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UID:submissions.supercomputing.org_SC20_sess158_pap510@linklings.com
SUMMARY:PREEMPT: Scalable Epidemic Interventions Using Submodular Optimiza
 tion on Multi-GPU Systems
DESCRIPTION:Paper\n\nPREEMPT: Scalable Epidemic Interventions Using Submod
 ular Optimization on Multi-GPU Systems\n\nMinutoli, Sambaturu, Halappanava
 r, Tumeo, Kalyanaraman...\n\nPreventing and slowing the spread of epidemic
 s are achieved through techniques such as vaccination and social distancin
 g. Given practical limitations on the number of vaccines and the cost of a
 dministration, 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 contribution
 s: First, we present a new approach to intervention via maximizing the inf
 luence of vaccinated nodes on the network. We call this method PREEMPT. Ne
 xt, we present a new parallel algorithm for PREEMPT, along with a CPU-GPU 
 implementation. Our results demonstrate that PREEMPT is able to achieve si
 gnificant speedups on Summit while improving the quality of solutions. Thi
 s work represents a first-of-its-kind effort in parallelizing greedy hill 
 climbing and applying it toward devising effective interventions for epide
 mics.\n\nTag: Accelerators, FPGA, and GPUs, Algorithms, Graph Algorithms, 
 Scalable Computing\n\nRegistration Category: Tech Program Reg Pass
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