Title

Fixed-PSNR Lossy Compression for Scientific Data

Abstract

Error-controlled lossy compression has been studied for years because of extremely large volumes of data being produced by today's scientific simulations. None of existing lossy compressors, however, allow users to fix the peak signal-to-noise ratio (PSNR) during compression, although PSNR has been considered as one of the most significant indicators to assess compression quality. In this paper, we propose a novel technique providing a fixed-PSNR lossy compression for scientific data sets. We implement our proposed method based on the SZ lossy compression framework and release the code as an open-source toolkit. We evaluate our fixed-PSNR compressor on three realworld high-performance computing data sets. Experiments show that our solution has a high accuracy in controlling PSNR, with an average deviation of 0.1 ~ 5.0 dB on the tested data sets.

Meeting Name

2018 IEEE International Conference on Cluster Computing, ICCC (2018: Sep. 10-13, Belfast, UK)

Department(s)

Computer Science

Comments

This research was supported by the Exascale Computing Project (ECP), Project Number: 17-SC-20-SCional Science Foundation, Grant 1619253

Keywords and Phrases

Lossy Compression; PSNR; Scientific Data

International Standard Book Number (ISBN)

978-153868319-4

International Standard Serial Number (ISSN)

1552-5244

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2018 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

29 Oct 2018

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