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.

翻译：误差有界有损压缩对于管理大规模高性能计算模拟产生的海量数据至关重要。虽然SZ和ZFP等先进压缩器提供了严格的数值误差保证，但它们通常无法保留对科学分析至关重要的拓扑结构（例如极小值点、极大值点和鞍点）。现有的拓扑感知压缩器解决了这一局限，但带来了巨大的计算开销。我们提出了TopoSZp，一种轻量级、拓扑感知、误差可控的有损压缩器，它能在保持高压缩和解压性能的同时，保留临界点及其关系。TopoSZp基于高吞吐量的SZp压缩器构建，集成了高效的临界点检测、局部顺序保持和针对性鞍点优化，所有这些都在一个宽松但严格执行的误差界内完成。在真实世界科学数据集上的实验结果表明，与现有的拓扑感知压缩器相比，TopoSZp实现了3到100倍更少的未保留临界点，不引入误报或错误的临界点类型，并提供100到10000倍更快的压缩速度和10到500倍更快的解压速度，同时保持有竞争力的压缩比。