2009
Ahrens, James; Woodring, Jonathan; DeMarle, David; Patchett, John; Maltrud, Mathew
Interactive remote large-scale data visualization via prioritized multi-resolution streaming Proceedings Article
In: Proceedings of the 2009 Workshop on Ultrascale Visualization, pp. 1–10, ACM 2009, (LA-UR-10-02112).
Abstract | Links | BibTeX | Tags: remote systems
@inproceedings{ahrens2009interactive,
title = {Interactive remote large-scale data visualization via prioritized multi-resolution streaming},
author = {James Ahrens and Jonathan Woodring and David DeMarle and John Patchett and Mathew Maltrud},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/InteractiveRemoteLarge-ScaleDataVisualizationViaPrioritizedMulti-resolutionStreaming2.pdf},
year = {2009},
date = {2009-01-01},
booktitle = {Proceedings of the 2009 Workshop on Ultrascale Visualization},
pages = {1--10},
organization = {ACM},
abstract = {The simulations that run on petascale and future exascale supercomputers pose a difficult challenge for scientists to visualize and analyze their results remotely. They are limited in their ability to interactively visualize their data mainly due to limited network bandwidth associated with sending and reading large data at a distance. To tackle this issue, we provide a generalized distance visualization architecture for large remote data that aims to provide interactive analysis. We achieve this through a prioritized, multi-resolution, streaming architecture. Since the original data size is several orders of magnitude greater than the display and network technologies, we stream downsampled versions of representation data over time to complete a visualization using fast local rendering. This technique provides the necessary interactivity and full-resolution results dynamically on demand while maintaining a full-featured visualization framework.},
note = {LA-UR-10-02112},
keywords = {remote systems},
pubstate = {published},
tppubtype = {inproceedings}
}
The simulations that run on petascale and future exascale supercomputers pose a difficult challenge for scientists to visualize and analyze their results remotely. They are limited in their ability to interactively visualize their data mainly due to limited network bandwidth associated with sending and reading large data at a distance. To tackle this issue, we provide a generalized distance visualization architecture for large remote data that aims to provide interactive analysis. We achieve this through a prioritized, multi-resolution, streaming architecture. Since the original data size is several orders of magnitude greater than the display and network technologies, we stream downsampled versions of representation data over time to complete a visualization using fast local rendering. This technique provides the necessary interactivity and full-resolution results dynamically on demand while maintaining a full-featured visualization framework.
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1.
Ahrens, James; Woodring, Jonathan; DeMarle, David; Patchett, John; Maltrud, Mathew
Interactive remote large-scale data visualization via prioritized multi-resolution streaming Proceedings Article
In: Proceedings of the 2009 Workshop on Ultrascale Visualization, pp. 1–10, ACM 2009, (LA-UR-10-02112).
@inproceedings{ahrens2009interactive,
title = {Interactive remote large-scale data visualization via prioritized multi-resolution streaming},
author = {James Ahrens and Jonathan Woodring and David DeMarle and John Patchett and Mathew Maltrud},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/InteractiveRemoteLarge-ScaleDataVisualizationViaPrioritizedMulti-resolutionStreaming2.pdf},
year = {2009},
date = {2009-01-01},
booktitle = {Proceedings of the 2009 Workshop on Ultrascale Visualization},
pages = {1--10},
organization = {ACM},
abstract = {The simulations that run on petascale and future exascale supercomputers pose a difficult challenge for scientists to visualize and analyze their results remotely. They are limited in their ability to interactively visualize their data mainly due to limited network bandwidth associated with sending and reading large data at a distance. To tackle this issue, we provide a generalized distance visualization architecture for large remote data that aims to provide interactive analysis. We achieve this through a prioritized, multi-resolution, streaming architecture. Since the original data size is several orders of magnitude greater than the display and network technologies, we stream downsampled versions of representation data over time to complete a visualization using fast local rendering. This technique provides the necessary interactivity and full-resolution results dynamically on demand while maintaining a full-featured visualization framework.},
note = {LA-UR-10-02112},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The simulations that run on petascale and future exascale supercomputers pose a difficult challenge for scientists to visualize and analyze their results remotely. They are limited in their ability to interactively visualize their data mainly due to limited network bandwidth associated with sending and reading large data at a distance. To tackle this issue, we provide a generalized distance visualization architecture for large remote data that aims to provide interactive analysis. We achieve this through a prioritized, multi-resolution, streaming architecture. Since the original data size is several orders of magnitude greater than the display and network technologies, we stream downsampled versions of representation data over time to complete a visualization using fast local rendering. This technique provides the necessary interactivity and full-resolution results dynamically on demand while maintaining a full-featured visualization framework.