[2608.10586] FaCTz: Fast Critical-Point and Topology-Aware GPU Compression for Scientific Vector Fields

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arXiv:2608.10586 (cs)

[Submitted on 11 Aug 2026]

Title:FaCTz: Fast Critical-Point and Topology-Aware GPU Compression for Scientific Vector Fields

Authors:Mingze Xia, Yuxiao Li, Sheng Di, Jiannan Tian, Baixi Sun, Boyi Zhang, Bei Wang, Hanqi Guo, Xin Liang<br>View a PDF of the paper titled FaCTz: Fast Critical-Point and Topology-Aware GPU Compression for Scientific Vector Fields, by Mingze Xia and 8 other authors

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Abstract:Error-bounded lossy compression is essential for storing and transferring the vector-field data produced by large-scale scientific simulations. Although it enforces a user-specified error bound to limit numerical distortion, it does not preserve the field's topology: small admissible perturbations can create or eliminate critical points on which downstream feature analysis depends. Existing GPU compressors achieve high throughput but are topology-agnostic, whereas the only compressor with provable critical-point preservation (cpSZ) runs on the CPU at throughput far below the data-generation rates of modern GPU-based systems. We observe that, although preserving critical points is inherently a coupled and sequential constraint, it can be reformulated into independent parallel tasks, either on a per-block basis or, speculatively, on a per-point basis. We present FaCTz, the first GPU-based error-bounded lossy compressor that guarantees critical-point preservation. FaCTz provides a block-wise mode optimized for throughput and a speculative per-point mode optimized for compression ratio. Across three vector-field datasets, FaCTz preserves every critical point while achieving throughput of up to 60 GB/s, approximately two orders of magnitude (up to approximately 640x) faster than the multithreaded CPU implementation of cpSZ. Its speculative mode further improves the compression ratio by approximately a factor of two over the throughput-oriented mode.

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Distributed, Parallel, and Cluster Computing (cs.DC)

Cite as:<br>arXiv:2608.10586 [cs.DC]

(or<br>arXiv:2608.10586v1 [cs.DC] for this version)

https://doi.org/10.48550/arXiv.2608.10586

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arXiv-issued DOI via DataCite

Submission history<br>From: Mingze Xia [view email]<br>[v1]<br>Tue, 11 Aug 2026 07:14:50 UTC (34,782 KB)

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