March 2008
Urgent Computing: Exploring Supercomputing's New Role
Karla Atkins
Christopher L. Barrett
Richard Beckman
Keith Bisset
Jiangzhou Chen
Stephen Eubank
Annette Feng
Xizhou Feng
Steven D. Harris
Bryan Lewis
V.S. Anil Kumar
Madhav V. Marathe
Achla Marathe
Henning Mortveit
Paula Stretz, Network Dynamics and Simulation Science Laboratory, Virginia Bio-Informatics Institute, Virginia Polytechnic Institute and State University


These are the people who would show up first for treatment if indeed a chemical or biological attack had occurred. They also would serve as the subpopulation to seed our epidemiological simulations. Simfrastructure calls data mining tools and Simdemics to achieve these tasks.

Biases in their demographics compared to a random sample of the population will induce persistent biases in the set of people infected at any time. We estimated the demand at hospitals, assuming that people would arrive at a hospital near their home or current location. We also estimated the demographics of casualties under an alternative scenario during only a heat wave. Historically, the most likely casualties of a heat wave are elderly people living alone with few activities outside the home.

This information, combined with demographic and household structure data, allowed us to estimate demand for health services created by the heat wave by demographic and location. For situation assessment, we noted the obvious differences between these two demand patterns. In an actual event, comparison with admissions surveillance data would allow quick disambiguation between the two situations. We estimated the likely spread of disease for several different pathogens by demographic and location. Furthermore, we implemented several suggested mitigating responses, such as closing schools and/or workplaces, or quarantining households with symptomatic people. Knowledge of the household structure permits an exceptionally realistic representation of the consequences of these actions. For example, if schools are closed, a care-giver will also need to stay home in many households.

Conclusions and Summary

We described our work in progress that aims to build a scalable CI to study large socio-technical networked systems. The goal of the CI is to provide seamless access to HPC-based modeling and analysis capability for routine analytical efforts. It consists of (i) high-resolution models, tools for decision making, and consequence analysis, (ii) service-oriented architecture and delivery mechanism for facilitating the use of these models by domain experts, (iii) distributed coordinating architecture for information fusion, model execution and data processing, and (iv) scalable methods for visual and data analytics to support analysts.

Due to space considerations, we have not discussed peta-scale computing and data grids that will serve as the underlying technology. Much remains to be done to develop the CI. Researchers across the world are developing new tools in web services, tools and CI for various problem domains 9 10 11. We hope to build on these advances.

Acknowledgements The work is partially supported by an NSF HSD grant, the NIH MIDAS project, the CDC center of excellence, DoD and a Virginia Tech internal grant. We thank our current and past collaborators, on related topics, especially, Douglas Roberts, Aravind Srinivasan, Geoffery Fox, Arun Phadke and Jim Thorp, students in the Network Dynamics and Simulation Science Laboratory and members of the TRANSIMS and NISAC projects at Los Alamos National Laboratory. Finally, we thank Suman Nadella and Pete Beckman for inviting us to submit an article for this CTWatch Quarterly issue.
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19Atkins, K., Barrett, C., Beckman, R., Bisset, K., Chen, J., Eubank, S., Anil Kumar, V. S., Lewis, B., Marathe, A., Marathe, M., Mortveit, H., Stretz, P. “An analysis of layered public health interventions at Ft. Lewis and Ft. Hood during a pandemic influenza event," TR-NDSSL-07-019, 2007.

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Reference this article
Atkins, K., Barrett, C. L., Beckman, R., Bisset, K., Chen, J., Eubank, S., Feng, A., Feng, Z., Harris, S. D., Lewis, B., Anil Kuman, V. S., Marathe, M. V., Marathe, A., Mortveit, H., Stretz, P. "An Interaction Based Composable Architecture for Building Scalable Models of Large Social, Biological, Information and Technical Systems," CTWatch Quarterly, Volume 4, Number 1, March 2008. http://www.ctwatch.org/quarterly/articles/2008/03/an-interaction-based-composable-architecture-for-building-scalable-models-of-large-social-biological-information-and-technical-systems/

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