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Efficient parallel simulation of spatially-explicit agent-based epidemiological models

机译:基于空间显式病原体的流行病学模型的高效并行模拟

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Agent-based approaches enable simulation driven analysis and discovery of system-level properties using descriptive models of known behaviors of the entities constituting the system. Accordingly, a spatially-explicit agent-based ecological modeling, parallel simulation, and analysis environment called SEARUMS has been developed. However, the conservatively synchronized parallel simulation infrastructure of SEARUMS did not scale effectively. Furthermore, the initial multithreaded shared-memory design prevented utilization of resources on multiple compute nodes of a distributed memory cluster. Consequently, the simulation infrastructure of SEARUMS was redesigned to operate as a Time Warp synchronized parallel and distributed discrete event simulation (PDES) on modern distributed-memory supercomputing platforms. The new PDES environment is called SEARUMS++. The spatially-explicit nature of the models posed several challenges in achieving scalable and efficient PDES, necessitating new approaches in SEARUMS++ for: ① modeling spatial interactions and initial partitioning of agents, ② logical migration of an agent during simulation using proxy agents to reflect migratory characteristics, and ③ ghosting of agents using multiple proxy agents to handle boundary cases that occur during logical migration of agents. This article presents our optimization efforts involving new methods to address aforementioned challenges. The design of SEARUMS++ and experimental evaluation of various alternatives that were explored to achieve scalable and efficient PDES are also discussed. Our experiments indicate that SEARUMS++ provides 200% performance improvement and maintains scalability to a larger number of processors, thus enabling efficient parallel simulation of spatially-explicit agent-based epidemiological models.
机译:基于代理的方法可以使用构成系统的实体的已知行为的描述模型,进行仿真驱动的分析和系统级属性的发现。因此,已经开发了一种基于空间显式代理的生态建模,并行模拟和分析环境,称为SEARUMS。但是,SEARUMS的保守同步并行模拟基础结构无法有效扩展。此外,最初的多线程共享内存设计阻止了分布式内存集群的多个计算节点上资源的利用。因此,SEARUMS的仿真基础结构经过重新设计,可以在现代分布式内存超级计算平台上作为时间扭曲同步并行和分布式离散事件仿真(PDES)进行操作。新的PDES环境称为SEARUMS ++。模型的空间显式性质对实现可扩展和高效的PDES提出了一些挑战,因此需要在SEARUMS ++中使用新方法来进行以下操作:①对空间交互和代理的初始分区进行建模,②在模拟过程中使用代理代理反映迁移特征的代理逻辑迁移③代理的重影使用多个代理代理来处理在代理逻辑迁移期间发生的边界情况。本文介绍了我们的优化工作,其中涉及解决上述挑战的新方法。还讨论了SEARUMS ++的设计以及为实现可扩展和高效的PDES而探索的各种替代方案的实验评估。我们的实验表明SEARUMS ++可以提高200%的性能,并保持可扩展到更多处理器的能力,从而可以对基于空间显式代理的流行病学模型进行有效的并行仿真。

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