Automatic Constraint Partitioning to Speed Up CLP Execution

Abstract

Speedup in distributed executions of constraint logic programming (CLP) applications are directed related to a good constraint partitioning algorithm. In this work we study different mechanisms to distribute constraints to processors based on straightforward mechanisms such as round-robin and block distribution, and on a more sophisticated automatic distribution method, grouping-sink, that takes into account the connectivity of the constraint network graph. This aims at reducing the communication overhead in distributed environments. Our results show that grouping-sink is, in general, the best alternative for partitioning constraints as it produces results as good or better than round-robin or blocks with low communication rate.