Abstract
As data sets grow in size and complexity, global analysis methods do not necessarily characterize the phenomena of interest, and scientists are increasingly reliant on feature-based analysis methods to study the results of large-scale simulations. This chapter presents a framework that efficiently encodes the set of all possible features in a hierarchy that is augmented with attributes, such as statistical moments of various scalar fields. The resulting meta-data generated by the framework is orders of magnitude smaller than the original simulation data, yet it is sufficient to support a fully flexible and interactive analysis of the features, allowing for arbitrary thresholds, providing per-feature statistics, and creating various global diagnostics such as Cumulative Density Functions (CDFs), histograms, or time-series. The analysis is combined with a rendering of the features in a linked-view browser that enables scientists to interactively explore, visualize, and analyze data resulting from petascale simulations. While there exist a number of potential feature hierarchies that can be used to segment the simulation domain, we provide a detailed description of two: the merge tree and the Morse-Smale (MS) complex, and demonstrate the utility of this new framework in practical settings.
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Acknowledgments
This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No.DE-AC05-00OR22725. This research used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No.DE-AC02-05CH11231. Sandia National Laboratories is a multiprogram laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000. This work was performed under the auspices of the U.S.Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52- 07NA27344. This work is supported in part by BNSF CISE ACI-0904631, NSG IIS- 1045032,NSF EFT ACI-0906379,DOE/NEUP 120341, DOE/Codesign P01180734,DOE/SciDAC DESC0007446, and CCMSC DE-NA0002375.
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Bennett, J., Gyulassy, A., Pascucci, V., Bremer, PT. (2014). Large Scale Data Analysis. In: Hansen, C., Chen, M., Johnson, C., Kaufman, A., Hagen, H. (eds) Scientific Visualization. Mathematics and Visualization. Springer, London. https://doi.org/10.1007/978-1-4471-6497-5_27
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