Overcoming the Scalability Challenges of Epidemic Simulations on Blue Waters

Jae-seung Yeom, Abhinav Bhatele, Keith Bisset, Eric Bohm, Abhishek Gupta, Laxmikant V. Kale, Madhav Marathe, Dimitrios S. Nikolopoulos, Martin Schulz, Lukasz Wesolowski

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Citations (Scopus)
429 Downloads (Pure)


Modeling dynamical systems represents an important application class covering a wide range of disciplines including but not limited to biology, chemistry, finance, national security, and health care. Such applications typically involve large-scale, irregular graph processing, which makes them difficult to scale due to the evolutionary nature of their workload, irregular communication and load imbalance. EpiSimdemics is such an application simulating epidemic diffusion in extremely large and realistic social contact networks. It implements a graph-based system that captures dynamics among co-evolving entities. This paper presents an implementation of EpiSimdemics in Charm++ that enables future research by social, biological and computational scientists at unprecedented data and system scales. We present new methods for application-specific processing of graph data and demonstrate the effectiveness of these methods on a Cray XE6, specifically NCSA's Blue Waters system.
Original languageEnglish
Title of host publication2014 IEEE 28th International Parallel and Distributed Processing Symposium
Place of PublicationWashington, DC, USA
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Print)978-1-4799-3800-1
Publication statusPublished - May 2014


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