RPI Computer Scientist Wins NSF Award to Match Exascale Systems With Petascale Data Volumes
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Rensselaer Polytechnic Institute faculty member and computer scientist George Slota has been granted a National Science Foundation Faculty Early Career Development (CAREER) award to work on the problem of enabling exascale-class system to handle gigantic, petascale-class volumes of data.
"How do we best understand and get insight from this kind of data? To do that, we have to map the data to the hardware, with consideration of the algorithm itself," said Slota, who has been awarded a $490,000 CAREER grant. "Each aspect is fairly challenging because of the complexity of the data and the complexity of modern hardware."
The next generations of supercomputers will be powerful enough to analyze interactions as interactions[sic] as those between the users of a social network or between the neurons in a human brain. But the match between a machine that works on the exascale (1018 operations per second) and a complex dataset on the petascale (1015 elements) presents multiple challenges and must be carefully orchestrated.
[...] "The ideal solution is a general purpose way that, given any arbitrary dataset, we can load it, process it, and solve some arbitrary problem that's relevant to a biologist who's studying the brain, or a physicist studying particle interactions, or whatever the application may be," Slota said.
"A key challenge facing data-driven scientific advancement is the ability to organize and extract meaning from massive datasets associated with many fields of present-day research," said Curt Breneman, dean of the RPI School of Science. "George's work points the way towards new solutions to this critical data analysis bottleneck. This CAREER award recognizes his potential, as well as the importance of this research, and we congratulate him on this recognition."
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