Game-tree search plays an important role in the field of artificial intelligence. In this paper we analyze scalability performance of two parallel game-tree search applications in chess on two shared-memory multiprocessor systems. One is a recently-proposed Parallel Randomized Best-First Minimax search algorithm (PRBFM) in a chess-playing program, and the other is Crafty, a state-of-the-art alpha-beta-based chess-playing program. The analysis shows that the hash-table and dynamic tree splitting operations used in Crafty result in large scalability penalties while PRBFM prevents those penalties by using a fundamentally different search strategy. Our micro-architectural analysis also shows that PRBFM is memory-friendly while Crafty is latency-sensitive and both of them are not bandwidth bound. Although PRBFM is slower than Crafty in sequential performance, it will be much faster than Crafty on middle-scale multiprocessor systems due to its much better scalability. This makes the PRBFM a promising parallel game-tree search algorithm on future large-scale chip multiprocessor systems. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Chen, Y., Tan, Y., Zhang, Y., & Dulong, C. (2007). Performance analysis of two parallel game-tree search applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4699 LNCS, pp. 1105–1114). Springer Verlag. https://doi.org/10.1007/978-3-540-75755-9_128
Mendeley helps you to discover research relevant for your work.