  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.
