Abstract
Efficient error-controlled lossy compressors are becoming critical to the success of today's large-scale scientific applications because of the ever-increasing volume of data produced by the applications. In the past decade, many lossless and lossy compressors have been developed with distinct design principles for different scientific datasets in largely diverse scientific domains. In order to support researchers and users assessing and comparing compressors in a fair and convenient way, we establish a standard compression assessment benchmark - Scientific Data Reduction Benchmark (SDRBench)1. SDRBench contains a vast variety of real-world scientific datasets across different domains, summarizes several critical compression quality evaluation metrics, and integrates many state-of-the-art lossy and lossless compressors. We demonstrate evaluation results using SDRBench and summarize six valuable takeaways that are helpful to the in-depth understanding of lossy compressors.
Original language | English |
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Title of host publication | Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020 |
Editors | Xintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz |
Pages | 2716-2724 |
Number of pages | 9 |
ISBN (Electronic) | 9781728162515 |
DOIs | |
State | Published - Dec 10 2020 |
Event | 8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States Duration: Dec 10 2020 → Dec 13 2020 |
Publication series
Name | Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020 |
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Conference
Conference | 8th IEEE International Conference on Big Data, Big Data 2020 |
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Country/Territory | United States |
City | Virtual, Atlanta |
Period | 12/10/20 → 12/13/20 |
Bibliographical note
Publisher Copyright:© 2020 IEEE.
ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
- Information Systems and Management
- Safety, Risk, Reliability and Quality