Exploring Autoencoder-based Error-bounded Compression for Scientific Data
Exploring Autoencoder-based Error-bounded Compression for Scientific Data
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DOI：
10.1109/cluster48925.2021.00034
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
Jinyang Liu;S. Di;Kai Zhao;Sian Jin;Dingwen Tao;Xin Liang;Zizhong Chen;F. Cappello
中科院分区：
文献类型：
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作者：
Jinyang Liu;S. Di;Kai Zhao;Sian Jin;Dingwen Tao;Xin Liang;Zizhong Chen;F. Cappello
遇到的损失压缩正成为当今科学项目的不可或缺的技术，这些项目在模拟或仪器数据获取过程中产生的大量数据不仅可以显着降低数据尺寸，而且还可以基于用户指定的误差范围来控制压缩错误。科学应用是高度要求的。遇到的压缩框架，对块大小和潜在尺寸进行微调，并优化了潜在矢量的压缩效率（3）。 $ \ sim $ 800％的压缩率提高了，数据失真相同），与SZ2.1和ZFP相比，压缩率很高。
Error-bounded lossy compression is becoming an indispensable technique for the success of today’s scientific projects with vast volumes of data produced during the simulations or instrument data acquisitions. Not only can it significantly reduce data size, but it also can control the compression errors based on user-specified error bounds. Autoencoder (AE) models have been widely used in image compression, but few AE-based compression approaches support error-bounding features, which are highly required by scientific applications. To address this issue, we explore using convolutional autoencoders to improve error-bounded lossy compression for scientific data, with the following three key contributions. (1) We provide an in-depth investigation of the characteristics of various autoencoder models and develop an error-bounded autoencoder-based framework in terms of the SZ model. (2) We optimize the compression quality for main stages in our designed AE-based error-bounded compression framework, fine-tuning the block sizes and latent sizes and also optimizing the compression efficiency of latent vectors. (3) We evaluate our proposed solution using five real-world scientific datasets and comparing them with six other related works. Experiments show that our solution exhibits a very competitive compression quality from among all the compressors in our tests. In absolute terms, it can obtain a much better compression quality (100% $\sim$ 800% improvement in compression ratio with the same data distortion) compared with SZ2.1 and ZFP in cases with a high compression ratio.