Jinyang Liu, S. Di, Kai Zhao, Xin Liang, Zizhong Chen, F. Cappello
{"title":"FAZ: A flexible auto-tuned modular error-bounded compression framework for scientific data","authors":"Jinyang Liu, S. Di, Kai Zhao, Xin Liang, Zizhong Chen, F. Cappello","doi":"10.1145/3577193.3593721","DOIUrl":null,"url":null,"abstract":"Error-bounded lossy compression has been effective to resolve the big scientific data issue because it has a great potential to significantly reduce the data volume while allowing users to control data distortion based on specified error bounds. However, none of the existing error-bounded lossy compressors can always obtain the best compression quality because of the diverse characteristics of different datasets. In this paper, we develop FAZ, a flexible and adaptive error-bounded lossy compression framework, which projects a fairly high capability of adapting to diverse datasets. FAZ can always keep the compression quality at the best level compared with other state-of-the-art compressors for different datasets. We perform a comprehensive evaluation using 6 real-world scientific applications and 6 other state-of-the-art error-bounded lossy compressors. Experiments show that compared with the other existing lossy compressors, FAZ can improve the compression ratio by up to 120%, 190%, and 75% when setting the same error bound, the same PSNR and the same SSIM, respectively.","PeriodicalId":424155,"journal":{"name":"Proceedings of the 37th International Conference on Supercomputing","volume":"57 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 37th International Conference on Supercomputing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3577193.3593721","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Error-bounded lossy compression has been effective to resolve the big scientific data issue because it has a great potential to significantly reduce the data volume while allowing users to control data distortion based on specified error bounds. However, none of the existing error-bounded lossy compressors can always obtain the best compression quality because of the diverse characteristics of different datasets. In this paper, we develop FAZ, a flexible and adaptive error-bounded lossy compression framework, which projects a fairly high capability of adapting to diverse datasets. FAZ can always keep the compression quality at the best level compared with other state-of-the-art compressors for different datasets. We perform a comprehensive evaluation using 6 real-world scientific applications and 6 other state-of-the-art error-bounded lossy compressors. Experiments show that compared with the other existing lossy compressors, FAZ can improve the compression ratio by up to 120%, 190%, and 75% when setting the same error bound, the same PSNR and the same SSIM, respectively.