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Re-optimizing the Performance of Shortest Path Queries Using Parallelized Combining Speedup Technique based on Bidirectional Arc flags and Multilevel Approach

机译:基于双向弧标记和多级方法的并行组合加速技术重新优化最短路径查询的性能

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摘要

Globally shortest path problems have increasing demand due to voluminous datasets in applications like roadmaps, web search engines, mobile data sets, etc., Computing shortest path between nodes in a given directed graph is a very common problem. Among the various shortest path algorithms, Dijkstra's shortest path algorithm [1] is said to have better performance with regard to run time than the other algorithms. The output of Dijkstra's shortest path algorithm can be improved with speedup techniques. In this paper a new combined speedup technique based on three speedup techniques were combined and each technique is parallelised individually and the performance of the combination is measured with respect to preprocessing time, runtime and number of nodes visited in random graphs, planar graphs and real world data sets.
机译:由于路线图,Web搜索引擎,移动数据集等应用程序中的大量数据集,全球最短路径问题的需求不断增长。在给定有向图中计算节点之间的最短路径是一个非常普遍的问题。在各种最短路径算法中,Dijkstra的最短路径算法[1]在运行时间方面具有比其他算法更好的性能。 Dijkstra最短路径算法的输出可以使用加速技术进行改进。在本文中,结合了一种基于三种加速技术的新组合加速技术,每种技术分别进行了并行化,并根据预处理时间,运行时间和在随机图,平面图和现实世界中访问的节点数来衡量组合的性能数据集。

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