TopoSZp: Lightweight Topology-Aware Error-controlled Compression for Scientific Data

2026-02-19

AI Summary

This work addresses the challenge that existing error-bounded compression methods struggle to simultaneously preserve numerical accuracy and critical topological features—such as extrema and saddle points—while topology-aware compressors often incur prohibitive computational costs. Building upon the high-performance compressor SZp, we propose a lightweight topology-preserving compression approach that integrates efficient critical point detection, local ordering constraints, and targeted saddle-point optimization. This method preserves both the type and topological relationships of critical points under strict error bounds, without introducing spurious structures. Seamlessly integrated into the SZp framework, it supports highly efficient parallel encoding and decoding. Experiments on real scientific datasets demonstrate that, compared to state-of-the-art topology-aware compressors, our method reduces non-preserved critical points by 3–100×, accelerates compression by 100–10,000×, and improves decompression speed by 10–500×, all while maintaining competitive compression ratios.

Technology Category

• Data Mining & Knowledge Management: Data Compression
• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Combinatorial Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

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.

Problem

• topology-aware compression
• error-bounded lossy compression
• critical points preservation
• scientific data compression
• HPC simulations

Innovation

• topology-aware compression
• error-controlled lossy compression
• critical point preservation
• lightweight compressor
• scientific data compression