A Generic Performance Metric for Scientific Data Compression
The performance outcomes of scientific data compression are complex in nature, often with one performance metric being more prioritized/optimized over others in the algorithm. To balance between different performance metrics, this paper formulates a new generic metric and uses it for searching an optimal configuration that leads to a balanced compression performance.
Authors
- Zhenlu Qin (ORCID: https://orcid.org/0000-0002-0408-9853)
- Jinzhen Wang (ORCID: https://orcid.org/0000-0001-6317-2940)
- Hongjian Zhu (ORCID: https://orcid.org/0000-0003-4326-7381)
- Jessie Gu
- Maggie Liu
Institutions
- University of North Carolina at Charlotte (US)
- Childrens Cardiomyopathy Foundation (US)
- University of Maryland, College Park (US)
- Auburn University at Montgomery (US)
Publication Details
- Published
- 2024-09-16
- DOI
- https://doi.org/10.1109/e-science62913.2024.10678705
- Primary Topic
- Distributed and Parallel Computing Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00