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Optimizing Massively Parallel Simulations of Infection Spread Through Air-Travel for Policy Analysis
Conference proceeding   Peer reviewed

Optimizing Massively Parallel Simulations of Infection Spread Through Air-Travel for Policy Analysis

Ashok Srinivasan, C D Sudheer and Sirish Namilae
2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp.136-145
IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), 16th (Cartagena, Colombia , 05/16/2016–05/19/2016)
01/01/2016
DOI: https://doi.org/10.1109/CCGrid.2016.23
Web of Science ID: WOS:000382529800017

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7 Times Cited - Web of Science

Abstract

Project VIPRA [1] uses a new approach to modeling the potential spread of infections in airplanes, which involves tracking detailed movements of individual passengers. Inherent uncertainties are parameterized, and a parameter sweep carried out in this space to identify potential vulnerabilities. Simulation time is a major bottleneck for exploration of 'what-if' scenarios in a policy-making context under real-world time constraints. This paper identifies important bottlenecks to e cient computation: ine ciency in workflow, parallel IO, and load imbalance. Our solutions to the above problems include modifying the workflow, optimizing parallel IO, and a new scheme to predict computational time, which leads to e cient load balancing on fewer nodes than currently required. Our techniques reduce the computational time from several hours on 69,000 cores to around 20 minutes on around 39,000 cores on the Blue Waters machine for the same computation. The significance of this paper lies in identifying performance bottlenecks in this class of applications, which is crucial to public health, and presenting a solution that is e ective in practice.

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