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As supercomputers advance towards exascale capabilities, computational intensity increases significantly, and the volume of data requiring storage and transmission experiences exponential growth. Multi-resolution methods, such as Adaptive Mesh Refinement (AMR), have emerged as an effective solution to address these challenges. Concurrently, error-bounded lossy compression is recognized as one of the most efficient approaches to tackle the latter issue. Despite their respective advantages, few attempts have been made to investigate how the multi-resolution method and error-bounded lossy compression can function together.
To bridge this gap, this study introduces a series of application-driven system solutions, coupled with algorithmic innovations, for real-world multi-resolution data reduction: (1) This study first enhances the offline compression quality of multi-resolution data for different state-of-the-art scientific compressors by using different novel algorithms to adaptively preprocess the data based on AMR data features and optimize the SZ2 compressor. (2) This study then presents a novel in-situ lossy compression framework, this framework effectively utilizes HDF5 and optimizes the SZ2 compressor, specifically tailored for real-world AMR applications. This framework can greatly reduce I/O costs and improve compression quality compared to the state-of-the-art solution. (3) Lastly, to extend the usability of multi-resolution techniques, this study introduces a workflow for multi-resolution data compression, applicable to both uniform and AMR simulations. Initially, the workflow employs a Region of Interest (ROI) extraction approach to enable multi-resolution methods for uniform data. Subsequently, to bridge the gap between multi-resolution techniques and lossy compressors, we optimize three distinct compressors, ensuring their optimal performance on multi-resolution data. Lastly, we incorporate an advanced uncertainty visualization method into our workflow to help users understand the potential impacts of lossy compression.