@inproceedings{6189d5d6eae94a0c88fe5451bb246bed,
title = "SDRBench: Scientific Data Reduction Benchmark for Lossy Compressors",
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.",
author = "Kai Zhao and Sheng Di and Xin Lian and Sihuan Li and Dingwen Tao and Julie Bessac and Zizhong Chen and Franck Cappello",
note = "Funding Information: including software, applications, hardware, advanced system engineering and early testbed platforms, to support the nation{\textquoteright}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 acknowledge the computing resources provided on Bebop, which is operated by the Laboratory Computing Resource Center at Argonne National Laboratory. Funding Information: 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, Publisher Copyright: {\textcopyright} 2020 IEEE.; 8th IEEE International Conference on Big Data, Big Data 2020 ; Conference date: 10-12-2020 Through 13-12-2020",
year = "2020",
month = dec,
day = "10",
doi = "10.1109/BigData50022.2020.9378449",
language = "English (US)",
series = "Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2716--2724",
editor = "Xintao Wu and Chris Jermaine and Li Xiong and Hu, {Xiaohua Tony} and Olivera Kotevska and Siyuan Lu and Weijia Xu and Srinivas Aluru and Chengxiang Zhai and Eyhab Al-Masri and Zhiyuan Chen and Jeff Saltz",
booktitle = "Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020",
address = "United States",
}