TY - GEN
T1 - Understanding Effectiveness of Multi-Error-Bounded Lossy Compression for Preserving Ranges of Interest in Scientific Analysis
AU - Lin, Yuanjian
AU - Di, Sheng
AU - Zhao, Kai
AU - Jin, Sian
AU - Wang, Cheng
AU - Chard, Kyle
AU - Tao, Dingwen
AU - Foster, Ian
AU - Cappello, Franck
N1 - Funding Information:
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, under contract DE-AC02-06CH11357, and supported by the National Science Foundation under Grant SHF-1617488, OAC-2003709 and OAC-2003624/2042084. We acknowledge the computing resources provided on Bebop, which is operated by the Laboratory Computing Resource Center at Argonne National Laboratory.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Lossy compression frameworks have been proposed as a method to reduce the size of data produced by scientific simulations. However, they do so at the expense of precision and existing compressors apply a single error bound across the entire dataset. Varying the precision across user-specified ranges of scalar values appears to be a promising approach to further improve compression ratios while retaining precision in specific areas of interest. In this work, we investigate a specific compression method, based on the SZ framework, that can set multiple error bounds. We evaluate its effectiveness by applying it to real-world datasets which have concrete precision requirements. Our results show that the multi-error-bounded lossy compression can improve compression ration by 15 % with negligible overhead in compression time.
AB - Lossy compression frameworks have been proposed as a method to reduce the size of data produced by scientific simulations. However, they do so at the expense of precision and existing compressors apply a single error bound across the entire dataset. Varying the precision across user-specified ranges of scalar values appears to be a promising approach to further improve compression ratios while retaining precision in specific areas of interest. In this work, we investigate a specific compression method, based on the SZ framework, that can set multiple error bounds. We evaluate its effectiveness by applying it to real-world datasets which have concrete precision requirements. Our results show that the multi-error-bounded lossy compression can improve compression ration by 15 % with negligible overhead in compression time.
KW - n/a
UR - http://www.scopus.com/inward/record.url?scp=85124506279&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85124506279&partnerID=8YFLogxK
U2 - 10.1109/DRBSD754563.2021.00010
DO - 10.1109/DRBSD754563.2021.00010
M3 - Conference contribution
AN - SCOPUS:85124506279
T3 - Proceedings of DRBSD-7 2021: 7th International Workshop on Data Analysis and Reduction for Big Scientific Data, Held in conjunction with SC 2021: The International Conference for High Performance Computing, Networking, Storage and Analysis
SP - 40
EP - 46
BT - Proceedings of DRBSD-7 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Workshop on Data Analysis and Reduction for Big Scientific Data, DRBSD-7 2021
Y2 - 14 November 2021
ER -