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SC Conference - Activity Details

Interactive HPC-driven Visual Analysis for Multiple Genome Datasets

Team Members:
Christopher S Oehmen  (Pacific Northwest National Laboratory)
Lee Ann McCue  (Pacific Northwest National Laboratory)
Bobbie-Jo M Webb-Robertson  (Pacific Northwest National Laboratory)
Scott T Dowson  (Pacific Northwest National Laboratory)
Justin P Almquist  (Pacific Northwest National Laboratory)
Jason E McDermott  (Pacific Northwest National Laboratory)
Challenges Session
SC Analytics Challenge
Tuesday,  04:36PM - 04:58PM
Room 14
Conventional techniques for studying biological systems one gene or protein at a time are being augmented by whole-genome analyses and comparative genomics approaches, which are often used in a discovery-driven, rather than hypothesis-driven, manner. Common desktop systems frequently do not have the computational capacity to handle the volume of calculations needed to drive data analysis on multiple genome datasets or metagenome datasets without significant filtering, whereby one risks losing significant data. At the other end of the spectrum, common HPC platforms can drive the throughput needed to bring multiple genome analysis within reach, but they are often batch, multi-user systems and therefore not conducive to iterative analysis and hypothesis evaluation. The goal of this HPC visual analytics entry is to demonstrate feasibility and performance of driving efficient multiprocessor execution of sequence analysis at the multiple genome scale with intuitive, browsable visual metaphors enabling iterative evaluation of a scientific hypothesis.
   IEEE Computer Society  /  ACM     2 0   Y E A R S   -   U N L E A S H I N G   T H E   P O W E R   O F   H P C