Explicit Spatial Scattering for Load Balancing in Conservatively Synchronized Parallel Discrete Event Simulations

S. Thulasidasan, S. Kasiviswanathan, S. Eidenbenz, Philip Romero
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引用次数: 17

Abstract

We re-examine the problem of load balancing in conservatively synchronized parallel, discrete- event simulations executed on high-performance computing clusters, focusing on simulations where computational and messaging load tend to be spatially clustered. Such domains are frequently characterized by the presence of geographic ``hot-spots'' -- regions that generate significantly more simulation events than others. Examples of such domains include simulation of urban regions, transportation networks and networks where interaction between entities is often constrained by physical proximity. Noting that in conservatively synchronized parallel simulations, the speed of execution of the simulation is determined by the slowest ( i.e most heavily loaded) simulation process, we study different partitioning strategies in achieving equitable processor-load distribution in domains with spatially clustered load. In particular, we study the effectiveness of partitioning via spatial scattering to achieve optimal load balance. In this partitioning technique, nearby entities are explicitly assigned to different processors, thereby scattering the load across the cluster. This is motivated by two observations, namely, (i) since load is spatially clustered, spatial scattering should, intuitively, spread the load across the compute cluster, and (ii) in parallel simulations, equitable distribution of CPU load is a greater determinant of execution speed than message passing overhead. Through large-scale simulation experiments -- both of abstracted and real simulation models -- on high performance clusters, we observe that scatter partitioning -- even with its greatly increased messaging overhead -- often significantly outperforms more conventional spatial partitioning techniques that seek to reduce messaging overhead. Further, even if hot-spots change over the course of the simulation, if the underlying feature of spatial clustering is retained, load continues to be balanced with spatial scattering leading us to the observation that spatial scattering can often obviate the need for dynamic load balancing.
保守同步并行离散事件模拟中负载均衡的显式空间散射
我们重新研究了在高性能计算集群上执行的保守同步并行、离散事件模拟中的负载平衡问题,重点关注计算和消息负载倾向于空间集群的模拟。这些领域通常以地理“热点”的存在为特征,这些区域比其他区域产生更多的模拟事件。这些领域的例子包括城市区域、交通网络和网络的模拟,其中实体之间的相互作用经常受到物理接近的限制。注意到在保守同步并行仿真中,仿真的执行速度由最慢(即负载最重)的仿真进程决定,我们研究了不同的分区策略,以在空间集群负载域中实现公平的处理器负载分配。特别是,我们研究了通过空间散射进行分区以实现最佳负载平衡的有效性。在这种分区技术中,将附近的实体显式地分配给不同的处理器,从而将负载分散到集群中。这是由两个观察结果引起的,即:(i)由于负载在空间上聚集,空间散射应该直观地将负载分散到整个计算集群中,以及(ii)在并行模拟中,CPU负载的公平分配比消息传递开销更能决定执行速度。通过在高性能集群上进行的大规模模拟实验(包括抽象模型和真实模型),我们观察到分散分区——即使其消息传递开销大大增加——通常显著优于寻求减少消息传递开销的更传统的空间分区技术。此外,即使热点在模拟过程中发生变化,如果空间聚类的基本特征被保留,负载继续与空间散射平衡,这导致我们观察到空间散射通常可以消除动态负载平衡的需要。
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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