分布式仿真中同步事件的高效分析

Patrick Peschlow, P. Martini
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引用次数: 17

摘要

同时事件是分布式离散事件仿真的一个基本挑战:不同的执行顺序可能导致不同的仿真结果。通常,同时发生的事件通过打破规则来处理,以保证再现性。然而,第二种方法是使用分支机制检查不同的执行顺序。通过分析同步事件的影响,可以提高仿真结果的可信度。自然地,分支可能会因为大量同时发生的事件而变得昂贵。因此,为了成为一种实用的方法,分支的效率是至关重要的。在本文中,我们提出了分布式仿真中并发事件的有效分支机制,该机制可以与打破规则结合使用,也可以作为打破规则的替代方案。我们应用的技术优化了同时事件的检测,并尽可能地减少了分支的数量。此外,我们使用克隆在不同的分支之间共享计算。
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Efficient Analysis of Simultaneous Events in Distributed Simulation
Simultaneous events are a fundamental challenge in distributed discrete-event simulation: Different execution orders may lead to different simulation results. Commonly, simultaneous events are handled by tie-breaking rules in order to guarantee reproducibility. A second approach, however, is examining different execution orders with a branching mechanism. By analyzing the effects of simultaneous events, confidence in simulation results may be increased. Naturally, branching may get expensive with large numbers of simultaneous events. Thus, efficiency is crucial for branching in order to be a practical method. In this paper, we present an efficient branching mechanism for simultaneous events in distributed simulation, which may be used in conjunction with, or as an alternative to tie-breaking rules. We apply techniques which optimize the detection of simultaneous events and reduce the number of branches as much as possible. Furthermore, we use cloning to share computations among different branches.
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