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Coing through Rough Times; from Non-Equilibrium Surface Growth to Algorithmic Scalability

机译:渡过艰难时期;从非平衡表面增长到算法可扩展性

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摘要

Efficient and faithful parallel simulation of large asynchronous systems is a challenging computational problem. It requires using the concept of local simulated times and a synchronization scheme. We study the scalability of massively parallel algorithms for discrete-event simulations which employ conservative synchronization to enforce causality. We do this by looking at the simulated time horizon as a complex evolving system, and we identify its universal characteristics. We find that the time horizon for the conservative parallel discrete-event simulation scheme exhibits Kardar-Parisi-Zhang-like kinetic roughening. This implies that the algorithm is asymptotically scalable in the sense that the average progress rate of the simulation approaches a non-zero constant. It also implies, however, that there are diverging memory requirements associated with such schemes.
机译:大型异步系统的有效和忠实的并行仿真是一个具有挑战性的计算问题。它要求使用本地模拟时间和同步方案的概念。我们针对离散事件模拟研究大规模并行算法的可伸缩性,该算法采用保守同步来增强因果关系。为此,我们将模拟时间范围视为一个复杂的演化系统,并确定其普遍特征。我们发现保守并行离散事件模拟方案的时间范围表现出类似Kardar-Parisi-Zhang的动力学粗糙化。这意味着从模拟的平均进度接近非零常数的意义上说,该算法是渐近可伸缩的。但是,这也意味着与这种方案相关的内存需求存在差异。

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