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XVis: Visualization for the Extreme-Scale Scientific-Computation Ecosystem (Year-end report FY16 Q4)

Moreland, Kenneth D.; Sewell, Christopher; Childs, Hank; Ma, Kwan-Liu; Geveci, Berk; Pugmire, David

The XVis project brings together the key elements of research to enable scientific discovery at extreme scale. Scientific computing will no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/O limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. This project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressing four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis.

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TYPE Other Report YEAR 2016
DOIOSTI

In Situ Methods, Infrastructures, and Applications on High Performance Computing Platforms

Computer Graphics Forum

Bauer, A.C.; Abbasi, H.; Ahrens, J.; Childs, H.; Geveci, B.; Klasky, S.; Moreland, Kenneth D.; O'Leary, P.; Vishwanath, V.; Whitlock, B.; Bethel, E.W.

The considerable interest in the high performance computing (HPC) community regarding analyzing and visualization data without first writing to disk, i. e., in situ processing, is due to several factors. First is an I/O cost savings, where data is analyzed/visualized while being generated, without first storing to a filesystem. Second is the potential for increased accuracy, where fine temporal sampling of transient analysis might expose some complex behavior missed in coarse temporal sampling. Third is the ability to use all available resources, CPU's and accelerators, in the computation of analysis products. This STAR paper brings together researchers, developers and practitioners using in situ methods in extreme-scale HPC with the goal to present existing methods, infrastructures, and a range of computational science and engineering applications using in situ analysis and visualization.

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TYPE Conference Poster YEAR 2016
OSTIScopus

XVis: Visualization for the Extreme-Scale Scientific-Computation Ecosystem. Mid-year report FY16 Q2

Moreland, Kenneth D.; Sewell, Christopher; Childs, Hank; Ma, Kwan-Liu; Geveci, Berk; Meredith, Jeremy

The XVis project brings together the key elements of research to enable scientific discovery at extreme scale. Scientific computing will no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/O limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. This project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressing four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis.

More Details
TYPE Other Report YEAR 2016
DOIOSTI
Results 76–100 of 313
Results 76–100 of 313