Diversification and Intensification in Parallel {SAT} Solving

Long Guo (1) , Youssef Hamadi (2) , Said Jabbour (3) , Lakhdar Saïs (1)

1 CRIL - Centre de Recherche en Informatique de Lens
2 Microsoft - Microsoft Research [Cambridge]
3 MSR - INRIA - Microsoft Research - Inria Joint Centre

In this paper, we explore the two well-known principles of diversification and intensification in portfolio-based parallel SAT solving. These dual concepts play an important role in several search algorithms including local search, and appear to be a key point in modern parallel SAT solvers. To study their trade-off, we define two roles for the computational units. Some of them classified as Masters perform an original search strategy, ensuring diversification. The remaining units, classified as Slaves are there to intensify their master's strategy. Several important questions have to be answered. The first one is what information should be given to a slave in order to intensify a given search effort? The second one is, how often, a subordinated unit has to receive such information? Finally, the question of finding the number of subordinated units along their connections with the search efforts has to be answered. Our results lead to an original intensification strategy which outperforms the best parallel SAT solver, and solves some open SAT instances.

Domaines: Intelligence artificielle [cs.AI]

Long Guo, Youssef Hamadi, Said Jabbour, Lakhdar Saïs. Diversification and Intensification in Parallel {SAT} Solving. 16th International Conference on Principles and Practice of Constraint Programming (CP'10), 2010, United Kingdom. pp.252-265. ⟨hal-00865417⟩