Statistical super resolution for data analysis and visualization of large scale cosmological simulations

Ko Chih Wang, Jiayi Xu, Jonathan Woodring, Han Wei Shen

研究成果: 書貢獻/報告類型會議論文篇章

5 引文 斯高帕斯(Scopus)

摘要

Cosmologists build simulations for the evolution of the universe using different initial parameters. By exploring the datasets from different simulation runs, cosmologists can understand the evolution of our universe and approach its initial conditions. A cosmological simulation nowadays can generate datasets on the order of petabytes. Moving datasets from the supercomputers to post data analysis machines is infeasible. We propose a novel approach called statistical super-resolution to tackle the big data problem for cosmological data analysis and visualization. It uses datasets from a few simulation runs to create a prior knowledge, which captures the relation between low-and high-resolution data. We apply in situ statistical down-sampling to datasets generated from simulation runs to minimize the requirements of I/O bandwidth and storage. High-resolution datasets are reconstructed from the statistical down-sampled data by using the prior knowledge for scientists to perform advanced data analysis and render high-quality visualizations.

原文英語
主出版物標題Proceedings - 2019 IEEE Pacific Visualization Symposium, PacificVis 2019
發行者IEEE Computer Society
頁面303-312
頁數10
ISBN(電子)9781538692264
DOIs
出版狀態已發佈 - 2019 4月
對外發佈
事件12th IEEE Pacific Visualization Symposium, PacificVis 2019 - Bangkok, 泰国
持續時間: 2019 4月 232019 4月 26

出版系列

名字IEEE Pacific Visualization Symposium
2019-April
ISSN(列印)2165-8765
ISSN(電子)2165-8773

會議

會議12th IEEE Pacific Visualization Symposium, PacificVis 2019
國家/地區泰国
城市Bangkok
期間2019/04/232019/04/26

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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