Large-scale agent-based modeling with repast HPC: A case study in parallelizing an agent-based model

Abstract

We present a case study for parallelizing a large-scale epidemiologic ABM developed with Repast HPC, the Chicago Social Interaction Model (chiSIM). The original serial model is a CA-MRSA model which tracks CA-MRSA transmission dynamics and infection in Chicago, and represents the spread of CA-MRSA in the population of Chicago. We utilize both within compute node parallelization using the OpenMP toolkit and distributed parallelization across multiple processes using MPI. The combined approach yields a 1350% increase in run time performance utilizing 128 compute nodes.