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首页> 外文期刊>Proceedings of the Workshop on Principles of Advanced and Distributed Simulation >Explicit Spatial Scattering for Load Balancing in Conservatively Synchronized Parallel Discrete-Event Simulations
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Explicit Spatial Scattering for Load Balancing in Conservatively Synchronized Parallel Discrete-Event Simulations

机译:保守同步并行离散事件模拟中用于负载平衡的显式空间散射

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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 - we observe that scatter partitioning, even with its greatly increased messaging overhead, 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.
机译:我们将重新检查在高性能计算群集上执行的保守同步并行,离散事件模拟中的负载平衡问题,重点是计算和消息负载倾向于在空间上聚类的模拟。这样的域通常以地理“热点”的存在为特征-这些区域产生的模拟事件比其他区域多得多。这样的领域的示例包括城市区域,交通网络和实体之间的交互通常受物理邻近性约束的网络的仿真。注意在保守同步并行仿真中,仿真的执行速度由最慢(即最重负载)的仿真过程确定,我们研究了在空间上群集负载的域中实现处理器负载均衡分配的不同分区策略。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,附近的实体被明确分配给不同的处理器,从而将负载分散到整个群集中。这是由两个观察结果引起的,即,(i)由于负载在空间上是聚类的,因此空间分散应该直观地将负载分散到整个计算群集中,并且(ii)在并行模拟中,CPU负载的公平分配是确定负载的更大决定因素。执行速度比消息传递开销大。通过大型仿真实验(包括抽象的仿真模型和真实的仿真模型),我们观察到分散分区(即使其消息传递开销大大增加)也明显优于试图减少消息传递开销的更传统的空间划分技术。此外,即使热点在模拟过程中发生变化,如果保留了空间聚类的基本特征,则负载仍将继续与空间散射保持平衡,这使我们得出以下结论:空间散射通常可以消除对动态负载平衡的需求。

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