Best-firs t heuristic search for multicore machines

To harness modern multicore processors, it is imperative to d velop parallel versions of fundamental algorithms. In this paper, we compare different appr oaches to parallel best-first search in a shared-memory setting. We present a new method, PBNF, that u ses abstraction to partition the state space and to detect duplicate states without requiring freq uent locking. PBNF allows speculative expansions when necessary to keep threads busy. We identify a d fix potential livelock conditions in our approach, proving its correctness using temporal log ic. Our approach is general, allowing it to extend easily to suboptimal and anytime heuristic search . In an empirical comparison on STRIPS planning, grid pathfinding, and sliding tile puzzle problem s using 8-core machines, we show that A*, weighted A* and Anytime weighted A* implemented using PB NF yield faster search than improved versions of previous parallel search proposals.

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