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.
| Original language | English |
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| Title of host publication | Proceedings - 2018 IEEE International Conference on Cluster Computing, CLUSTER 2018 |
| Pages | 314-318 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781538683194 |
| DOIs | |
| State | Published - Oct 29 2018 |
| Event | 2018 IEEE International Conference on Cluster Computing, CLUSTER 2018 - Belfast, United Kingdom Duration: Sep 10 2018 → Sep 13 2018 |
Publication series
| Name | Proceedings - IEEE International Conference on Cluster Computing, ICCC |
|---|---|
| Volume | 2018-September |
| ISSN (Print) | 1552-5244 |
Conference
| Conference | 2018 IEEE International Conference on Cluster Computing, CLUSTER 2018 |
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| Country/Territory | United Kingdom |
| City | Belfast |
| Period | 9/10/18 → 9/13/18 |
Bibliographical note
Publisher Copyright:© 2018 IEEE.
Funding
ACKNOWLEDGE This research was supported by the Exascale Computing Project (ECP), Project Number: 17-SC-20-SC, a collaborative effort of two DOE organizations – the Office of Science and the National Nuclear Security Administration, responsible for the planning and preparation of a capable exascale ecosystem, including software, applications, hardware, advanced system engineering and early testbed platforms, to support the nation’s exascale computing imperative. The material was supported by the U.S. Department of Energy, Office of Science, under contract DE-AC02-06CH11357,and supported by the National Science Foundation under Grant No. 1619253. We gratefully acknowledge the computing resources provided on Bebop, a high-performance computing cluster operated by the Laboratory Computing Resource Center at Argonne National Laboratory.
| Funders | Funder number |
|---|---|
| National Science Foundation Arctic Social Science Program | 1619253 |
| U.S. Department of Energy Oak Ridge National Laboratory U.S. Department of Energy National Science Foundation National Energy Research Scientific Computing Center | DE-AC02-06CH11357 |
| Office of Science Programs | |
| National Nuclear Security Administration |
Keywords
- Lossy compression
- PSNR
- Scientific data
ASJC Scopus subject areas
- Software
- Signal Processing
- Hardware and Architecture