English

SDRBench: Scientific Data Reduction Benchmark for Lossy Compressors

Distributed, Parallel, and Cluster Computing 2021-11-04 v1

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). 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.

Keywords

Cite

@article{arxiv.2101.03201,
  title  = {SDRBench: Scientific Data Reduction Benchmark for Lossy Compressors},
  author = {Kai Zhao and Sheng Di and Xin Liang and Sihuan Li and Dingwen Tao and Julie Bessac and Zizhong Chen and Franck Cappello},
  journal= {arXiv preprint arXiv:2101.03201},
  year   = {2021}
}

Comments

Published in Proceedings of the 1st International Workshop on Big Data Reduction @BigData'20

R2 v1 2026-06-23T21:55:56.348Z