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

2026-08-11

AI Summary

Existing error-bounded compression methods struggle to preserve the topological structure of critical points when compressing scientific vector field data, thereby compromising downstream feature analysis. This work proposes the first GPU-accelerated, error-bounded compression method that strictly preserves all critical points. By reformulating the traditionally serially coupled topological constraints into parallelizable tasks, the approach introduces two optimization strategies: block-level and speculative point-wise processing. Experimental results demonstrate that the method fully retains all critical points across three vector field datasets while achieving a throughput of up to 60 GB/s—approximately 640× faster than the CPU-based cpSZ implementation—and nearly doubles the compression ratio under the speculative mode.

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.

Problem

• error-bounded compression
• vector fields
• critical-point preservation
• topology-aware compression
• GPU compression

Innovation

• GPU compression
• critical-point preservation
• topology-aware
• error-bounded lossy compression
• vector field