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With the ever-increasing execution scale of high performance computing (HPC)applications, vast amounts of data are being produced by scientific researchevery day. Error-bounded lossy compression has been considered a very promisingsolution to address the big-data issue for scientific applications because itcan significantly reduce the data volume with low time cost meanwhile allowingusers to control the compression errors with a specified error bound. Theexisting error-bounded lossy compressors, however, are all developed based oninflexible designs or compression pipelines, which cannot adapt to diversecompression quality requirements/metrics favored by different applicationusers. In this paper, we propose a novel dynamic quality metric orientederror-bounded lossy compression framework, namely QoZ. The detailedcontribution is three-fold. (1) We design a novel highly-parameterizedmulti-level interpolation-based data predictor, which can significantly improvethe overall compression quality with the same compressed size. (2) We designthe error-bounded lossy compression framework QoZ based on the adaptivepredictor, which can auto-tune the critical parameters and optimize thecompression result according to user-specified quality metrics during onlinecompression. (3) We evaluate QoZ carefully by comparing its compression qualitywith multiple state-of-the-arts on various real-world scientific applicationdatasets. Experiments show that, compared with the second-best lossycompressor, QoZ can achieve up to 70% compression ratio improvement under thesame error bound, up to 150% compression ratio improvement under the same PSNR,or up to 270% compression ratio improvement under the same SSIM.

Dynamic Quality Metric Oriented Error-bounded Lossy Compression for Scientific Datasets