SC Conference - Activity Details

High Performance Multivariate Visual Data Exploration for Extremely Large Data

Oliver RĂ¼bel  (Lawrence Berkeley National Laboratory)
Mr Prabhat  (Lawrence Berkeley National Laboratory)
Kesheng Wu  (Lawrence Berkeley National Laboratory)
Hank Childs  (Lawrence Livermore National Laboratory)
Jeremy Meredith  (Oak Ridge National Laboratory)
Cameron Geddes  (Lawrence Berkeley National Laboratory)
Sean Ahern  (Oak Ridge National Laboratory)
Gunther Weber  (Lawrence Berkeley National Laboratory)
Hans Hagen  (University of Kaiserslautern)
Bernd Hamann  (University of California, Davis)
E. Wes Bethel  (Lawrence Berkeley National Laboratory)
Estelle Cormier-Michel  (Lawrence Berkeley National Laboratory)
Peter Messmer  (Tech-X Corporation)
Papers Session
Visualization and Data Management
Thursday,  02:30PM - 03:00PM
Room Ballroom G
One of the central challenges in modern science is the ability to quickly derive knowledge and understanding from large, complex collections of data. We present a novel approach to solve this problem that combines and extends techniques from high performance visual data analysis and scientific data management. This approach is demonstrated within the context of gaining insight from complex, time-varying datasets produced by a laser wakefield accelerator simulation. Our approach leverages histogram-based parallel coordinates for both visual information display as well as a vehicle for guiding a data mining operation. Data extraction and subsetting is implemented with state-of-the-art index/query technology. This approach, while applied here to accelerator science, is generally applicable to a broad set of science applications, and is implemented in a production-quality visual data analysis infrastructure. We conduct an extensive performance analysis and demonstrate good scalability on a distributed memory Cray XT4 system.
The full paper can be found in the IEEE Xplore Digital Library and ACM Digital Library
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