Preventing the spread of infectious diseases requires implementing interventions at various 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. Modeling of epidemic diffusion in large social contact networks requires the use of parallel algorithms and resources. In this work, we present Loimos, a scalable parallel framework for simulating epidemic diffusion. Loimos uses a hybrid of time-stepping and discrete-event simulation to model disease spread, and is implemented on top of an asynchronous, many-task runtime. We demonstrate that Loimos is to able to achieve significant speedups while scaling to large core counts. In particular, Loimos is able to simulate 200 days of a COVID-19 outbreak on a digital twin of California in about 42 seconds, for an average of 4.6 billion traversed edges per second (TEPS), using 4096 cores on Perlmutter at NERSC.

翻译：预防传染病传播需要在各级政府层面实施干预措施，并评估这些预防性措施的潜在影响与效能。基于智能体的建模方法可用于对疫情扩散及潜在干预措施进行精细化研究。在大型社会接触网络中模拟疫情扩散过程，需要运用并行算法与计算资源。本研究提出Loimos——一个可扩展的并行疫情扩散模拟框架。Loimos采用时间步进与离散事件相结合的混合模拟方法对疾病传播进行建模，并构建于异步多任务运行时系统之上。实验证明，Loimos在扩展至大规模核心数时能实现显著的加速效果。具体而言，在NERSC的Perlmutter超算系统上使用4096个核心，Loimos仅需约42秒即可完成加州数字孪生模型中200天的COVID-19疫情模拟，平均每秒可遍历46亿条边（TEPS）。