2001
Ahrens, James; Brislawn, Kristi; Martin, Ken; Geveci, Berk; Law, Charles; Papka, Michael
Large-scale data visualization using parallel data streaming Journal Article
In: Computer Graphics and Applications, IEEE, vol. 21, no. 4, pp. 34–41, 2001, (LA-UR-01-0970).
Abstract | Links | BibTeX | Tags: data streaming, LargeScaleVisualization, MPI, ParallelVisualization, VTK
@article{ahrens2001large,
title = {Large-scale data visualization using parallel data streaming},
author = {James Ahrens and Kristi Brislawn and Ken Martin and Berk Geveci and Charles Law and Michael Papka},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/LargeScaleDataVisualizationUsingParallelDataStreaming.pdf},
year = {2001},
date = {2001-01-01},
journal = {Computer Graphics and Applications, IEEE},
volume = {21},
number = {4},
pages = {34--41},
publisher = {IEEE},
abstract = {Effective large-scale data visualization remains a significant and important challenge with analysis codes already producing terabyte results on clusters with thousands of processors. Frequently the analysis codes produce distributed data and consume a significant portion of the available memory per node. This paper presents an architectural approach to handling these visualization problems based on mixed dataset topology parallel data streaming. This enables visualizations on a parallel cluster that would normally require more storage/memory than is available while at the same time achieving high code reuse. Results from a variety of hardware and visualization configurations are discussed with data sizes ranging near to a petabyte.},
note = {LA-UR-01-0970},
keywords = {data streaming, LargeScaleVisualization, MPI, ParallelVisualization, VTK},
pubstate = {published},
tppubtype = {article}
}
Effective large-scale data visualization remains a significant and important challenge with analysis codes already producing terabyte results on clusters with thousands of processors. Frequently the analysis codes produce distributed data and consume a significant portion of the available memory per node. This paper presents an architectural approach to handling these visualization problems based on mixed dataset topology parallel data streaming. This enables visualizations on a parallel cluster that would normally require more storage/memory than is available while at the same time achieving high code reuse. Results from a variety of hardware and visualization configurations are discussed with data sizes ranging near to a petabyte.
: . .
1.
Ahrens, James; Brislawn, Kristi; Martin, Ken; Geveci, Berk; Law, Charles; Papka, Michael
Large-scale data visualization using parallel data streaming Journal Article
In: Computer Graphics and Applications, IEEE, vol. 21, no. 4, pp. 34–41, 2001, (LA-UR-01-0970).
@article{ahrens2001large,
title = {Large-scale data visualization using parallel data streaming},
author = {James Ahrens and Kristi Brislawn and Ken Martin and Berk Geveci and Charles Law and Michael Papka},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/LargeScaleDataVisualizationUsingParallelDataStreaming.pdf},
year = {2001},
date = {2001-01-01},
journal = {Computer Graphics and Applications, IEEE},
volume = {21},
number = {4},
pages = {34--41},
publisher = {IEEE},
abstract = {Effective large-scale data visualization remains a significant and important challenge with analysis codes already producing terabyte results on clusters with thousands of processors. Frequently the analysis codes produce distributed data and consume a significant portion of the available memory per node. This paper presents an architectural approach to handling these visualization problems based on mixed dataset topology parallel data streaming. This enables visualizations on a parallel cluster that would normally require more storage/memory than is available while at the same time achieving high code reuse. Results from a variety of hardware and visualization configurations are discussed with data sizes ranging near to a petabyte.},
note = {LA-UR-01-0970},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Effective large-scale data visualization remains a significant and important challenge with analysis codes already producing terabyte results on clusters with thousands of processors. Frequently the analysis codes produce distributed data and consume a significant portion of the available memory per node. This paper presents an architectural approach to handling these visualization problems based on mixed dataset topology parallel data streaming. This enables visualizations on a parallel cluster that would normally require more storage/memory than is available while at the same time achieving high code reuse. Results from a variety of hardware and visualization configurations are discussed with data sizes ranging near to a petabyte.