TopoSZp: 轻量级拓扑感知的误差控制压缩用于科学数据
TopoSZp: Lightweight Topology-Aware Error-controlled Compression for Scientific Data
AI总结：
TopoSZp是一种轻量级拓扑感知压缩器，通过高效算法在保持高压缩性能的同时，精准保留科学数据中的关键拓扑结构。
摘要：
误差受控的损失性压缩对于管理由大规模HPC模拟产生的海量数据至关重要。尽管最先进的压缩器如SZ和ZFP提供了强大的数值误差保证，但它们往往无法保留拓扑结构（例如极小值、极大值和鞍点）这些对科学分析至关重要的特性。现有拓扑感知压缩器解决了这一限制，但带来了显著的计算开销。我们提出了TopoSZp，一种轻量级、拓扑感知、误差受控的损失性压缩器，能够在保持高压缩和解压性能的同时保留关键点及其关系。基于高吞吐量的SZp压缩器，TopoSZp集成了高效的临界点检测、局部排序保留和定向鞍点细化，所有这些都在宽松但严格执行的误差范围内。在真实世界科学数据集上的实验结果表明，TopoSZp在非保留的关键点上减少了3到100倍，不引入假阳性或错误的关键点类型，并在保持竞争性压缩比的同时，比现有拓扑感知压缩器快100到10000倍的压缩速度和10到500倍的解压速度。
英文摘要：
Error-bounded lossy compression is essential for managing the massive data volumes produced by large-scale HPC simulations. While state-of-the-art compressors such as SZ and ZFP provide strong numerical error guarantees, they often fail to preserve topological structures (example, minima, maxima, and saddle points) that are critical for scientific analysis. Existing topology-aware compressors address this limitation but incur substantial computational overhead. We present TopoSZp, a lightweight, topology-aware, error-controlled lossy compressor that preserves critical points and their relationships while maintaining high compression and decompression performance. Built on the high-throughput SZp compressor, TopoSZp integrates efficient critical point detection, local ordering preservation, and targeted saddle point refinement, all within a relaxed but strictly enforced error bound. Experimental results on real-world scientific datasets show that TopoSZp achieves 3 to 100 times fewer non-preserved critical points, introduces no false positives or incorrect critical point types, and delivers 100 to 10000 times faster compression and 10 to 500 times faster decompression compared to existing topology-aware compressors, while maintaining competitive compression ratios.
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