PSATO: a distributed propositional prover and its application to quasigroup problems
Journal of Symbolic Computation - Special issue on parallel symbolic computation
Experiments with massively parallel constraint solving
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Parallelizing constraint programs transparently
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Sampling strategies and variable selection in weighted degree heuristics
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Confidence-based work stealing in parallel constraint programming
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Diversification and intensification in parallel SAT solving
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Improving Search Space Splitting for Parallel SAT Solving
ICTAI '10 Proceedings of the 2010 22nd IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Grid-based SAT solving with iterative partitioning and clause learning
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
Algorithm selection and scheduling
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
Algorithm portfolio design: theory vs. practice
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Massively parallel constraint programming for supercomputers: challenges and initial results
CPAIOR'10 Proceedings of the 7th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
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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.