2015
Rogers, David; Ahrens, James; Patchett, John; DeMarle, David
Exploring Cinema with the Cinema Virtual Machine Proceedings Article
In: 2015, (Documentation/instructions. LA-UR-15-21934).
Abstract | Links | BibTeX | Tags: cinema, in-situ data analysis
@inproceedings{rogers2015exploring,
title = {Exploring Cinema with the Cinema Virtual Machine},
author = {David Rogers and James Ahrens and John Patchett and David DeMarle},
url = {http://datascience.dsscale.org/wp-content/uploads/2017/08/LA-UR-15-21934.pdf},
year = {2015},
date = {2015-05-27},
abstract = {Extreme scale scientific simulations are pushing the limits of scientific computation, and are stressing the limits of the data that we can store, explore, and understand. Options for extreme scale data analysis are often presented as a stark contrast: save massive data files to disk for interactive, exploratory visualization, or perform in situ analysis to save detailed data about phenomena a scientist knows about in advance. We propose that there is an alternative approach—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 new approach supports interactve exploration of a wide
range of results, while still significantly reducing data movement and storage.},
note = {Documentation/instructions. LA-UR-15-21934},
keywords = {cinema, in-situ data analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
Extreme scale scientific simulations are pushing the limits of scientific computation, and are stressing the limits of the data that we can store, explore, and understand. Options for extreme scale data analysis are often presented as a stark contrast: save massive data files to disk for interactive, exploratory visualization, or perform in situ analysis to save detailed data about phenomena a scientist knows about in advance. We propose that there is an alternative approach—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 new approach supports interactve exploration of a wide
range of results, while still significantly reducing data movement and storage.
range of results, while still significantly reducing data movement and storage.
: . .
1.
Rogers, David; Ahrens, James; Patchett, John; DeMarle, David
Exploring Cinema with the Cinema Virtual Machine Proceedings Article
In: 2015, (Documentation/instructions. LA-UR-15-21934).
@inproceedings{rogers2015exploring,
title = {Exploring Cinema with the Cinema Virtual Machine},
author = {David Rogers and James Ahrens and John Patchett and David DeMarle},
url = {http://datascience.dsscale.org/wp-content/uploads/2017/08/LA-UR-15-21934.pdf},
year = {2015},
date = {2015-05-27},
abstract = {Extreme scale scientific simulations are pushing the limits of scientific computation, and are stressing the limits of the data that we can store, explore, and understand. Options for extreme scale data analysis are often presented as a stark contrast: save massive data files to disk for interactive, exploratory visualization, or perform in situ analysis to save detailed data about phenomena a scientist knows about in advance. We propose that there is an alternative approach—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 new approach supports interactve exploration of a wide
range of results, while still significantly reducing data movement and storage.},
note = {Documentation/instructions. LA-UR-15-21934},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Extreme scale scientific simulations are pushing the limits of scientific computation, and are stressing the limits of the data that we can store, explore, and understand. Options for extreme scale data analysis are often presented as a stark contrast: save massive data files to disk for interactive, exploratory visualization, or perform in situ analysis to save detailed data about phenomena a scientist knows about in advance. We propose that there is an alternative approach—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 new approach supports interactve exploration of a wide
range of results, while still significantly reducing data movement and storage.
range of results, while still significantly reducing data movement and storage.