AAAI Publications, Twenty-Second International Joint Conference on Artificial Intelligence

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Evaluations of Hash Distributed A* in Optimal Sequence Alignment
Yoshikazu Kobayashi, Akihiro Kishimoto, Osamu Watanabe

Last modified: 2011-06-28

Abstract


Hash Distributed A* (HDA*) is a parallel A* algorithm that is proven to be effective in optimal sequential planning with unit edge costs. HDA* leverages the Zobrist function to almost uniformly distribute and schedule work among processors. This paper evaluates the performance of HDA* in optimal sequence alignment. We observe that with a large number of CPU cores HDA* suffers from an increase of search overhead caused by reexpansions of states in the closed list due to nonuniform edge costs in this domain. We therefore present a new work distribution strategy limiting processors to distribute work, thus increasing the possibility of detecting such duplicate search effort. We evaluate the performance of this approach on a cluster of multi-core machines and show that the approach scales well up to 384 CPU cores.

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