Visualization and Analysis for Near-Real-Time Decision Making in Distributed Workflows

Conference · Sun May 01 00:00:00 EDT 2016

DOI:https://doi.org/10.1109/IPDPSW.2016.175· OSTI ID:1558497

Pugmire, Dave  ^[1]; Kress, James M.  ^[1]; Choi, Jong Youl  ^[1]; Klasky, Scott A.  ^[1]; Kurc, Tahsin M. ^[1]; Churchill, Michael ^[2]; Wolf, Matthew D.  ^[1]; Eisenhauer, Greg ^[3]; Childs, Hank ^[4]; Wu, Kesheng ^[5]; Sim, Alexander ^[5]; Gu, Junmin ^[5]; Low, Jonathan ^[6]

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1. ORNL
2. Princeton Plasma Physics Laboratory (PPPL)
3. Georgia Institute of Technology, Atlanta
4. University of Oregon
5. Lawrence Berkeley National Laboratory (LBNL)
6. A*Star Computational Resource Centre

Data driven science is becoming increasingly more common, complex, and is placing tremendous stresses on visualization and analysis frameworks. Data sources producing 10GB per second (and more) are becoming increasingly commonplace in both simulation, sensor and experimental sciences. These data sources, which are often distributed around the world, must be analyzed by teams of scientists that are also distributed. Enabling scientists to view, query and interact with such large volumes of data in near-real-time requires a rich fusion of visualization and analysis techniques, middleware and workflow systems. This paper discusses initial research into visualization and analysis of distributed data workflows that enables scientists to make near-real-time decisions of large volumes of time varying data.

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1558497
• Resource Relation: Conference: International Parallel and Distributed Processing Symposium Workshops (IPDPSW) - Chicago, Illinois, United States of America - 5/23/2016 8:00:00 AM-5/27/2016 4:00:00 AM
• Country of Publication: United States
• Language: English