FAZ: A flexible auto-tuned modular error-bounded compression framework for scientific data

Jinyang Liu, Sheng Di, Kai Zhao, Xin Liang, Zizhong Chen, Franck Cappello

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

Error-bounded lossy compression has been effective to resolve the big scientific data issue because it has a great potential to significantly reduce the data volume while allowing users to control data distortion based on specified error bounds. However, none of the existing error-bounded lossy compressors can always obtain the best compression quality because of the diverse characteristics of different datasets. In this paper, we develop FAZ, a flexible and adaptive error-bounded lossy compression framework, which projects a fairly high capability of adapting to diverse datasets. FAZ can always keep the compression quality at the best level compared with other state-of-the-art compressors for different datasets. We perform a comprehensive evaluation using 6 real-world scientific applications and 6 other state-of-the-art error-bounded lossy compressors. Experiments show that compared with the other existing lossy compressors, FAZ can improve the compression ratio by up to 120%, 190%, and 75% when setting the same error bound, the same PSNR and the same SSIM, respectively.

Original languageEnglish
Title of host publicationACM ICS 2023 - Proceedings of the International Conference on Supercomputing
Pages1-13
Number of pages13
ISBN (Electronic)9798400700569
DOIs
StatePublished - Jun 21 2023
Event37th ACM International Conference on Supercomputing, ICS 2023 - Orlando, United States
Duration: Jun 21 2023Jun 23 2023

Publication series

NameProceedings of the International Conference on Supercomputing

Conference

Conference37th ACM International Conference on Supercomputing, ICS 2023
Country/TerritoryUnited States
CityOrlando
Period6/21/236/23/23

Bibliographical note

Publisher Copyright:
© 2023 ACM.

Keywords

  • data compression
  • high performance computing

ASJC Scopus subject areas

  • General Computer Science

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