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Author:

Tao, Dingwen (Tao, Dingwen.) | Di, Sheng (Di, Sheng.) | Guo, Hanqi (Guo, Hanqi.) | Chen, Zizhong (Chen, Zizhong.) | Cappello, Franck (Cappello, Franck.)

Indexed by:

EI Scopus SCIE

Abstract:

Because of the vast volume of data being produced by today's scientific simulations and experiments, lossy data compressor allowing user-controlled loss of accuracy during the compression is a relevant solution for significantly reducing the data size. However, lossy compressor developers and users are missing a tool to explore the features of scientific data sets and understand the data alteration after compression in a systematic and reliable way. To address this gap, we have designed and implemented a generic framework called Z-checker. On the one hand, Z-checker combines a battery of data analysis components for data compression. On the other hand, Z-checker is implemented as an open-source community tool to which users and developers can contribute and add new analysis components based on their additional analysis demands. In this article, we present a survey of existing lossy compressors. Then, we describe the design framework of Z-checker, in which we integrated evaluation metrics proposed in prior work as well as other analysis tools. Specifically, for lossy compressor developers, Z-checker can be used to characterize critical properties (such as entropy, distribution, power spectrum, principal component analysis, and autocorrelation) of any data set to improve compression strategies. For lossy compression users, Z-checker can detect the compression quality (compression ratio and bit rate) and provide various global distortion analysis comparing the original data with the decompressed data (peak signal-to-noise ratio, normalized mean squared error, rate-distortion, rate-compression error, spectral, distribution, and derivatives) and statistical analysis of the compression error (maximum, minimum, and average error; autocorrelation; and distribution of errors). Z-checker can perform the analysis with either coarse granularity (throughout the whole data set) or fine granularity (by user-defined blocks), such that the users and developers can select the best fit, adaptive compressors for different parts of the data set. Z-checker features a visualization interface displaying all analysis results in addition to some basic views of the data sets such as time series. To the best of our knowledge, Z-checker is the first tool designed to assess lossy compression comprehensively for scientific data sets.

Keyword:

data analytics lossy compression scientific data Framework visualization assessment tool

Author Community:

  • [ 1 ] [Tao, Dingwen]Univ Calif Riverside, Dept Comp Sci & Engn, Riverside, CA 92521 USA
  • [ 2 ] [Chen, Zizhong]Univ Calif Riverside, Dept Comp Sci & Engn, Riverside, CA 92521 USA
  • [ 3 ] [Di, Sheng]Argonne Natl Lab, Div Comp Sci & Math, 9700 Cass Ave, Lemont, IL 60439 USA
  • [ 4 ] [Guo, Hanqi]Argonne Natl Lab, Div Comp Sci & Math, 9700 Cass Ave, Lemont, IL 60439 USA
  • [ 5 ] [Cappello, Franck]Argonne Natl Lab, Div Comp Sci & Math, 9700 Cass Ave, Lemont, IL 60439 USA
  • [ 6 ] [Chen, Zizhong]Beijing Univ Technol, Beijing, Peoples R China
  • [ 7 ] [Cappello, Franck]Univ Illinois, Parallel Comp Inst, Champaign, IL USA

Reprint Author's Address:

  • [Cappello, Franck]Argonne Natl Lab, Div Comp Sci & Math, 9700 Cass Ave, Lemont, IL 60439 USA

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Source :

INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS

ISSN: 1094-3420

Year: 2019

Issue: 2

Volume: 33

Page: 285-303

3 . 1 0 0

JCR@2022

ESI Discipline: COMPUTER SCIENCE;

ESI HC Threshold:147

JCR Journal Grade:2

Cited Count:

WoS CC Cited Count: 35

SCOPUS Cited Count: 45

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 8

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