Boolean satisfiability (SAT) solvers have experienced dramatic im-
provements in their performance and scalability over the last several
years [5, 7] and are now routinely used in diverse EDA applications.
Nevertheless, a number of practical SAT instances remain difficult
to solve  and continue to defy even the best available SAT solv-
ers [5, 7]. Recent work pointed out that symmetries in the Boolean
search space are often to blame. A theoretical framework for detect-
ing and breaking such symmetries was introduced in . This
framework was subsequently extended, refined, and empirically
shown to yield significant speed-ups for a large number of bench-
mark classes in .
Symmetries in the search space are broken by adding appropri-
ate symmetry-breaking predicates (SBPs) to a SAT instance in con-
junctive normal form (CNF). The SBPs prune the search space by
acting as a filter that confines the search to non-symmetric regions
of the space without affecting the satisfiability of the CNF formula.
For symmetry breaking to be effective in practice, the computation-
al overhead of generating and manipulating the SBPs must be sig-
nificantly less than the run time savings they yield due to search