Optimizing Error-Bounded Lossy Compression for Scientific Data by Dynamic Spline Interpolation

Conference · Fri Jan 01 04:00:00 UTC 2021

DOI:https://doi.org/10.1109/ICDE51399.2021.00145· OSTI ID:1864145

Zhao, Kai; Di, Sheng; Dmitriev, Maxim; Tonellot, Thierry-Laurent D.; Chen, Zhizhong; Cappello, Franck

Today's scientific simulations are producing vast volumes of data that cannot be stored and transferred efficiently because of limited storage capacity, parallel I/O bandwidth, and network bandwidth. The situation is getting worse over time because of the ever-increasing gap between relatively slow data transfer speed and fast-growing computation power in modern supercomputers. Error-bounded lossy compression is becoming one of the most critical techniques for resolving the big scientific data issue, in that it can significantly reduce the scientific data volume while guaranteeing that the reconstructed data is valid for users because of its compression-error-bounding feature. In this paper, we present a novel error-bounded lossy compressor based on a state-of-the-art prediction-based compression framework. Our solution exhibits substantially better compression quality than all of the existing error-bounded lossy compressors, with comparable compression speed. Specifically, our contribution is threefold. (1) We provide an in-depth analysis of why the best-existing prediction-based lossy compressor can only minimally improve the compression quality. (2) We propose a dynamic spline interpolation approach with a series of optimization strategies that can significantly improve the data prediction accuracy, substantially improving the compression quality in turn. (3) We perform a thorough evaluation using six real-world scientific simulation datasets across different science domains to evaluate our solution vs. all other related works. Experiments show that the compression ratio of our solution is higher than that of the second-best lossy compressor by 20%similar to 460% with the same error bound in most of the cases.

• Research Organization: Argonne National Laboratory (ANL)
• Sponsoring Organization: USDOE Exascale Computing Project (ECP); USDOE Office of Science; National Science Foundation (NSF)
• DOE Contract Number: AC02-06CH11357
• OSTI ID: 1864145
• Country of Publication: United States
• Language: English