In-situ sampling of a large-scale particle simulation for interactive visualization and analysis

Authors:
J. Woodring;J. Ahrens;J. Figg;J. Wendelberger;S. Habib;K. Heitmann
Affiliations:
CCS-7 Applied Computer Science Group, Los Alamos National Laboratory;CCS-7 Applied Computer Science Group, Los Alamos National Laboratory;CCS-6 Statistical Sciences Group, Los Alamos National Laboratory;CCS-6 Statistical Sciences Group, Los Alamos National Laboratory;T-2 Nuclear and Particle Physics, Astrophysics, and Cosmology Group, Los Alamos National Laboratory;ISR-1 Space Science and Applications Group, Los Alamos National Laboratory
Venue:
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Year:
2011

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Abstract

We describe a simulation-time random sampling of a large-scale particle simulation, the RoadRunner Universe MC3 cosmological simulation, for interactive post-analysis and visualization. Simulation 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 and 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 resources, statistical sampling techniques are used to create a representative data set of a large population. Following this analogy, we propose to store a simulationtime 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 format for post-visualization and analysis, which amortizes the cost of post-processing and reduces workflow 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.