Today's scientific simulations require significant data volume reduction because of the enormous amounts of data produced and the limited I/O bandwidth and storage space. Error-bounded lossy compression has been considered one of the most effective solutions to the above problem. However, little work has been done to improve error-bounded lossy compression for Adaptive Mesh Refinement (AMR) simulation data. Unlike the previous work that only leverages 1D compression, in this work, we propose an approach (TAC) to leverage high-dimensional SZ compression for each refinement level of AMR data. To remove the data redundancy across different levels, we propose several pre-process strategies and adaptively use them based on the data features. We further optimize TAC to TAC+ by improving the lossless encoding stage of SZ compression to handle many small AMR data blocks after the pre-processing efficiently. Experiments on 10 AMR datasets from three real-world large-scale AMR simulations demonstrate that TAC+ can improve the compression ratio by up to 4.9$\times$ under the same data distortion, compared to the state-of-the-art method. In addition, we leverage the flexibility of our approach to tune the error bound for each level, which achieves much lower data distortion on two application-specific metrics.

翻译：当今科学模拟因产生海量数据且I/O带宽和存储空间有限，亟需显著降低数据体量。误差有界有损压缩被认为是解决上述问题最有效的方法之一。然而，针对自适应网格细化（AMR）模拟数据的误差有界有损压缩优化工作尚属空白。与仅利用一维压缩的现有研究不同，本文提出一种方法（TAC），对AMR数据的每个细化层级采用高维SZ压缩。为消除不同层级间的数据冗余，我们设计多种预处理策略，并根据数据特征自适应选择。进一步将TAC优化为TAC+，通过改进SZ压缩的无损编码阶段，高效处理预处理后大量的小型AMR数据块。在来自三个真实大规模AMR模拟的10个AMR数据集上的实验表明：与现有最优方法相比，TAC+在相同数据失真下最多可将压缩比提升4.9倍。此外，我们利用本方法的灵活性为每个层级调整误差界，在两个应用特定指标上实现了更低的数理失真。