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UID:submissions.supercomputing.org_SC23_sess431_ws_drbsd102@linklings.com
SUMMARY:Analyzing Impact of Data Reduction Techniques on Visualization for
  AMR Applications Using AMReX Framework
DESCRIPTION:Daoce Wang (Indiana University), Jesus Pulido and Pascal Gross
 et (Los Alamos National Laboratory (LANL)), Jiannan Tian (Indiana Universi
 ty), James Ahrens (Los Alamos National Laboratory (LANL)), and Dingwen Tao
  (Indiana University)\n\nToday’s scientific simulations generate exception
 ally large volumes of data, challenging the capacities of available I/O ba
 ndwidth and storage space. This necessitates a substantial reduction in da
 ta volume, for which error-bounded lossy compression has emerged as a high
 ly effective strategy. A crucial metric for assessing the efficacy of loss
 y compression is visualization. Despite extensive research on the impact o
 f compression on visualization, there is a notable gap in the literature c
 oncerning the effects of compression on the visualization of Adaptive Mesh
  Refinement (AMR) data. AMR has proven to be a potent solution for the ris
 ing computational intensity and the explosive growth in data volume. Howev
 er, the hierarchical and multi-resolution characteristics of AMR data intr
 oduce unique challenges to its visualization, and these challenges are fur
 ther compounded when data compression comes into play. This article study 
 the intricacies of how data compression influences and introduces novel ch
 allenges to the visualization of AMR data.\n\nTag: Data Analysis, Visualiz
 ation, and Storage, Data Compression\n\nRegistration Category: Workshop Re
 g Pass\n\nSession Chairs: Sheng Di (Argonne National Laboratory (ANL), Uni
 versity of Chicago); Dingwen Tao (Institute of Computing Technology, Chine
 se Academy of Sciences; University of Chinese Academy of Sciences); Ana Ga
 inaru (Oak Ridge National Laboratory (ORNL)); Jieyang Chen (University of 
 Oregon); Shadi Ibrahim (French Institute for Research in Computer Science 
 and Automation (INRIA)); and Xin Liang (Oregon State University)\n\n
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