Indemics: an interactive data intensive framework for high performance epidemic simulation

  • Authors:
  • Keith R. Bisset;Jiangzhuo Chen;Xizhou Feng;Yifei Ma;Madhav V. Marathe

  • Affiliations:
  • Virginia Tech, Blacksburg, VA;Virginia Tech, Blacksburg, VA;Virginia Tech, Blacksburg, VA and Marquette University, Milwaukee, WI;Virginia Tech, Blacksburg, VA;Virginia Tech, Blacksburg, VA

  • Venue:
  • Proceedings of the 24th ACM International Conference on Supercomputing
  • Year:
  • 2010

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Abstract

To respond to the serious threat of pandemics (e.g. 2009 H1N1 influenza) to human society, we developed Indemics (Interactive Epidemic Simulation), an interactive, data intensive, high performance modeling environment for realtime pandemic planning, situation assessment, and course of action analysis. Indemics was built upon a model of interactive data intensive scientific computation, supporting online interactions between users and simulations and enabling epidemic simulations over detailed social contact networks and realistic representations of complex public policies and intervention strategies. Instead of simply making a highly optimized parallel application run even faster, Indemics introduced several innovative ideas such as online interactive computation and HPC-DBMS integration that significantly improved the functionality, flexibility, modularity, and usability of HPC software. Our performance evaluation suggests that additional computational overhead incurred by Indemics compared to non-interactive simulations is easily offset by its new capabilities. Preliminary results show that Indemics significantly broadens the range of course of action scenarios that can be simulated and enables domain experts to analyze problems that were previously not possible to study.