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Keith Bisset

2014

Lukasz Wesolowski, Ramprasad Venkataraman, Abhishek Gupta, Jae-Seung Yeom, Keith Bisset, Yanhua Sun, Pritish Jetley, Thomas R. Quinn, and Laxmikant V. Kale (2014): TRAM: Optimizing Fine-Grained Communication with Topological Routing and Aggregation of Messages, Institute of Electrical & Electronics Engineers, 2014 43rd International Conference on Parallel Processing (ICPP), pp211-220, Minneapolis, Minnesota, U.S.A.

Blue Waters Symposium a success


May 28, 2014

The symposium, held May 13-15 in Champaign, Ill., gathered many of the country’s leading supercomputer users to share what they have learned using Blue Waters and discuss the future of supercomputing. On May 13, 2014, Blue Waters supercomputer users and many of the NCSA staff who support their work converged in Champaign, Ill., for the second annual Blue Waters Symposium. The ensuing three days were filled with what many of them would later refer to as a wonderful variety of science talks and opportunities for networking and collaboration.


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Getting viral


Sep 1, 2009

"In the planning world, we work with policymakers to design studies of particular outcomes," says Virginia Tech's Keith Bisset. Months of planning, collaboration, and modeling might go into strategies for what a city, county, or entire country might do when facing a disease outbreak. "But now we also have tools that allow for a quick turnaround. We can do a situational assessment that shows them what a particular [outbreak] might look like tomorrow or next week as it unfolds. They describe the situation, and we can tell them the outcomes of various interventions," he says. This spring Bisset and a group from Virginia Tech joined forces with the Pittsburgh Supercomputing Center's Shawn Brown and Douglas Roberts and Diglio Simoni of North Carolina's Research Triangle Institute to win one of the first Petascale Computing Resource Allocations awards. With that support and with computing time on Blue Waters, they expect to model global epidemics, as well as smaller-scale outbreaks. Instead of looking at a few hundred million people, as the team members do with their current codes, they'll look at more than 6 billion people.


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