Collaborative Research: Simulation of Contagion on Very Large Social Networks with Blue Waters

Keith Bisset, Virginia Polytechnic Institute and State University

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With an increasingly urbanized and mobile population, the likelihood of a worldwide pandemic is increasing. With rising input sizes and strict deadlines for simulation results, e.g., for real-time planning during the outbreak of an epidemic, we must expand the use of high performance computing (HPC) approaches and, in particular, push the boundaries of scalability for this application area. EpiSimdemics simulates diffusion of contagion extremely large and realistic social contact networks: currently of he United States (300 million agents) with the goal of a world-wide network. EpiSimdemics captures dynamics among co-evolving entities. Such applications typically involve large-scale, irregular graph processing, which makes them difficult to scale due to irregular communication, load imbalance, and the evolutionary nature of their workload. We present an implementation of EpiSimdemics in Charm++ running on Blue Waters that enables research by social scientists at unprecedented data and system scales.