Grid-Based SAT Solving with Iterative Partitioning and Clause Learning

This work studies the solving of challenging SAT problem instances in distributed computing environments that have massive amounts of parallel resources but place limits on individual computations. We present an abstract framework which extends a previously presented iterative partitioning approach with clause learning, a key technique applied in modern SAT solvers. In addition we present two techniques that alter the clause learning of modern SAT solvers to fit the framework. An implementation of the proposed framework is then analyzed experimentally using a well-known set of benchmark instances. The results are very encouraging. For example, the implementation is able to solve challenging SAT instances not solvable in reasonable time by state-of-the-art sequential and parallel SAT solvers.

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