Error-Controlled Lossy Compression Optimized for High Compression Ratios of Scientific Datasets
Abstract
Today's scientific simulations require a significant reduction of the data size because of extremely large volumes of data they produce and the limitation of storage bandwidth and space. If the compression is set to reach a high compression ratio, however, the reconstructed data are often distorted too much to tolerate. In this paper, we explore a new compression strategy that can effectively control the data distortion when significantly reducing the data size. The contribution is threefold. (1) We propose an adaptive compression framework to select either our improved Lorenzo prediction method or our optimized linear regression method dynamically in different regions of the dataset. (2) We explore how to select them accurately based on the data features in each block to obtain the best compression quality. (3) We analyze the effectiveness of our solution in details using four real-world scientific datasets with 100+ fields. Evaluation results confirm that our new adaptive solution can significantly improve the rate distortion for the lossy compression with fairly high compression ratios. The compression ratio of our compressor is 1.5X~8X as high as that of two other leading lossy compressors (SZ and ZFP) with the same peak single-to-noise ratio (PSNR), in the high-compression cases. Parallel experiments with 8,192 cores and 24 TB of data shows that our solution obtains 1.86X dumping performance and 1.95X loading performance compared with the second-best lossy compressor, respectively.
Recommended Citation
X. Liang et al., "Error-Controlled Lossy Compression Optimized for High Compression Ratios of Scientific Datasets," Proceedings of the 2018 IEEE International Conference on Big Data, Big Data 2018, pp. 438 - 447, Institute of Electrical and Electronics Engineers (IEEE), Jan 2019.
The definitive version is available at https://doi.org/10.1109/BigData.2018.8622520
Meeting Name
2018 IEEE International Conference on Big Data, Big Data 2018 (2018: Dec. 10-13, Seattle, WA)
Department(s)
Computer Science
International Standard Book Number (ISBN)
978-153865035-6
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2019 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
22 Jan 2019
Comments
This research was supported by the Exascale Computing Project (ECP), Project Number: 17-SC-20-SC. This research is also supported by NSF Award No. 1513201.