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High-performance Effective Scientific Error-bounded Lossy Compression with Auto-tuned Multi-component Interpolation by Liu, Jinyang; Di, Sheng; Zhao, Kai; Liang, Xin; Jin, Sian; Jian, Zizhe; Huang, Jiajun; Wu, Shixun; Chen, Zizhong; Cappello, Franck is a scholarly article available to read on EtoBox.

Error-bounded lossy compression has been identified as a promising solution for significantly reducing scientific data volumes upon users' requirements on data distortion. For the existing scientific error-bounded lossy compressors, some of them (such as SPERR and FAZ) can reach fairly high compression ratios and some others (such as SZx, SZ, and ZFP) feature high compression speeds, but they rarely exhibit both high ratio and high speed meanwhile. In this paper, we propose HPEZ with newly-designed interpolations and quality-metric-driven auto-tuning, which features significantly improved compression quality upon the existing high-performance compressors, meanwhile being exceedingly faster than high-ratio compressors. The key contributions lie in the following points: (1) We develop a series of advanced techniques such as interpolation re-ordering, multi-dimensional interpolation, and natural cubic splines to significantly improve compression qualities with interpolation-based data prediction. (2) The auto-tuning module in HPEZ has been carefully designed with novel strategies, including but not limited to block-wise interpolation tuning, dynamic dimension freezing, and Lorenzo tun

Author

Liu, Jinyang; Di, Sheng; Zhao, Kai; Liang, Xin; Jin, Sian; Jian, Zizhe; Huang, Jiajun; Wu, Shixun; Chen, Zizhong; Cappello, Franck

Published

2023

Language

EN