2014
Ahrens, James; Jourdain, Sebastien; O'Leary, Patrick; Patchett, John; Rogers, David; Petersen, Mark
An Image-based Approach to Extreme Scale in Situ Visualization and Analysis Proceedings Article
In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 424–434, IEEE Press, New Orleans, Louisana, 2014, ISBN: 978-1-4799-5500-8, (LA-UR-14-26864).
Abstract | Links | BibTeX | Tags: analysis, cinema, cinemascience, image-based, in-situ, visualization
@inproceedings{Ahrens:2014:IAE:2683593.2683640,
title = {An Image-based Approach to Extreme Scale in Situ Visualization and Analysis},
author = {James Ahrens and Sebastien Jourdain and Patrick O'Leary and John Patchett and David Rogers and Mark Petersen},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/AnImage-basedApproachToExtremeScaleInSituvisualizationAndAnalysis.pdf
http://dx.doi.org/10.1109/SC.2014.40},
doi = {10.1109/SC.2014.40},
isbn = {978-1-4799-5500-8},
year = {2014},
date = {2014-01-01},
booktitle = {Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis},
pages = {424--434},
publisher = {IEEE Press},
address = {New Orleans, Louisana},
series = {SC '14},
abstract = {Extreme scale scientific simulations are leading a charge to exascale computation, and data analytics runs the risk of being a bottleneck to scientific discovery. Due to power and I/O constraints, we expect in situ visualization and analysis will be a critical component of these workflows. Options for extreme scale data analysis are often presented as a stark contrast: write large files to disk for interactive, exploratory analysis, or perform in situ analysis to save detailed data about phenomena that a scientists knows about in advance. We present a novel framework for a third option - a highly interactive, image-based approach that promotes exploration of simulation results, and is easily accessed through extensions to widely used open source tools. This in situ approach supports interactive exploration of a wide range of results, while still significantly reducing data movement and storage.},
note = {LA-UR-14-26864},
keywords = {analysis, cinema, cinemascience, image-based, in-situ, visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
Extreme scale scientific simulations are leading a charge to exascale computation, and data analytics runs the risk of being a bottleneck to scientific discovery. Due to power and I/O constraints, we expect in situ visualization and analysis will be a critical component of these workflows. Options for extreme scale data analysis are often presented as a stark contrast: write large files to disk for interactive, exploratory analysis, or perform in situ analysis to save detailed data about phenomena that a scientists knows about in advance. We present a novel framework for a third option - a highly interactive, image-based approach that promotes exploration of simulation results, and is easily accessed through extensions to widely used open source tools. This in situ approach supports interactive exploration of a wide range of results, while still significantly reducing data movement and storage.
: . .
1.
Ahrens, James; Jourdain, Sebastien; O'Leary, Patrick; Patchett, John; Rogers, David; Petersen, Mark
An Image-based Approach to Extreme Scale in Situ Visualization and Analysis Proceedings Article
In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 424–434, IEEE Press, New Orleans, Louisana, 2014, ISBN: 978-1-4799-5500-8, (LA-UR-14-26864).
@inproceedings{Ahrens:2014:IAE:2683593.2683640,
title = {An Image-based Approach to Extreme Scale in Situ Visualization and Analysis},
author = {James Ahrens and Sebastien Jourdain and Patrick O'Leary and John Patchett and David Rogers and Mark Petersen},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/AnImage-basedApproachToExtremeScaleInSituvisualizationAndAnalysis.pdf
http://dx.doi.org/10.1109/SC.2014.40},
doi = {10.1109/SC.2014.40},
isbn = {978-1-4799-5500-8},
year = {2014},
date = {2014-01-01},
booktitle = {Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis},
pages = {424--434},
publisher = {IEEE Press},
address = {New Orleans, Louisana},
series = {SC '14},
abstract = {Extreme scale scientific simulations are leading a charge to exascale computation, and data analytics runs the risk of being a bottleneck to scientific discovery. Due to power and I/O constraints, we expect in situ visualization and analysis will be a critical component of these workflows. Options for extreme scale data analysis are often presented as a stark contrast: write large files to disk for interactive, exploratory analysis, or perform in situ analysis to save detailed data about phenomena that a scientists knows about in advance. We present a novel framework for a third option - a highly interactive, image-based approach that promotes exploration of simulation results, and is easily accessed through extensions to widely used open source tools. This in situ approach supports interactive exploration of a wide range of results, while still significantly reducing data movement and storage.},
note = {LA-UR-14-26864},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Extreme scale scientific simulations are leading a charge to exascale computation, and data analytics runs the risk of being a bottleneck to scientific discovery. Due to power and I/O constraints, we expect in situ visualization and analysis will be a critical component of these workflows. Options for extreme scale data analysis are often presented as a stark contrast: write large files to disk for interactive, exploratory analysis, or perform in situ analysis to save detailed data about phenomena that a scientists knows about in advance. We present a novel framework for a third option - a highly interactive, image-based approach that promotes exploration of simulation results, and is easily accessed through extensions to widely used open source tools. This in situ approach supports interactive exploration of a wide range of results, while still significantly reducing data movement and storage.