DISTRIBUTED AGENT-BASED SIMULATION WITH REPAST4PY.

Nicholson Collier, Jonathan Ozik
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引用次数: 2

Abstract

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.

使用repast4py进行分布式代理仿真。
高性能计算(HPC)的日益普及加速了应用计算模拟来捕捉大型复杂系统的更细粒度特征的潜力。本教程介绍Repast套件中基于代理的建模工具包的最新成员Repast4Py。Repast4Py是一个基于Python代理的建模框架,它提供了构建跨多个处理核心的大型、mpi分布式基于代理的模型(ABM)的能力。reast4py简化了构建大规模ABM的过程,旨在为来自不同科学界的研究人员提供一个更容易的入口,以应用分布式ABM方法。我们将介绍关键的Repast4Py组件,以及如何将它们组合起来创建不同类型的分布式模拟,构建在实现七个常见分布式ABM用例的三个示例模型上。我们试图说明模型结构和性能考虑之间的关系,并就如何利用Repast4Py特性来开发设计良好且性能良好的分布式abm提供指导。
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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CiteScore
1.30
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