Relaxing Synchronization in Parallel Agent-Based Road Traffic Simulation
针对大规模并行智能体交通模拟中同步开销过高的问题，提出了两种利用交通不确定性来放松同步的方法，在不改变统计结果的前提下提升运行效率。
Large-scale agent-based traffic simulation is computationally intensive. Parallel computing can help to speed up agent-based traffic simulation. Parallelization of agent-based traffic simulations is generally achieved by decomposing the road network into subregions. The agents in each subregion are executed by a Logical Process (LP). There are data dependencies between LPs which require synchronization of LPs. An asynchronous protocol allows LPs to progress and communicate asynchronously. LPs use lookahead to indicate the time to synchronize with other LPs. Larger lookahead means less frequent synchronization operations. High synchronization overhead is still a major performance issue of large-scale parallel agent-based traffic simulations. In this article, two methods to increase the lookahead of LPs for an asynchronous protocol are developed. They take advantage of uncertainties in traffic simulation to relax synchronization without altering simulation results statistically. Efficiency of the proposed methods is investigated in the parallel agent-based traffic simulator SEMSim Traffic. Experiment results showed that the proposed methods are able to reduce overall running time of the parallel simulation compared to existing methods.