A Survey of Parallel A*
This is an incremental survey for researchers and practitioners in AI and high-performance computing, summarizing existing methods without introducing new techniques.
The paper surveys parallel A* algorithms, addressing the memory limitations of serial implementations by exploring distributed memory approaches that enable solving larger problems.
A* is a best-first search algorithm for finding optimal-cost paths in graphs. A* benefits significantly from parallelism because in many applications, A* is limited by memory usage, so distributed memory implementations of A* that use all of the aggregate memory on the cluster enable problems that can not be solved by serial, single-machine implementations to be solved. We survey approaches to parallel A*, focusing on decentralized approaches to A* which partition the state space among processors. We also survey approaches to parallel, limited-memory variants of A* such as parallel IDA*.