Extreme-Scale Distribution-Based Data Analysis

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Presentation transcript:

Extreme-Scale Distribution-Based Data Analysis Han-Wei Shen （Lead PI), Gagan Agrawa, Huamin Wang The Ohio State University Tom Peterka Argonne National Laboratory Jonathan Woodring, Joanne Wendelberger Los Alamos National Laboratory Research Goals Develop distribution-based data analysis and visualization techniques to support DOE’s exascale applications Efficient computation, representation, and query of data distributions In situ data reduction, summarization, and triage Distribution-based data analytics and visualization Climate: POP and MPAS-O (LANL) Superconductivity: SOScon (ANL) Cosmology: HACC (ANL) Science Applications Distribution-Based Visual Analytics Why Distributions? A compact representation of data Many statistics of the data can be derived Information flow across the data analytics pipeline can be analyzed Regions of high information content can be identified Parameters of various visualization algorithms can be optimized Allow detailed data analysis and inferences Support many needs of in situ data analysis Data reduction Data summarization Data triage Feature extraction and indexing \ Research Tasks Computation and Representation of Distributions Computing distributions from bitmap indices Supporting efficient range distribution query Statistics-preserving block decomposition Data Summarization, Reduction, and Triage Spatial domain data summarization, reduction, and triage Value domain summarization, reduction, and triage Temporal domain summarization, reduction, and triage Distribution-based Visual Analytics Distribution-based multivariate data analysis Feature-driven view seleciton and control Query-driven visual analysis with distributions Software Library (EDDA) A C/C++ library for entropy and distribution computation for large scale datasets Different information-theoretic measurement Distributed computation via MPI Support of various data types In Situ Data Analysis/Visualization Pipeline Acknowledgement This work was supported by DOE Office of Science, Advanced Scientific Computing Research, under award number DE-DC0012495, program manager Lucy Nowell.