Simulating the Spread of Infectious Disease over Large Realistic Social Networks Using Charm++

Abstract

Preventing and controlling outbreaks of infectious diseases such as pandemic influenza is a top public health priority. EpiSimdemics is an implementation of a scalable parallel algorithm to simulate the spread of contagion, including disease, fear and information, in large (108 individuals), realistic social contact networks using individual-based models. It also has a rich language for describing public policy and agent behavior. We describe CharmSimdemics and evaluate its performance on national scale populations. Charm++ is a machine independent parallel programming system, providing high-level mechanisms and strategies to facilitate the task of developing highly complex parallel applications. Our design includes mapping of application entities to tasks, leveraging the efficient and scalable communication, synchronization and load balancing strategies of Charm++. Our experimental results on a 768 core system show that the Charm++ version achieves up to a 4-fold increase in performance when compared to the MPI version.