Paper Info
Title
OptZConfig: Efficient Parallel Optimization of Lossy Compression Configuration
Abstract
Lossless compressors have very low compression ratios that do not meet the needs of today’s large-scale scientific applications that produce vast volumes of data. Error-bounded lossy compression (EBLC) is considered a critical technique for the success of scientific research. Although EBLC allows users to set an error bound for the compression, users have been unable to specify the requirements on the compression quality, limiting practical use. Our contributions are: (1) We formulate the problem of configuring EBLC to preserve a user-defined metric as an optimization problem. This allows many classes of new metrics to be preserved, which improves over current practices. (2) We present a framework, OptZConfig, that can adapt to improvements in the search algorithm, compressor, and metrics with minimal changes, enabling future advancements in this area. (3) We demonstrate the advantages of our approach against the leading methods to configure compressors to preserve specific metrics. Our approach improves compression ratios against a specialized compressor by up to <inline-formula><tex-math notation="LaTeX">$3\times$</tex-math></inline-formula> , has a 56× speedup over FRaZ, 1000× speedup over MGARD-QOI post tuning, and 110× speedup over systematic approaches which had not been bounded by compressors before.
Year
DOI
Venue
2022
10.1109/TPDS.2022.3154096
IEEE Transactions on Parallel and Distributed Systems
Keywords
DocType
Volume
Error bounded lossy compression,LibPressio,non-linear optimization,parallel computing
Journal
33
Issue
ISSN
Citations 
12
1045-9219
0
PageRank 
References 
Authors
0.34
15
5
Name
Order
Citations
PageRank
Robert Underwood141.42
Jon C. Calhoun233.41
Sheng Di373755.88
Amy W. Apon410418.27
Franck Cappello53775251.47