Collier Nicholson, Ozik Jonathan
Decision and Infrastructure Sciences, Argonne National Laboratory, 9700 S. Cass Avenue, Lemont, IL 60439, USA.
Proc Winter Simul Conf. 2022 Dec;2022:192-206. doi: 10.1109/wsc57314.2022.10015389. Epub 2023 Jan 23.
The increasing availability of high-performance computing (HPC) has accelerated the potential for applying computational simulation to capture ever more granular features of large, complex systems. This tutorial presents Repast4Py, the newest member of the Repast Suite of agent-based modeling toolkits. Repast4Py is a Python agent-based modeling framework that provides the ability to build large, MPI-distributed agent-based models (ABM) that span multiple processing cores. Simplifying the process of constructing large-scale ABMs, Repast4Py is designed to provide an easier on-ramp for researchers from diverse scientific communities to apply distributed ABM methods. We will present key Repast4Py components and how they are combined to create distributed simulations of different types, building on three example models that implement seven common distributed ABM use cases. We seek to illustrate the relationship between model structure and performance considerations, providing guidance on how to leverage Repast4Py features to develop well designed and performant distributed ABMs.
高性能计算(HPC)可用性的不断提高,加速了应用计算模拟来捕捉大型复杂系统中更精细特征的潜力。本教程介绍Repast4Py,它是基于代理的建模工具包Repast套件的最新成员。Repast4Py是一个基于Python的代理建模框架,能够构建跨多个处理核心的大型MPI分布式基于代理的模型(ABM)。Repast4Py旨在简化构建大规模ABM的过程,为来自不同科学领域的研究人员提供更便捷的途径来应用分布式ABM方法。我们将介绍Repast4Py的关键组件,以及它们如何组合以创建不同类型的分布式模拟,并基于实现七个常见分布式ABM用例的三个示例模型进行讲解。我们试图阐明模型结构与性能考量之间的关系,为如何利用Repast4Py的特性来开发设计良好且性能卓越的分布式ABM提供指导。