A distribution method for solving SAT in grids

Abstract. The emerging large-scale computational grid infrastructure is providing an interesting platform for massive distributed computations. In this paper the problem of exploiting such computational grids for solving challenging propositional satisfiability problem (SAT) instances is studied. When designing a distributed algorithm for a large loosely coupled computational grid, a number of grid specific problems need to be tackled including the heterogeneity of the resources, inherent communication delays, and high failure probabilities of grid jobs. In this work a novel distribution method for solving SAT problem instances, called scattering, is introduced. The key advantages of scattering are that it can be used in conjunction with any sequential SAT solver (including industrial black box solvers), the distribution heuristic is strictly separated from the heuristic used in sequential solving, and it requires no communication between processes solving subproblems but still allows coordination of such processes. An implementation of the method has been developed for NorduGrid, a large widely distributed production-level grid running in Scandinavia. The implementation has been benchmarked with test cases including random 3SAT and challenging industrial benchmarks used in previous SAT competitions.

Improving Parallel Local Search for SAT

Abstract. In this work, our objective is to study the impact of knowledge sharing on the performance of portfolio-based parallel local search algorithms. Our work is motivated by the demonstrated importance of clause-sharing in the performance of complete parallel SAT solvers. Unlike complete solvers, state-of-the-art local search algorithms for SAT are not able to generate redundant clauses during their execution. In our settings, each member of the portfolio shares its best configuration (i.e., one which minimizes conflicting clauses) in a common structure. At each restart point, instead of classically generating a random configuration to start with, each algorithm aggregates the shared knowledge to carefully craft a new starting point. We present several aggregation strategies and evaluate them on a large set of problems

Paqube: Distributed qbf solving with advanced knowledge sharing

Abstract. In this paper we present the parallel QBF Solver PaQuBE. This new solver leverages the additional computational power that can be exploited from modern computer architectures, from pervasive multicore boxes to clusters and grids, to solve more relevant instances and faster than previous generation solvers. PaQuBE extends QuBE, its sequential core, by providing a Master/Slave Message Passing Interface (MPI) based design that allows it to split the problem up over an arbitrary number of distributed processes. Furthermore, PaQuBE’s progressive parallel framework is the first to support advanced knowledge sharing in which solution cubes as well as conflict clauses can be shared. According to the last QBF Evaluation, QuBE is the most powerful state-of-the-art QBF Solver. It was able to solve more than twice as many benchmarks as the next best independent solver. Our results here, show that PaQuBE provides additional speedup, solving even more instances, faster