Spatiotemporally Adaptive Compression for Scientific Dataset with Feature Preservation – A Case Study on Simulation Data with Extreme Climate Events Analysis-Reference-Cited by-同舟云学术

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Spatiotemporally Adaptive Compression for Scientific Dataset with Feature Preservation – A Case Study on Simulation Data with Extreme Climate Events Analysis

Published:2023-10-09 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 19th International Conference on e-Science (e-Science)
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Author:

Gong Qian¹,Zhang Chengzhu²,Liang Xin³,Reshniak Viktor¹,Chen Jieyang⁴,Rangarajan Anand⁵,Ranka Sanjay⁵,Vidal Nicolas¹,Wan Lipeng⁶,Ullrich Paul²,Podhorszki Norbert¹,Jacob Robert⁷,Klasky Scott¹

Affiliation:

1. Oak Ridge National Laboratory,Oak Ridge,TN

2. Lawrence Livermore National Laboratory,Livermore,CA

3. University of Kentucky,Lexington,KY

4. University of Alabama,Birmingham,AL

5. University of Florida,Gainesville,FL

6. Georgia State University,Atlanta,GA

7. Argonne National Laboratory,Lemont,IL

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10254737/10254662/10254796.pdf?arnumber=10254796

Reference41 articles.

1. Optimizing Error-Bounded Lossy Compression for Scientific Data by Dynamic Spline Interpolation

2. Error Analysis of ZFP Compression for Floating-Point Data

3. Error-Controlled Lossy Compression Optimized for High Compression Ratios of Scientific Datasets

4. Use cases of lossy compression for floating-point data in scientific data sets

5. Fixed-Rate Compressed Floating-Point Arrays

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1. Multifacets of lossy compression for scientific data in the Joint-Laboratory of Extreme Scale Computing;Future Generation Computer Systems;2025-02

2. MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring;SoftwareX;2023-12

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