  A conflict graph represents logical relations between binary variables, and
effective use of the graph can significantly accelerate branch-and-cut solvers
for mixed-integer programming (MIP). In this paper we develop efficient
parallel conflict graph management: conflict detection; maximal clique
generation; clique extension; and clique merging. We leverage parallel
computing in order to intensify computational effort on the conflict graph,
thereby generating a much larger pool of cutting planes than what can be
practically achieved in serial. Computational experiments demonstrate that the
expanded pool of cuts enabled by parallel computing lead to substantial
reductions in total MIP solve time, especially for more challenging cases.
