search on constraint satisfaction problems. To parallelize a sequential solver under a popular message passing protocol, the new paradigm described here combines portfolio-based methods and search space splitting. To split effectively and to balance processor workload, this paradigm adaptively exploits knowledge acquired during search and allocates additional resources to the most difficult parts of a problem. Extensive experiments in a parallel environment show that this paradigm significantly improves the performance of an underlying sequential solver, outperforms more naive approaches to parallelization, and solves many difficult problems left open after recent solver competitions. © 2012 Springer-Verlag.
CITATION STYLE
Yun, X., & Epstein, S. L. (2012). A hybrid paradigm for adaptive parallel search. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7514 LNCS, pp. 720–734). https://doi.org/10.1007/978-3-642-33558-7_52
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