  Recent work introduced the cube-and-conquer technique to solve hard SAT
instances. It partitions the search space into cubes using a lookahead solver.
Each cube is tackled by a conflict-driven clause learning (CDCL) solver.
Crucial for strong performance is the cutoff heuristic that decides when to
switch from lookahead to CDCL. Yet, this offline heuristic is far from ideal.
In this paper, we present a novel hybrid solver that applies the cube and
conquer steps simultaneously. A lookahead and a CDCL solver work together on
each cube, while communication is restricted to synchronization. Our concurrent
cube-and-conquer solver can solve many instances faster than pure lookahead,
pure CDCL and offline cube-and-conquer, and can abort early in favor of a pure
CDCL search if an instance is not suitable for cube-and-conquer techniques.

Data Structures and Algorithms;Artificial Intelligence