The sudden onset of the COVID-19 global health crisis and as-sociated economic and social fall-out has highlighted the im-portance of speed in modeling emergency scenarios so that ro-bust, 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. Popular and ac-cessible agent-based modeling platforms such as NetLogo can be fast to develop, but slow to run when exploring broad param-eter spaces on individual workstations. However, while deploy-ment 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. 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 ad-ditional support for deploying their models on such infrastruc-ture. We show that model run-time speed improvements of be-tween 200x and 400x over desktop machines are possible using 1) a benchmark ‘wolf-sheep predation’ model in addition to 2) an example drawn from our own work modeling the spread of COVID-19 in Victoria, Australia. We describe how a focus on improving model speed is non-trivial for model development and discuss its practical importance for improved policy and de-cision-making in the real world. We provide all associated doc-umentation in a linked git repository.
Improving speed of models for improved real-world decision-making