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Conference proceeding
Parallel Low Discrepancy Parameter Sweep for Public Health Policy
2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), pp.291-300
International Symposium on Cluster, Cloud and Grid Computing (CCGRID), 18th (Washington, DC, USA, 05/01/2018–05/04/2018)
01/01/2018
Web of Science ID: WOS:000494275100030
Abstract
Numerical simulations are used to analyze the effectiveness of alternate public policy choices in limiting the spread of infections. In practice, it is usually not feasible to predict their precise impacts due to inherent uncertainties, especially at the early stages of an epidemic. One option is to parameterize the sources of uncertainty and carry out a parameter sweep to identify their robustness under a variety of possible scenarios. The Self Propelled Entity Dynamics (SPED) model has used this approach successfully to analyze the robustness of different airline boarding and deplaning procedures. However, the time taken by this approach is too large to answer questions raised during the course of a decision meeting. In this paper, we use a modified approach that pre-computes simulations of passenger movement, performing only the disease-specific analysis in real time. A novel contribution of this paper lies in using a low discrepancy sequence (LDS) in the parameter sweep, and demonstrating that it can lead to a reduction in analysis time by one to three orders of magnitude over the conventional lattice-based parameter sweep. However, its parallelization suffers from greater load imbalance than the conventional approach. We examine this and relate it to number-theoretic properties of the LDS. We then propose solutions to this problem. Our approach and analysis are applicable to other parameter sweep problems too. The primary contributions of this paper lie in the new approach of low discrepancy parameter sweep and in exploring solutions to challenges in its parallelization, evaluated in the context of an important public health application.
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Details
- Title
- Parallel Low Discrepancy Parameter Sweep for Public Health Policy
- Publication Details
- 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), pp.291-300
- Resource Type
- Conference proceeding
- Conference
- International Symposium on Cluster, Cloud and Grid Computing (CCGRID), 18th (Washington, DC, USA, 05/01/2018–05/04/2018)
- Publisher
- IEEE
- Copyright
- © 2018 IEEE
- Identifiers
- WOS:000494275100030; 99380178126806600
- Academic Unit
- Hal Marcus College of Science and Engineering ; Computer Science
- Language
- English