High Performance Multivariate Visual Data Exploration for Extremely Large Data

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One of the central challenges in modern science is the need to quickly derive knowledge and understanding from large, complex collections of data. We present a new approach that deals with this challenge by combining and extending techniques from high performance visual data analysis and scientific data management. This approach is demonstrated within the context of gaining insight from complex, time-varying datasets produced by a laser wakefield accelerator simulation. Our approach leverages histogram-based parallel coordinates for both visual information display as well as a vehicle for guiding a data mining operation. Data extraction and subsetting are implemented with state-of-the-art index/query ... continued below

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Rubel, Oliver; Wu, Kesheng; Childs, Hank; Meredith, Jeremy; Geddes, Cameron G.R.; Cormier-Michel, Estelle et al. August 22, 2008.

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Description

One of the central challenges in modern science is the need to quickly derive knowledge and understanding from large, complex collections of data. We present a new approach that deals with this challenge by combining and extending techniques from high performance visual data analysis and scientific data management. This approach is demonstrated within the context of gaining insight from complex, time-varying datasets produced by a laser wakefield accelerator simulation. Our approach leverages histogram-based parallel coordinates for both visual information display as well as a vehicle for guiding a data mining operation. Data extraction and subsetting are implemented with state-of-the-art index/query technology. This approach, while applied here to accelerator science, is generally applicable to a broad set of science applications, and is implemented in a production-quality visual data analysis infrastructure. We conduct a detailed performance analysis and demonstrate good scalability on a distributed memory Cray XT4 system.

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  • SuperComputing 2008, Austin, Texas, USA, Nov.15-21 2008

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  • Report No.: LBNL-716E
  • Grant Number: DE-AC02-05CH11231
  • Office of Scientific & Technical Information Report Number: 941717
  • Archival Resource Key: ark:/67531/metadc902090

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Office of Scientific & Technical Information Technical Reports

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  • August 22, 2008

Added to The UNT Digital Library

  • Sept. 27, 2016, 1:39 a.m.

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  • Oct. 31, 2016, 12:16 p.m.

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Rubel, Oliver; Wu, Kesheng; Childs, Hank; Meredith, Jeremy; Geddes, Cameron G.R.; Cormier-Michel, Estelle et al. High Performance Multivariate Visual Data Exploration for Extremely Large Data, article, August 22, 2008; Berkeley, California. (digital.library.unt.edu/ark:/67531/metadc902090/: accessed June 22, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.