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In‐situ Sampling of a Large‐Scale Particle Simulation for Interactive Visualization and Analysis
Author(s) -
Woodring J.,
Ahrens J.,
Figg J.,
Wendelberger J.,
Habib S.,
Heitmann K.
Publication year - 2011
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/j.1467-8659.2011.01964.x
Subject(s) - computer science , visualization , sampling (signal processing) , population , sample (material) , interactive visualization , workflow , sample size determination , data visualization , scale (ratio) , data mining , statistics , database , mathematics , cartography , chemistry , demography , filter (signal processing) , chromatography , sociology , computer vision , geography
We describe a simulation‐time random sampling of a large‐scale particle simulation, the RoadRunner Universe MC 3 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 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 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.