CliZ: Optimizing Lossy Compression for Climate Datasets with Adaptive Fine-tuned Data Prediction-Reference-Cited by-同舟云学术

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CliZ: Optimizing Lossy Compression for Climate Datasets with Adaptive Fine-tuned Data Prediction

Published:2024-05-27 Issue: Volume:145 Page:417-429
ISSN:
Container-title:2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
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Author:

Jian Zizhe¹,Di Sheng²,Liu Jinyang¹,Zhao Kai³,Liang Xin⁴,Xu Haiying⁵,Underwood Robert²,Wu Shixun¹,Huang Jiajun¹,Chen Zizhong¹,Cappello Franck²

Affiliation:

1. University of California,Riverside,CA,USA

2. Argonne National Laboratory,Lemont,IL,USA

3. Florida State University,Tallahassee,FL,USA

4. University of Kentucky,Lexington,KY,USA

5. National Center for Atmospheric Research,Boulder,CO,USA

Funder

U.S. Department of Energy

Office of Science

Advanced Scientific Computing Research

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10579076/10579083/10579165.pdf?arnumber=10579165

Reference35 articles.

1. The Community Earth System Model (CESM) Large Ensemble Project: A Community Resource for Studying Climate Change in the Presence of Internal Climate Variability

2. The Community Earth System Model: A Framework for Collaborative Research

3. Fast Error-Bounded Lossy HPC Data Compression with SZ

4. Significantly Improving Lossy Compression for Scientific Data Sets Based on Multidimensional Prediction and Error-Controlled Quantization

5. Fixed-Rate Compressed Floating-Point Arrays

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