  Learned clauses minimization (LCM) let to performance improvements of modern
SAT solvers especially in solving hard SAT instances. Despite the success of
LCM approaches in sequential solvers, they are not widely incorporated in
parallel SAT solvers. In this paper we explore the potential of LCM for
parallel SAT solvers by defining multiple LCM approaches based on clause
vivification, comparing their runtime in different SAT solvers and discussing
reasons for performance gains and losses. Results show that LCM only boosts
performance of parallel SAT solvers on a fraction of SAT instances. More
commonly applying LCM decreases performance. Only certain LCM approaches are
able to improve the overall performance of parallel SAT solvers.
