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ABSTRACT

As supercomputers advance towards exascale, 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) can effectively address these challenges. Concurrently, error-bounded lossy compression is one of the most efficient approaches to tackle the data volume issue. Despite their respective advantages, few studies have examined how the multi-resolution method and error-bounded lossy compression can work 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 adaptively preprocessing the data based on data features and optimizing the compressor. (2) This study then presents a novel in-situ lossy compression framework, utilizing HDF5 and enhanced SZ2, specifically tailored for real-world AMR applications. This framework can improve I/O costs and compression quality. (3) Finally, this study introduces a workflow for multi-resolution data compression applicable to both uniform and AMR simulations. It optimizes three compressors to better integrate multi-resolution techniques with lossy compression and incorporates an advanced uncertainty visualization method to help users understand the potential impacts of compression.

Daoce Wang

Indiana ¾Å¾Å¸£ÀûÍø

Daoce Wang is a fifth-year Ph.D. student at Indiana ¾Å¾Å¸£ÀûÍø, Bloomington. He earned his bachelor's degree in Computer Science from the ¾Å¾Å¸£ÀûÍø of Electronic Science and Technology of China in 2018 and his master’s degree in Computer Science from the ¾Å¾Å¸£ÀûÍø of Florida in 2020. He served as a summer research intern at Los Alamos National Laboratory in 2021, 2022, 2023, and 2024. His research interests include high-performance computing (HPC), scientific data management and visualization, lossy compression, machine learning (ML), and fault tolerance. Daoce’s primary Ph.D. research focuses on designing efficient data reduction methods for extreme-scale scientific simulations on HPC systems. He has published five first-authored papers in CLUSTER ’21, HPDC ’22, TPDS ’24, SC ’23, and SC ’24.