Title: Z-checker: A Framework for Assessing Lossy Compression of Scientific Data,
Abstract: Because of 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 datasets 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 paper, 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 of any dataset to improve compression
strategies. For lossy compression users, Z-checker can detect the compression
quality, provide various global distortion analysis comparing the original data
with the decompressed data and statistical analysis of the compression error.
Z-checker can perform the analysis with either coarse granularity or fine
granularity, such that the users and developers can select the best-fit,
adaptive compressors for different parts of the dataset. Z-checker features a
visualization interface displaying all analysis results in addition to some
basic views of the datasets such as time series. To the best of our knowledge,
Z-checker is the first tool designed to assess lossy compression
comprehensively for scientific datasets.