2024/12/03 by Jinyang Liu, Pu Jiao, Liu, Jinyang +9 · 2 citations
Computer Science · Engineering · #Advanced Data Storage Technologies #Computational Engineering #Databases (cs.DB) #Distributed #FOS: Computer and information sciences #Finance #Parallel #Parallel Computing and Optimization Techniques #Sparse and Compressive Sensing Techniques #and Cluster Computing (cs.DC) #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2412.02799
openalex publication_date 2024/12/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
Error-bounded lossy compression has been widely adopted in many scientific domains because it can address the challenges in storing, transferring, and analyzing unprecedented amounts of scientific data. Although error-bounded lossy compression offers general data distortion control by enforcing strict error bounds on raw data, it may fail to meet the quality requirements on the results of downstream analysis, a.k.a. Quantities of Interest (QoIs), derived from raw data. This may lead to uncertainties and even misinterpretations in scientific discoveries, significantly limiting the use of lossy compression in practice. In this paper, we propose QPET, a novel, versatile, and portable framework for QoI-preserving error-bounded lossy compression, which overcomes the challenges of modeling diverse QoIs by leveraging numerical strategies. QPET features (1) high portability to multiple existing lossy compressors, (2) versatile preservation to most differentiable univariate and multivariate QoIs, and (3) significant compression improvements in QoI-preservation tasks. Experiments with six real-world datasets demonstrate that integrating QPET into state-of-the-art error-bounded lossy compressors can gain 2x to 10x compression speedups of existing QoI-preserving error-bounded lossy compression solutions, up to 1000% compression ratio improvements to general-purpose compressors, and up to 133% compression ratio improvements to existing QoI-integrated scientific compressors.