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 language | English |
---|---|
Title of host publication | ACM ICS 2023 - Proceedings of the International Conference on Supercomputing |
Pages | 1-13 |
Number of pages | 13 |
ISBN (Electronic) | 9798400700569 |
DOIs | |
State | Published - Jun 21 2023 |
Event | 37th ACM International Conference on Supercomputing, ICS 2023 - Orlando, United States Duration: Jun 21 2023 → Jun 23 2023 |
Publication series
Name | Proceedings of the International Conference on Supercomputing |
---|
Conference
Conference | 37th ACM International Conference on Supercomputing, ICS 2023 |
---|---|
Country/Territory | United States |
City | Orlando |
Period | 6/21/23 → 6/23/23 |
Bibliographical note
Publisher Copyright:© 2023 ACM.
Keywords
- data compression
- high performance computing
ASJC Scopus subject areas
- General Computer Science