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Title: High Performance Multivariate Visual Data Exploration for Extremely Large Data

Abstract

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.

Authors:
; ; ; ; ; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
Accelerator& Fusion Research Division; Computational Research Division
OSTI Identifier:
941717
Report Number(s):
LBNL-716E
TRN: US0807486
DOE Contract Number:  
DE-AC02-05CH11231
Resource Type:
Conference
Resource Relation:
Conference: SuperComputing 2008, Austin, Texas, USA, Nov.15-21 2008
Country of Publication:
United States
Language:
English
Subject:
43; 97; ACCELERATORS; DATA ANALYSIS; EXPLORATION; LASERS; MANAGEMENT; MINING; PERFORMANCE; SIMULATION; WAKEFIELD ACCELERATORS; data mining, visual data analysis, accelerator modeling, parallel visualization, large data visualization, temporal visualization, temporal data analysis

Citation Formats

Rubel, Oliver, Wu, Kesheng, Childs, Hank, Meredith, Jeremy, Geddes, Cameron G.R., Cormier-Michel, Estelle, Ahern, Sean, Weber, Gunther H., Messmer, Peter, Hagen, Hans, Hamann, Bernd, Bethel, E. Wes, and Prabhat,. High Performance Multivariate Visual Data Exploration for Extremely Large Data. United States: N. p., 2008. Web.
Rubel, Oliver, Wu, Kesheng, Childs, Hank, Meredith, Jeremy, Geddes, Cameron G.R., Cormier-Michel, Estelle, Ahern, Sean, Weber, Gunther H., Messmer, Peter, Hagen, Hans, Hamann, Bernd, Bethel, E. Wes, & Prabhat,. High Performance Multivariate Visual Data Exploration for Extremely Large Data. United States.
Rubel, Oliver, Wu, Kesheng, Childs, Hank, Meredith, Jeremy, Geddes, Cameron G.R., Cormier-Michel, Estelle, Ahern, Sean, Weber, Gunther H., Messmer, Peter, Hagen, Hans, Hamann, Bernd, Bethel, E. Wes, and Prabhat,. Fri . "High Performance Multivariate Visual Data Exploration for Extremely Large Data". United States. https://www.osti.gov/servlets/purl/941717.
@article{osti_941717,
title = {High Performance Multivariate Visual Data Exploration for Extremely Large Data},
author = {Rubel, Oliver and Wu, Kesheng and Childs, Hank and Meredith, Jeremy and Geddes, Cameron G.R. and Cormier-Michel, Estelle and Ahern, Sean and Weber, Gunther H. and Messmer, Peter and Hagen, Hans and Hamann, Bernd and Bethel, E. Wes and Prabhat,},
abstractNote = {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.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2008},
month = {8}
}

Conference:
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