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Today's scientific simulations require a significant reduction of data volumebecause of extremely large amounts of data they produce and the limited I/Obandwidth and storage space. Error-bounded lossy compression has beenconsidered one of the most effective solutions to the above problem. However,little work has been done to improve error-bounded lossy compression forAdaptive Mesh Refinement (AMR) simulation data. Unlike the previous work thatonly leverages 1D compression, in this work, we propose to leveragehigh-dimensional (e.g., 3D) compression for each refinement level of AMR data.To remove the data redundancy across different levels, we propose threepre-process strategies and adaptively use them based on the datacharacteristics. Experiments on seven AMR datasets from a real-worldlarge-scale AMR simulation demonstrate that our proposed approach can improvethe compression ratio by up to 3.3X under the same data distortion, compared tothe state-of-the-art method. In addition, we leverage the flexibility of ourapproach to tune the error bound for each level, which achieves much lower datadistortion on two application-specific metrics.

TAC: Optimizing Error-Bounded Lossy Compression for Three-Dimensional Adaptive Mesh Refinement Simulations