A Large-Scale Epidemic Simulation Framework for Realistic Social Contact Networks
CoRR(2024)
摘要
Global pandemics can wreak havoc and lead to significant social, economic,
and personal losses. Preventing the spread of infectious diseases requires
implementing interventions at different levels of government, and evaluating
the potential impact and efficacy of those preemptive measures. Agent-based
modeling can be used for detailed studies of epidemic diffusion and possible
interventions. We present Loimos, a highly parallel simulation of epidemic
diffusion written on top of Charm++, an asynchronous task-based parallel
runtime. Loimos uses a hybrid of time-stepping and discrete-event simulation to
model disease spread. We demonstrate that our implementation of Loimos is able
to scale to large core counts on an HPC system. In particular, Loimos is able
to simulate a US-scale synthetic interaction network in an average of 1.497
seconds per simulation day when executed on 16 nodes on Rivanna at the
University of Virginia, processing around 428 billion interactions
(person-person edges) in under five minutes for an average of 1.4 billion
traversed edges per second (TEPS).
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