2011
Woodring, Jonathan; Ahrens, James; Figg, Jeannette; Wendelberger, Joanne; Habib, Salman; Heitmann, Katrin
In-situ Sampling of a Large-Scale Particle Simulation for Interactive Visualization and Analysis Proceedings Article
In: Computer Graphics Forum, pp. 1151–1160, Wiley Online Library 2011, (LA-UR-11-02106).
Abstract | Links | BibTeX | Tags: in-situ, large-scale particle simulation, sampling, visualization
@inproceedings{woodring2011situ,
title = {In-situ Sampling of a Large-Scale Particle Simulation for Interactive Visualization and Analysis},
author = {Jonathan Woodring and James Ahrens and Jeannette Figg and Joanne Wendelberger and Salman Habib and Katrin Heitmann},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/In-situSamplingOfALarge-ScaleParticleSimulationForInteractiveVisualizationAndAnalysis.pdf},
year = {2011},
date = {2011-01-01},
booktitle = {Computer Graphics Forum},
volume = {30},
number = {3},
pages = {1151--1160},
organization = {Wiley Online Library},
abstract = {We describe a simulation-time random sampling of a large-scale particle simu lation, the RoadRunner Universe MC 3 cosmological simulation, for interactive post-analysis and visualization. Simu lation data generation rates will continue to be far greater than storage bandwidth rates by many orders of magnitude. This implies that only a very small fraction of data generated by a simulation can ever be stored a nd subsequently post-analyzed. The limiting factors in this situation are similar to the problem in many population surveys : there aren’t enough human resources to query a large population. To cope with the lack of resource s, statistical sampling techniques are used to create a representative data set of a large population. Following this analo gy, we propose to store a simulation-time random sampling of the particle data for post-analysis, with level-of-detail organization, to cope with the bottlenecks. A sample is stored directly from the simulation in a level-of-detail for mat for post-visualization and analysis, which amortizes the cost of post-processing and reduces wo rkflow time. Additionally by sampling during the simulation, we are able to analyze the entire particle population to record full population statistics and quantify sample error.},
note = {LA-UR-11-02106},
keywords = {in-situ, large-scale particle simulation, sampling, visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
We describe a simulation-time random sampling of a large-scale particle simu lation, the RoadRunner Universe MC 3 cosmological simulation, for interactive post-analysis and visualization. Simu lation data generation rates will continue to be far greater than storage bandwidth rates by many orders of magnitude. This implies that only a very small fraction of data generated by a simulation can ever be stored a nd subsequently post-analyzed. The limiting factors in this situation are similar to the problem in many population surveys : there aren’t enough human resources to query a large population. To cope with the lack of resource s, statistical sampling techniques are used to create a representative data set of a large population. Following this analo gy, we propose to store a simulation-time random sampling of the particle data for post-analysis, with level-of-detail organization, to cope with the bottlenecks. A sample is stored directly from the simulation in a level-of-detail for mat for post-visualization and analysis, which amortizes the cost of post-processing and reduces wo rkflow time. Additionally by sampling during the simulation, we are able to analyze the entire particle population to record full population statistics and quantify sample error.
: . .
1.
Woodring, Jonathan; Ahrens, James; Figg, Jeannette; Wendelberger, Joanne; Habib, Salman; Heitmann, Katrin
In-situ Sampling of a Large-Scale Particle Simulation for Interactive Visualization and Analysis Proceedings Article
In: Computer Graphics Forum, pp. 1151–1160, Wiley Online Library 2011, (LA-UR-11-02106).
@inproceedings{woodring2011situ,
title = {In-situ Sampling of a Large-Scale Particle Simulation for Interactive Visualization and Analysis},
author = {Jonathan Woodring and James Ahrens and Jeannette Figg and Joanne Wendelberger and Salman Habib and Katrin Heitmann},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/In-situSamplingOfALarge-ScaleParticleSimulationForInteractiveVisualizationAndAnalysis.pdf},
year = {2011},
date = {2011-01-01},
booktitle = {Computer Graphics Forum},
volume = {30},
number = {3},
pages = {1151--1160},
organization = {Wiley Online Library},
abstract = {We describe a simulation-time random sampling of a large-scale particle simu lation, the RoadRunner Universe MC 3 cosmological simulation, for interactive post-analysis and visualization. Simu lation data generation rates will continue to be far greater than storage bandwidth rates by many orders of magnitude. This implies that only a very small fraction of data generated by a simulation can ever be stored a nd subsequently post-analyzed. The limiting factors in this situation are similar to the problem in many population surveys : there aren’t enough human resources to query a large population. To cope with the lack of resource s, statistical sampling techniques are used to create a representative data set of a large population. Following this analo gy, we propose to store a simulation-time random sampling of the particle data for post-analysis, with level-of-detail organization, to cope with the bottlenecks. A sample is stored directly from the simulation in a level-of-detail for mat for post-visualization and analysis, which amortizes the cost of post-processing and reduces wo rkflow time. Additionally by sampling during the simulation, we are able to analyze the entire particle population to record full population statistics and quantify sample error.},
note = {LA-UR-11-02106},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We describe a simulation-time random sampling of a large-scale particle simu lation, the RoadRunner Universe MC 3 cosmological simulation, for interactive post-analysis and visualization. Simu lation data generation rates will continue to be far greater than storage bandwidth rates by many orders of magnitude. This implies that only a very small fraction of data generated by a simulation can ever be stored a nd subsequently post-analyzed. The limiting factors in this situation are similar to the problem in many population surveys : there aren’t enough human resources to query a large population. To cope with the lack of resource s, statistical sampling techniques are used to create a representative data set of a large population. Following this analo gy, we propose to store a simulation-time random sampling of the particle data for post-analysis, with level-of-detail organization, to cope with the bottlenecks. A sample is stored directly from the simulation in a level-of-detail for mat for post-visualization and analysis, which amortizes the cost of post-processing and reduces wo rkflow time. Additionally by sampling during the simulation, we are able to analyze the entire particle population to record full population statistics and quantify sample error.