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

Guan Li, Jiayi Xu, Tianchi Zhang, Guihua Shan*, Han Wei Shen, Ko Chih Wang, Shihong Liao, Zhonghua Lu

*此作品的通信作者

研究成果: 書貢獻/報告類型會議論文篇章

2 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
主出版物標題2020 IEEE Pacific Visualization Symposium, PacificVis 2020 - Proceedings
編輯Fabian Beck, Jinwook Seo, Chaoli Wang
發行者IEEE Computer Society
頁面171-180
頁數10
ISBN(電子)9781728156972
DOIs
出版狀態已發佈 - 2020 六月
事件13th IEEE Pacific Visualization Symposium, PacificVis 2020 - Tianjin, 中国
持續時間: 2020 四月 142020 四月 17

出版系列

名字IEEE Pacific Visualization Symposium
2020-June
ISSN(列印)2165-8765
ISSN(電子)2165-8773

會議

會議13th IEEE Pacific Visualization Symposium, PacificVis 2020
國家/地區中国
城市Tianjin
期間2020/04/142020/04/17

ASJC Scopus subject areas

  • 電腦繪圖與電腦輔助設計
  • 電腦視覺和模式識別
  • 硬體和架構
  • 軟體

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