In the domain of propositional Satisfiability Problem (SAT), parallel portfolio-based algorithms have become a standard methodology for both complete and incomplete solvers. In this methodology several algorithms explore the search space in parallel, either independently or cooperatively with some communication between the solvers. We conducted a study of the scalability of several SAT solvers in different application domains (crafted, verification, quasigroups and random instances) when drastically increasing the number of cores in the portfolio, up to 512 cores. Our experiments show that on different problem families the behaviors of different solvers vary greatly. We present an empirical study that suggests that the best sequential solver is not necessary the one with the overall best parallel speedup.
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