TopoSZp: Lightweight Topology-Aware Error-controlled Compression for Scientific Data
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| author | Agarwal, Tripti Di, Sheng Liang, Xin Su, Zhaoyuan Li, Yuxiao Gopalakrishnan, Ganesh Guo, Hanqi Cappello, Franck |
| author_facet | Agarwal, Tripti Di, Sheng Liang, Xin Su, Zhaoyuan Li, Yuxiao Gopalakrishnan, Ganesh Guo, Hanqi Cappello, Franck |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_17552 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TopoSZp: Lightweight Topology-Aware Error-controlled Compression for Scientific Data Agarwal, Tripti Di, Sheng Liang, Xin Su, Zhaoyuan Li, Yuxiao Gopalakrishnan, Ganesh Guo, Hanqi Cappello, Franck Distributed, Parallel, and Cluster Computing 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. |
| title | TopoSZp: Lightweight Topology-Aware Error-controlled Compression for Scientific Data |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2602.17552 |