Understanding Effectiveness of Multi-Error-Bounded Lossy Compression for Preserving Ranges of Interest in Scientific Analysis

Yuanjian Lin, Sheng Di, Kai Zhao, Sian Jin, Cheng Wang, Kyle Chard, Dingwen Tao, Ian Foster, Franck Cappello

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationProceedings of DRBSD-7 2021
Subtitle of host publication7th 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
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages40-46
Number of pages7
ISBN (Electronic)9781728186726
DOIs
StatePublished - 2021
Externally publishedYes
Event7th International Workshop on Data Analysis and Reduction for Big Scientific Data, DRBSD-7 2021 - St. Louis, United States
Duration: Nov 14 2021 → …

Publication series

NameProceedings 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

Conference

Conference7th International Workshop on Data Analysis and Reduction for Big Scientific Data, DRBSD-7 2021
Country/TerritoryUnited States
CitySt. Louis
Period11/14/21 → …

Keywords

  • n/a

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems
  • Information Systems and Management
  • Statistics, Probability and Uncertainty
  • Media Technology

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