A Graph Partitioning Algorithm for Parallel Agent-Based Road Traffic Simulation

Yadong Xu, Wentong Cai, D. Eckhoff, Suraj Nair, A. Knoll
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引用次数: 13

Abstract

A common approach of parallelising an agent-based road traffic simulation is to partition the road network into sub-regions and assign computations for each subregion to a logical process (LP). Inter-process communication for synchronisation between the LPs is one of the major factors that affect the performance of parallel agent-based road traffic simulation in a distributed memory environment. Synchronisation overhead, i.e., the number of messages and the communication data volume exchanged between LPs, is heavily dependent on the employed road network partitioning algorithm. In this paper, we propose Neighbour-Restricting Graph-Growing (NRGG), a partitioning algorithm which tries to reduce the required communication between LPs by minimising the number of neighbouring partitions. Based on a road traffic simulation of the city of Singapore, we show that our method not only outperforms graph partitioning methods such as METIS and Buffoon, for the synchronisation protocol used, but also is more resilient than stripe spatial partitioning when partitions are cut more ?nely.
一种基于并行agent的道路交通仿真图划分算法
并行化基于智能体的道路交通仿真的一种常见方法是将道路网络划分为子区域,并将每个子区域的计算分配给逻辑进程(LP)。进程间通信是影响分布式内存环境下基于并行代理的道路交通仿真性能的主要因素之一。同步开销,即lp之间交换的消息数量和通信数据量,严重依赖于所采用的路网划分算法。在本文中,我们提出了邻居限制图生长(NRGG),这是一种分区算法,它试图通过最小化相邻分区的数量来减少lp之间所需的通信。基于新加坡城市的道路交通模拟,我们表明我们的方法不仅优于METIS和Buffoon等图分区方法,对于所使用的同步协议,而且当分区被更严格地切割时,也比条纹空间分区更具弹性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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