EXaCTz: Guaranteed Extremum Graph and Contour Tree Preservation for Distributed and GPU-Parallel Lossy Compression

Authors

Yuxiao Li (The Ohio State University), Mingze Xia (Oregon State University), Xin Liang (Oregon State University), Bei Wang (University of Utah), Hanqi Guo (The Ohio State University)

Presentation

Session

That's way too big!

Time

Thursday, Nov 12, 15:48 – 16:00 (US/Eastern) · session 15:00 – 16:30

Location

Hall America center

Keywords

High-performance computing, parallel lossy compression, topology data analysis, contour trees, extremum graphs

Abstract

This paper introduces EXaCTz, a parallel algorithm that corrects lossy-compressed scalar field data to preserve extremum graphs and contour trees concurrently. While error-bounded lossy compression is essential for large-scale scientific simulations and workflows, existing topology-preserving methods suffer from (1) a significant throughput disparity, where topology correction speeds are on the order of MB/s, lagging orders of magnitude behind compression speeds on the order of GB/s; (2) limited support for diverse topological descriptors; and (3) a lack of theoretical convergence bounds. To address these challenges, EXaCTz introduces a high-performance, bounded-iteration algorithm that enforces topological consistency by deriving targeted edits for decompressed data. Unlike prior methods that rely on explicit topology reconstruction, EXaCTz enforces consistent min/max neighbors of all vertices, along with global ordering among critical points. As such, the algorithm enforces consistent critical-point classification, saddle-extremum connectivity, and the preservation of merge/split events. We theoretically prove the convergence of our algorithm, bounded by the longest path in a vulnerability graph that characterizes potential cascading effects during correction. Experiments on real-world datasets show that EXaCTz achieves a single-GPU throughput of up to 4.52 GB/s, outperforming the state-of-the-art contour-tree-preserving method by up to 213x (with a single-core CPU implementation for fair comparison) and 3,285x (with a single-GPU version). In distributed environments, EXaCTz scales to 128 GPUs with 55.6% efficiency (compared with 6.4% for a naive parallelization), processing datasets of up to 512 GB in under 48 seconds and achieving an aggregate correction throughput of up to 32.69 GB/s.

For Practitioners

This paper is primarily of interest to computational scientists working with large-scale simulations in cosmology, combustion, climate, and quantum chemistry, as well as HPC practitioners and data-management engineers who deploy lossy compression in in-situ or post-hoc analysis pipelines. Topological data analysis researchers and developers of compression libraries (e.g., SZ, ZFP, MGARD) may also find the edit-based correction framework directly applicable to their tools.