Toward parallel search for optimization in graphical models

We introduce a strategy for parallelizing a state-of-the-art sequential search algorithm for optimization on a grid of computers. Based on the AND/OR graph search framework, the procedure exploits the structure of the underlying problem graph. Worker nodes concurrently solve subproblems that are generated by a single master process. Subproblem generation is itself embedded into an AND/OR Branch and Bound algorithm and dynamically takes previous subproblem solutions into account. Drawing upon the underlying graph structure, we provide some theoretical analysis of the parallelization parameters. A prototype has been implemented and we present promising initial experimental results on genetic haplotyping and Mastermind problem instances, at the same time outlining several open questions.