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 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 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.
