@inproceedings{6ac180fbf240429e8de01c337552230b,
title = "Understanding Effectiveness of Multi-Error-Bounded Lossy Compression for Preserving Ranges of Interest in Scientific Analysis",
abstract = "Lossy compression frameworks have been proposed as a method to reduce the size of data produced by scientific simulations. However, they do so at the expense of precision and existing compressors apply a single error bound across the entire dataset. Varying the precision across user-specified ranges of scalar values appears to be a promising approach to further improve compression ratios while retaining precision in specific areas of interest. In this work, we investigate a specific compression method, based on the SZ framework, that can set multiple error bounds. We evaluate its effectiveness by applying it to real-world datasets which have concrete precision requirements. Our results show that the multi-error-bounded lossy compression can improve compression ration by 15 % with negligible overhead in compression time.",
keywords = "n/a",
author = "Yuanjian Lin and Sheng Di and Kai Zhao and Sian Jin and Cheng Wang and Kyle Chard and Dingwen Tao and Ian Foster and Franck Cappello",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 7th International Workshop on Data Analysis and Reduction for Big Scientific Data, DRBSD-7 2021 ; Conference date: 14-11-2021",
year = "2021",
doi = "10.1109/DRBSD754563.2021.00010",
language = "English (US)",
series = "Proceedings of DRBSD-7 2021: 7th International Workshop on Data Analysis and Reduction for Big Scientific Data, Held in conjunction with SC 2021: The International Conference for High Performance Computing, Networking, Storage and Analysis",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "40--46",
booktitle = "Proceedings of DRBSD-7 2021",
address = "United States",
}