Journal article
A Survey on Error-Bounded Lossy Compression for Scientific Datasets
ACM computing surveys, Vol.57(11), pp.1-38
11/30/2025
DOI: 10.1145/3733104
Abstract
Error-bounded lossy compression has been effective in significantly reducing the data storage/transfer burden while preserving the reconstructed data fidelity very well. Many error-bounded lossy compressors have been developed for a wide range of parallel and distributed use cases for years. They are designed with distinct compression models and principles, such that each of them features particular pros and cons. In this paper we provide a comprehensive survey of emerging error-bounded lossy compression techniques. The key contribution is fourfold. (1) We summarize a novel taxonomy of lossy compression into 6 classic models. (2) We provide a comprehensive survey of 10 commonly used compression components/modules. (3) We summarized pros and cons of 47 state-of-the-art lossy compressors and present how state-of-the-art compressors are designed based on different compression techniques. (4) We discuss how customized compressors are designed for specific scientific applications and use-cases. We believe this survey is useful to multiple communities including scientific applications, high-performance computing, lossy compression, and big data.
Details
- Title: Subtitle
- A Survey on Error-Bounded Lossy Compression for Scientific Datasets
- Creators
- Sheng Di - Argonne National LaboratoryJinyang Liu - University of California, RiversideKai Zhao - Florida State UniversityXin Liang - University of KentuckyRobert Underwood - Argonne National LaboratoryZhaorui Zhang - Hong Kong Polytechnic UniversityMilan Shah - North Carolina State UniversityYafan Huang - University of IowaJiajun Huang - University of California, RiversideXiaodong Yu - Stevens Institute of TechnologyCongrong Ren - The Ohio State UniversityHanqi Guo - The Ohio State UniversityGrant Wilkins - University of CambridgeDingwen Tao - Indiana University BloomingtonJiannan Tian - Indiana University BloomingtonSian Jin - Temple UniversityZizhe Jian - University of California, RiversideDaoce Wang - Indiana University BloomingtonMd Hasanur Rahman - University of IowaBoyuan Zhang - Indiana University BloomingtonShihui Song - University of IowaJon Calhoun - Clemson UniversityGuanpeng Li - University of IowaKazutomo Yoshii - Argonne National LaboratoryKhalid Alharthi - University of BihaćFranck Cappello - Argonne National Laboratory
- Resource Type
- Journal article
- Publication Details
- ACM computing surveys, Vol.57(11), pp.1-38
- DOI
- 10.1145/3733104
- ISSN
- 0360-0300
- eISSN
- 1557-7341
- Publisher
- ACM
- Grant note
- U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research (ASCR): DE-AC02-06CH11357, DE-SC0024559 National Science Foundation: OAC-2104023, OAC-2311875, OAC-2311876, OAC-2311878, OAC-2330367, OAC-2311756, OAC-2313123, OAC-2344717, SHF-1943114, SHF-1910197, OAC-2313122, NSF-2211538 Deanship of Graduate Studies and Scientific Research at University of Bisha: UB-Promising-40-1445
This research was supported by the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research (ASCR) , under contract DE-AC02-06CH11357 and DE-SC0024559, and supported by the National Science Foundation under Grant OAC-2104023, OAC-2311875, OAC-2311876, OAC-2311878, OAC-2330367, OAC-2311756, OAC-2311878, OAC-2313123, OAC-2344717, SHF-1943114, SHF-1910197, OAC-2313122 and NSF-2211538. The authors extend their appreciation to the Deanship of Graduate Studies and Scientific Research at University of Bisha for funding this research through the promising program under grant number (UB-Promising-40-1445) .
- Language
- English
- Electronic publication date
- 05/02/2025
- Date published
- 11/30/2025
- Academic Unit
- Computer Science
- Record Identifier
- 9984820569202771
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