通过重排节点提升科学数据压缩效率，兼容多种压缩算法。
A framework for compressing unstructured scientific data via serialization
- 基于网格连接性贪婪重排节点，保持局部结构
- 在真实大规模数据上实现显著压缩率提升
- 可离线或实时运行，无缝集成现有处理流程
我们提出一种通用框架，用于压缩具有已知局部连接性的非结构化科学数据。典型应用是定义在任意有限元网格上的模拟数据。该框架采用贪婪的拓扑保持节点重排，可在不改变现有数据处理流程的前提下高效集成。重排过程仅依赖网格连接性，可离线执行以获得最优效率，同时其贪婪特性也支持在线实时实现。所提方法兼容任何利用数据空间相关性的压缩算法。在大规模真实数据集上，通过MGARD、SZ和ZFP等多种压缩方法验证了该方法的有效性。
原文摘要 · Abstract (English)
We present a general framework for compressing unstructured scientific data with known local connectivity. A common application is simulation data defined on arbitrary finite element meshes. The framework employs a greedy topology preserving reordering of original nodes which allows for seamless integration into existing data processing pipelines. This reordering process depends solely on mesh connectivity and can be performed offline for optimal efficiency. However, the algorithm's greedy nature also supports on-the-fly implementation. The proposed method is compatible with any compression algorithm that leverages spatial correlations within the data. The effectiveness of this approach is demonstrated on a large-scale real dataset using several compression methods, including MGARD, SZ, and ZFP.
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