Analyzing Impact of Data Reduction Techniques on Visualization for AMR Applications Using AMReX Framework

• Main Authors: Wang, Daoce, Pulido, Jesus, Grosset, Pascal, Tian, Jiannan, Ahrens, James, Tao, Dingwen
• Format: Journal Article
• Language: English
• Published: 29-09-2023
• Subjects: Computer Science - Distributed, Parallel, and Cluster Computing
• DOI: 10.48550/arxiv.2309.16980

Today's scientific simulations generate exceptionally large volumes of data,
challenging the capacities of available I/O bandwidth and storage space. This
necessitates a substantial reduction in data volume, for which error-bounded
lossy compression has emerged as a highly effective strategy. A crucial metric
for assessing the efficacy of lossy compression is visualization. Despite
extensive research on the impact of compression on visualization, there is a
notable gap in the literature concerning the effects of compression on the
visualization of Adaptive Mesh Refinement (AMR) data. AMR has proven to be a
potent solution for addressing the rising computational intensity and the
explosive growth in data volume that requires storage and transmission.
However, the hierarchical and multi-resolution characteristics of AMR data
introduce unique challenges to its visualization, and these challenges are
further compounded when data compression comes into play. This article delves
into the intricacies of how data compression influences and introduces novel
challenges to the visualization of AMR data.