Parallel Discrepancy-Based Search: An Efficient and Scalable Search Strategy for Massively Parallel Supercomputers Providing Intrinsic Load-Balancing Without Communication
Backtracking strategies based on the computation of discrepancies have proved themselves successful at solving large problems. They show really good performance when provided with a high-quality domain-specific branching heuristic (variable and value ordering heuristic), which is the case for many industrial problems. The authors propose a novel approach (PDS) that allows parallelizing a strategy based on the computation of discrepancies (LDS). The pool of processors visits the leaves in exactly the same order as the centralized algorithm would do. The implementation allows for a natural/intrinsic load balancing to occur (filtering induced by constraint propagation would affect each processor pretty much in the same way), although there is no communication between processors.