State space search problems abound in the artificial intelligence, planning and optimization literature. Solving such problems is generally NP-hard, so that a brute-force approach to state space search must be employed. Given the exponential amount of work that state space search problems entail, it is desirable to solve them on large parallel machines with significant computational power. In this paper, we analyze the parallel performance of several classes of state space search applications. In particular, we focus on the issues of grain size, the prioritized execution of tasks and the balancing of load among processors in the system. We demonstrate the corresponding techniques that are used to scale such applications to large scale. Moreover, we tackle the problem of programmer productivity by incorporating these techniques into a general search engine framework designed to solve a broad class of state space search problems. We demonstrate the efficiency and scalability of our design using three example applications, and present scaling results up to 32,768 processors.
An Adaptive Framework for Large-Scale State Space Search
