A Template for Transfer of NetLogo Models to High-Performance Computing Environments for Enhanced Real-World Decision-Support

Jason Thompson, Haifeng Zhao, Sachith Seneviratne, Rohan Byrne, Rajith Vidanaarachchi, Roderick McClure

Research output: Chapter in Book / Conference PaperConference Paperpeer-review

Abstract

The sudden onset of the COVID-19 global health crisis and associated economic and social fall-out has highlighted the importance of speed in modeling emergency scenarios so that robust, reliable evidence can be placed in policy and decision-makers’ hands as swiftly as possible. For computational social scientists who are building complex policy models but who lack ready access to high-performance computing facilities, such time-pressure can hinder effective engagement with end-users. Popular and accessible agent-based modeling platforms in computational social science such as NetLogo can make models fast to develop, but slow to run when exploring broad parameter spaces on individual workstations. However, while deployment on high-performance computing (HPC) clusters can achieve marked performance improvements, transferring models from workstations to HPC clusters can also be a technically challenging and time-consuming task for social scientists or those from non computer science-related backgrounds. In this paper we present a set of generic templates that can be used and adapted by NetLogo users who have access to HPC clusters but require additional support for deploying their models on such infrastructure. We show how model run-time speed improvements of between 200× and 400× over desktop machines are possible using (1) a benchmark ‘wolf-sheep predation’ model in addition to (2) an example drawn from our own applied policy modeling work surrounding COVID-19 management settings for Government in Australia. We describe how a focus on improving model speed is a non-trivial concern for model developers in the social sciences and discuss its practical importance for improved policy and decision-making in the real world. We provide all associated documentation in a linked git repository.

Original languageEnglish
Title of host publicationAdvances in Social Simulation - Proceedings of the 17th Social Simulation Conference, European Social Simulation Association
EditorsFlaminio Squazzoni
PublisherSpringer Science and Business Media B.V.
Pages567-576
Number of pages10
ISBN (Print)9783031349195
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event17th annual conference of European Social Simulation Association, ESSA 2022 - Milan, Italy
Duration: 12 Sept 202216 Sept 2022

Publication series

NameSpringer Proceedings in Complexity
ISSN (Print)2213-8684
ISSN (Electronic)2213-8692

Conference

Conference17th annual conference of European Social Simulation Association, ESSA 2022
Country/TerritoryItaly
CityMilan
Period12/09/2216/09/22

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

  • Agent-Based model
  • Decision-Support
  • High performance computing
  • Policy

Fingerprint

Dive into the research topics of 'A Template for Transfer of NetLogo Models to High-Performance Computing Environments for Enhanced Real-World Decision-Support'. Together they form a unique fingerprint.

Cite this