Paper Info
Title
An Efficient Transformation Scheme for Lossy Data Compression with Point-Wise Relative Error Bound
Abstract
Because of the ever-increasing execution scale of scientific applications, how to store the extremely large volume of data efficiently is becoming a serious issue. A significant reduction of the scientific data size can effectively mitigate the I/O burden and save considerable storage space. Since lossless compressors suffer from limited compression ratios, error-controlled lossy compressors have been studied for years. Existing error-controlled lossy compressors, however, focus mainly on absolute error bounds, which cannot meet users' diverse demands such as pointwise relative error bounds. Although some of the state-of-the-art lossy compressors support pointwise relative error bound, the compression ratios are generally low because of the limitation in their designs and possible spiky data changes in local data regions. In this work, we propose a novel, efficient approach to perform compression based on the pointwise relative error bound with higher compression ratios than existing solutions provide. Our contribution is threefold. (1) We propose a novel transformation scheme that can transfer the pointwise relative-error-bounded compression problem to an absolute-error-bounded compression issue. We also analyze the practical properties of our transformation scheme both theoretically and experimentally. (2) We implement the proposed technique in two of the most popular absolute-error-bounded lossy compressors, SZ and ZFP. (3) We evaluate our solution using multiple real-world application data across different scientific domains on a supercomputer with up to 4,096 cores and 12 TB of data. Experiments show that our solution achieves over 1.38X dumping and 1.31X loading performance over the second-best lossy compressor, respectively.
Year
DOI
Venue
2018
10.1109/CLUSTER.2018.00036
2018 IEEE International Conference on Cluster Computing (CLUSTER)
Keywords
Field
DocType
Lossy compression,scientific simulations,point wise error bound
Data modeling,Lossy compression,Supercomputer,Computer science,Parallel computing,Algorithm,Compression ratio,Data compression,Approximation error,Pointwise,Lossless compression
Conference
ISSN
ISBN
Citations 
1552-5244
978-1-5386-8320-0
6
PageRank 
References 
Authors
0.41
8
5
Name
Order
Citations
PageRank
Xin Liang110712.74
Sheng Di273755.88
Dingwen Tao312917.66
Zizhong Chen492469.93
Franck Cappello53775251.47