Parallel consistency in constraint programming

Program parallelization becomes increasingly important when new multi-core architectures provide ways to improve performance. One of the greatest challenges of this development lies in programming parallel applications. Using declarative languages, such as constraint programming, can make the transition to parallelism easier by hiding the parallelization details in a framework. Automatic parallelization in constraint programming has previously focused on data parallelism. In this paper, we look at task parallelism, specifically the case of parallel consistency. We have developed two models of parallel consistency, one that shares intermediate results and one that does not. We evaluate which model is better in our experiments. Our results show that parallelizing consistency can provide the programmer with a robust scalability for regular problems with global constraints