TY - GEN
T1 - FAZ
T2 - 37th ACM International Conference on Supercomputing, ICS 2023
AU - Liu, Jinyang
AU - Di, Sheng
AU - Zhao, Kai
AU - Liang, Xin
AU - Chen, Zizhong
AU - Cappello, Franck
N1 - This research was supported by the Exascale Computing Project (ECP), Project Number: 17-SC-20-SC, a collaborative effort of two DOE organizations – the Office of Science and the National Nuclear Security Administration, responsible for the planning and preparation of a capable exascale ecosystem, including software, applications, hardware, advanced system engineering and early testbed platforms, to support the nation’s exascale computing imperative. The material was supported by the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research (ASCR), under contract DE-AC02-06CH11357, and supported by the National Science Foundation under Grant OAC-2003709, OAC-2104023 and OAC-2153451. We acknowledge the computing resources provided on Bebop (operated by Laboratory Computing Resource Center at Argonne) and on Theta and JLSE (operated by Argonne Leadership Computing Facility).
PY - 2023/6/21
Y1 - 2023/6/21
N2 - 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.
AB - 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.
KW - data compression
KW - high performance computing
UR - http://www.scopus.com/inward/record.url?scp=85168425872&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85168425872&partnerID=8YFLogxK
U2 - 10.1145/3577193.3593721
DO - 10.1145/3577193.3593721
M3 - Conference contribution
AN - SCOPUS:85168425872
T3 - Proceedings of the International Conference on Supercomputing
SP - 1
EP - 13
BT - ACM ICS 2023 - Proceedings of the International Conference on Supercomputing
PB - Association for Computing Machinery
Y2 - 21 June 2023 through 23 June 2023
ER -