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Performance evaluation of parallel iterative deepening A~* on clusters of workstations

机译:工作站集群上并行迭代加深A〜*的性能评估

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In this paper we investigate the performance of distributed heuristic search methods based on a well-known heuristic search algorithm, the iterative deepening A~* (IDA~*). The contribution of this paper includes proposing and assessing a distributed algorithm for IDA~*. The assessment is based on space, time and solution quality that are quantified in terms of several performance parameters such as generated search space and real execution time among others. The experiments are conducted on a cluster computer system consisting of 16 hosts built around a general-purpose network. The objective of this research is to investigate the feasibility of cluster computing as an alternative for hosting applications requiring intensive graph search. The results reveal that cluster computing improves on the performance of IDA~* at a reasonable cost.
机译:在本文中,我们研究了基于一种著名的启发式搜索算法(迭代加深A〜*(IDA〜*))的分布式启发式搜索方法的性能。本文的贡献包括提出和评估IDA〜*的分布式算法。评估基于空间,时间和解决方案质量,这些质量根据几个性能参数(例如生成的搜索空间和实际执行时间等)进行量化。实验是在一个群集计算机系统上进行的,该计算机系统由围绕通用网络构建的16个主机组成。这项研究的目的是研究群集计算作为托管需要大量图形搜索的应用程序的替代方案的可行性。结果表明,集群计算以合理的成本提高了IDA〜*的性能。

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