基于大型合成种群的复杂模拟器负载平衡方案

2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) Pub Date : 2021-06-01 DOI:10.1109/IPDPSW52791.2021.00156

Bogdan Mucenic, Chaitanya Kaligotla, Abby Stevens, J. Ozik, Nicholson T. Collier, C. Macal

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引用次数: 2

摘要

我们提出了负载平衡算法的发展，以有效地分布和并行运行大规模复杂基于代理的建模(ABM)模拟器在高性能计算(HPC)资源。我们的算法基于从ABM的底层合成种群中产生的共定位网络的分区。该算法的变体被实验应用于研究影响运行时性能的两个因素的算法选择。我们在CityCOVID ABM上报告了这些实验结果，该模型旨在模拟COVID-19在芝加哥大都会地区的传播。

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Load Balancing Schemes for Large Synthetic Population-Based Complex Simulators

We present our development of load balancing algorithms to efficiently distribute and parallelize the running of large-scale complex agent-based modeling (ABM) simulators on High-Performance Computing (HPC) resources. Our algorithm is based on partitioning the co-location network that emerges from an ABM’s underlying synthetic population. Variations of this algorithm are experimentally applied to investigate algorithmic choices on two factors that affect run-time performance. We report these experiments’ results on the CityCOVID ABM, built to model the spread of COVID-19 in the Chicago metropolitan region.

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来源期刊

2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

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