Authors: Joy Kitson and Ian Costello (University of Maryland), Jiangzhuo Chen (University of Virginia), Jaemin Choi (University of Illinois), Stefan Hoops (University of Virginia), Diego Jiménez (Costa Rica National High Technology Center), Tamar Kellner (University of Maryland), Esteban Meneses (Costa Rica National High Technology Center), Henning Mortveit (University of Virginia), Jae-Seung Yeom (Lawrence Livermore National Laboratory), Laxmikant V. Kale (University of Illinois), Madhav V. Marathe (University of Virginia), and Abhinav Bhatele (University of Maryland)
Abstract: Global pandemics can wreak havoc and lead to significant social, economic and personal losses. Preventing the spread of infectious diseases requires interventions at different levels needing the study of potential impact and efficacy of those preemptive measures. Modeling epidemic diffusion and possible interventions can help us in this goal. Agent-based models have been used effectively in the past to model contagion processes. We present Loimos, a highly parallel simulation of epidemic diffusion written on top of the Charm++ asynchronous task-based system. Loimos uses a hybrid time-stepped and discrete-event simulation to model disease spread. We demonstrate that our implementation of Loimos is able to efficiently utilize a large number of cores on different HPC platforms, namely, we scale to about 32k cores on Theta at ALCF and about 4k cores on Cori at NERSC.
Best Poster Finalist (BP): no
Poster summary: PDF
Back to Poster Archive Listing