ManySat is a DPLL-engine which includes all the classical features like twowatched-literal, unit propagation, activity-based decision heuristics, lemma deletion strategies, and clause learning. In addition to the classical first-UIP scheme, it incorporates a new technique which extends the classical implication graph used during conflict-analysis to exploit the satisfied clauses of a formula . When designing ManySat we decided to take advantage of the main weakness of modern DPLLs: their sensitivity to parameter tuning. For instance, changing parameters related to the restart strategy or to the variable selection heuristic can completely change the performance of a solver on a particular problem. In a multi-threading context, we can easily take advantage of this lack of robustness by designing a system which will run different incarnation of a core DPLL-engine on a particular problem. Each incarnation would exploit a particular parameter set and their combination should represent a set of orthogonal strategies. The following components where used to differentiate each strategies: – Variable selectio
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