SC20 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

PREEMPT: Scalable Epidemic Interventions Using Submodular Optimization on Multi-GPU Systems

Authors: Marco Minutoli (Pacific Northwest National Laboratory (PNNL)); Prathyush Sambaturu (University of Virginia); Mahantesh Halappanavar (Pacific Northwest National Laboratory (PNNL), Pacific Northwest National Laboratory); Antonino Tumeo (Pacific Northwest National Laboratory (PNNL)); Ananth Kalyanaraman (Washington State University); and Anil Vullikanti (University of Virginia)

Abstract: Preventing and slowing the spread of epidemics are achieved through techniques such as vaccination and social distancing. Given practical limitations on the number of vaccines and the cost of administration, optimization becomes a necessity. Previous approaches using mathematical programming methods have been shown to be effective but are limited by computational costs. In this work, we make several contributions: First, we present a new approach to intervention via maximizing the influence of vaccinated nodes on the network. We call this method PREEMPT. Next, we present a new parallel algorithm for PREEMPT, along with a CPU-GPU implementation. Our results demonstrate that PREEMPT is able to achieve significant speedups on Summit while improving the quality of solutions. This work represents a first-of-its-kind effort in parallelizing greedy hill climbing and applying it toward devising effective interventions for epidemics.

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