GWLZ: A Group-wise Learning-based Lossy Compression Framework for Scientific Data-Reference-Cited by-同舟云学术

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GWLZ: A Group-wise Learning-based Lossy Compression Framework for Scientific Data

Published:2024-06-03 Issue: Volume: Page:34-41
ISSN:
Container-title:Proceedings of the 14th Workshop on AI and Scientific Computing at Scale using Flexible Computing Infrastructures
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Author:

Jia Wenqi¹^ORCID,Jin Sian²^ORCID,Wang Jinzhen³^ORCID,Niu Wei⁴^ORCID,Tao Dingwen⁵^ORCID,Yin Miao¹^ORCID

Affiliation:

1. Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, United States of America

2. Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA

3. Department of Computer and Information Science, City University of New York - Brooklyn College, Brooklyn, NY, USA

4. School of Computing, University of Georgia, Athens, GA, United States of America

5. School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States of America

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3659995.3660041

Reference37 articles.

1. Nyx: A MASSIVELY PARALLEL AMR CODE FOR COMPUTATIONAL COSMOLOGY

2. Yann Collet and EM Kucherawy. 2015. Zstandard-real-time data compression algorithm. Zstandard-real-time data compression algorithm (2015).

3. Sheng Di and Franck Cappello. 2016. Fast error-bounded lossy HPC data compression with SZ. In 2016 ieee international parallel and distributed processing symposium (ipdps). IEEE, 730--739.

4. Image Generation: A Review

5. Brief review of image denoising techniques

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