activity
20232026
most citedA Survey on Error-Bounded Lossy Compression for Scientific Datasets

2 citations · 2 across the 14 of their papers we have counts for

collaborators
Showing 2026 · cs.DCShow all

5 papers · 2 filters

cs.DC2026

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

Mingze Xia, Yuxiao Li, Sheng Di +6

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-specifie…

cs.DC2026

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

Yuxiao Li, Mingze Xia, Xin Liang +2

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…

cs.DC2026

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

Tripti Agarwal, Sheng Di, Xin Liang +5

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 pr…

cs.DC2026

pMSz: A Distributed Parallel Algorithm for Correcting Extrema and Morse Smale Segmentations in Lossy Compression

Yuxiao Li, Mingze Xia, Xin Liang +7

Lossy compression, widely used by scientists to reduce data from simulations, experiments, and observations, can distort features of interest even under bounded error. Such distort…

cs.DC2026

FFCz: Fast Fourier Correction for Spectrum-Preserving Lossy Compression of Scientific Data

Congrong Ren, Robert Underwood, Sheng Di +5

This paper introduces a novel technique to preserve spectral features in lossy compression based on a novel fast Fourier correction algorithm\added{ for regular-grid data}. Preserv…