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Title: Application of High-performance Visual Analysis Methods to Laser Wakefield Particle Acceleration Data

Abstract

Our work combines and extends techniques from high-performance scientific data management and visualization to enable scientific researchers to gain insight from extremely large, complex, time-varying laser wakefield particle accelerator simulation data. We extend histogram-based parallel coordinates for use in visual information display as well as an interface for guiding and performing data mining operations, which are based upon multi-dimensional and temporal thresholding and data subsetting operations. To achieve very high performance on parallel computing platforms, we leverage FastBit, a state-of-the-art index/query technology, to accelerate data mining and multi-dimensional histogram computation. We show how these techniques are used in practice by scientific researchers to identify, visualize and analyze a particle beam in a large, time-varying dataset.

Authors:
; ; ; ; ; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
Accelerator& Fusion Research Division; Computational Research Division; Physics Division
OSTI Identifier:
940566
Report Number(s):
LBNL-952E
TRN: US0807156
DOE Contract Number:  
DE-AC02-05CH11231
Resource Type:
Conference
Resource Relation:
Conference: IEEE Visualization 2008, Columbus, Ohio, Oct.19-Oct.24, 2008
Country of Publication:
United States
Language:
English
Subject:
43; 97; ACCELERATION; ACCELERATORS; LASERS; MANAGEMENT; MINING; PARTICLE BEAMS; PERFORMANCE; SIMULATION; data mining, visual data analysis, accelerator modeling, parallel visualization, large data visualization, temporal visualization, temporal data analysis

Citation Formats

Rubel, Oliver, Prabhat, Mr., Wu, Kesheng, Childs, Hank, Meredith, Jeremy, Geddes, Cameron G.R., Cormier-Michel, Estelle, Ahern, Sean, Weber, Gunther H., Messmer, Peter, Hagen, Hans, Hamann, Bernd, and Bethel, E. Wes. Application of High-performance Visual Analysis Methods to Laser Wakefield Particle Acceleration Data. United States: N. p., 2008. Web.
Rubel, Oliver, Prabhat, Mr., 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. Application of High-performance Visual Analysis Methods to Laser Wakefield Particle Acceleration Data. United States.
Rubel, Oliver, Prabhat, Mr., Wu, Kesheng, Childs, Hank, Meredith, Jeremy, Geddes, Cameron G.R., Cormier-Michel, Estelle, Ahern, Sean, Weber, Gunther H., Messmer, Peter, Hagen, Hans, Hamann, Bernd, and Bethel, E. Wes. Thu . "Application of High-performance Visual Analysis Methods to Laser Wakefield Particle Acceleration Data". United States. https://www.osti.gov/servlets/purl/940566.
@article{osti_940566,
title = {Application of High-performance Visual Analysis Methods to Laser Wakefield Particle Acceleration Data},
author = {Rubel, Oliver and Prabhat, Mr. 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},
abstractNote = {Our work combines and extends techniques from high-performance scientific data management and visualization to enable scientific researchers to gain insight from extremely large, complex, time-varying laser wakefield particle accelerator simulation data. We extend histogram-based parallel coordinates for use in visual information display as well as an interface for guiding and performing data mining operations, which are based upon multi-dimensional and temporal thresholding and data subsetting operations. To achieve very high performance on parallel computing platforms, we leverage FastBit, a state-of-the-art index/query technology, to accelerate data mining and multi-dimensional histogram computation. We show how these techniques are used in practice by scientific researchers to identify, visualize and analyze a particle beam in a large, time-varying dataset.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2008},
month = {8}
}

Conference:
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