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Local consistencies stronger than arc consistency have received a lot ofattention since the early days of CSP research. %because of the strong pruningthey can achieve. However, they have not been widely adopted by CSP solvers.This is because applying such consistencies can sometimes result inconsiderably smaller search tree sizes and therefore in important speed-ups,but in other cases the search space reduction may be small, causing severe runtime penalties. Taking advantage of recent advances in parallelization, wepropose a novel approach for the application of strong local consistencies(SLCs) that can improve their performance by largely preserving the speed-upsthey offer in cases where they are successful, and eliminating the run timepenalties in cases where they are unsuccessful. This approach is presented inthe form of two search algorithms. Both algorithms consist of a master searchprocess, which is a typical CSP solver, and a number of slave processes, witheach one implementing a SLC method. The first algorithm runs the different SLCssynchronously at each node of the search tree explored in the master process,while the second one can run them asynchronously at different nodes of thesearch tree. Experimental results demonstrate the benefits of the proposedmethod.

Exploiting the Pruning Power of Strong Local Consistencies Through Parallelization