Over the years, parallel SAT solving becomes more and more important. However, most of state-of-the-art parallel SAT solvers are portfolio-based ones. They aim at running several times the same solver with different parameters. In this paper, we propose a tool called Dolius, mainly based on the divide and conquer paradigm. In contrast to most current parallel efficient engines, Dolius does not need shared memory, can be distributed, and scales well when a large number of computing units is available. Furthermore, our tool contains an API allowing to plug any SAT solver in a simple way.

Managing learnt clause database is known to be a tricky task in SAT solvers. In the portfolio framework, the collaboration between threads through learnt clause exchange makes this problem even more difficult to tackle. Several techniques have been proposed in the last few years, but practical results are still in favor of very limited collaboration, or even no collaboration at all. This is mainly due to the difficulty that each thread has to manage a large amount of learnt clauses generated by the other workers. In this paper, we propose new efficient techniques for clause exchanges within a parallel SAT solver. In contrast to most of the current clause exchange methods, our approach relies on both export and import policies, and makes use of recent techniques that proves very effective in the sequential case. Extensive experimentations show the practical interest of the proposed ideas.

Most of state-of-the-art parallel SAT solvers are portfolio-based ones. They aim at running several times the same solver with different parameters. In this paper, we propose a solver called Dolius, based on the divide and conquer paradigm. In contrast to most current parallel efficient engines, Dolius does not need shared memory, can be distributed, and scales well when a large number of computing units is available.