Distribution-based Particle Data Reduction for In-situ Analysis and Visualization of Large-scale N-body Cosmological Simulations

Cosmological N-body simulation is an important tool for scientists to study the evolution of the universe. With the increase of computing power, billions of particles of high space-time fidelity can be simulated by supercomputers. However, limited computer storage can only hold a small subset of the...

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Veröffentlicht in:IEEE Pacific Visualization Symposium S. 171 - 180
Hauptverfasser: Li, Guan, Xu, Jiayi, Zhang, Tianchi, Shan, Guihua, Shen, Han-Wei, Wang, Ko-Chih, Liao, Shihong, Lu, Zhonghua
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2020
Schlagworte:
Analytical models
Computational modeling
Data models
Data visualization
Human-centered computing
Probabilistic logic
Probability distribution
Scientific visualization
Supercomputers
Visualization
Visualization application domains
ISSN:2165-8773
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Zusammenfassung:Cosmological N-body simulation is an important tool for scientists to study the evolution of the universe. With the increase of computing power, billions of particles of high space-time fidelity can be simulated by supercomputers. However, limited computer storage can only hold a small subset of the simulation output for analysis, which makes the understanding of the underlying cosmological phenomena difficult. To alleviate the problem, we design an in-situ data reduction method for large-scale unstructured particle data. During the data generation phase, we use a combined k-dimensional partitioning and Gaussian mixture model approach to reduce the data by utilizing probability distributions. We offer a model evaluation criterion to examine the quality of the probabilistic distribution models, which allows us to identify and improve low-quality models. After the in-situ processing, the particle data size is greatly reduced, which satisfies the requirements from the domain experts. By comparing the astronomical attributes and visualizations of the reconstructed data with the raw data, we demonstrate the effectiveness of our in-situ particle data reduction technique.
ISSN:2165-8773
DOI:10.1109/PacificVis48177.2020.1186