Load-balancing methods for parallel and distributed constraint solving

Program parallelization and distribution becomes increasingly important when new multi-core architectures and cheaper cluster technology provide ways to improve performance. Using declarative languages, such as constraint programming, can make the transition to parallelism easier for the programmer. In this paper, we address parallel and distributed search in constraint programming (CP) by proposing several load-balancing methods. We show how these methods improve the execution-time scalability of constraint programs. Scalability is the greatest challenge of parallelism and it is particularly an issue in constraint programming, where load-balancing is difficult. We address this problem by proposing CP-specific load-balancing methods and evaluating them on a cluster by using benchmark problems. Our experimental results show that the methods behave differently well depending on the type of problem and the type of search. This gives the programmer the opportunity to optimize the performance for a particular problem.