16 citations · 26 across the 10 of their papers we have counts for
12 papers
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…
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…
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…
Time-varying Vector Field Compression with Preserved Critical Point Trajectories
Mingze Xia, Yuxiao Li, Pu Jiao +3
Scientific simulations and observations are producing vast amounts of time-varying vector field data, making it hard to store them for archival purposes and transmit them for analy…
TFZ: Topology-Preserving Compression of 2D Symmetric and Asymmetric Second-Order Tensor Fields
Nathaniel Gorski, Xin Liang, Hanqi Guo +1
In this paper, we present a novel compression framework, TFZ, that preserves the topology of 2D symmetric and asymmetric second-order tensor fields defined on flat triangular meshe…
A General Framework for Augmenting Lossy Compressors with Topological Guarantees
Nathaniel Gorski, Xin Liang, Hanqi Guo +2
Topological descriptors such as contour trees are widely utilized in scientific data analysis and visualization, with applications from materials science to climate simulations. It…
Preserving Discrete Morse-Smale Complexes in Error-Bounded Lossy Compression
Yuxiao Li, Mingze Xia, Xin Liang +2
Scientific applications are generating unprecedented volumes of data that overwhelm storage and transmission systems, posing significant challenges for the design of data managemen…
MSz: An Efficient Parallel Algorithm for Correcting Morse-Smale Segmentations in Error-Bounded Lossy Compressors
Yuxiao Li, Xin Liang, Bei Wang +3
This research explores a novel paradigm for preserving topological segmentations in existing error-bounded lossy compressors. Today's lossy compressors rarely consider preserving t…
A Prediction-Traversal Approach for Compressing Scientific Data on Unstructured Meshes with Bounded Error
Congrong Ren, Xin Liang, Hanqi Guo
We explore an error-bounded lossy compression approach for reducing scientific data associated with 2D/3D unstructured meshes. While existing lossy compressors offer a high compres…
TopoSZ: Preserving Topology in Error-Bounded Lossy Compression
Lin Yan, Xin Liang, Hanqi Guo +1
Existing error-bounded lossy compression techniques control the pointwise error during compression to guarantee the integrity of the decompressed data. However, they typically do n…
Semi-Supervised Clustering via Dynamic Graph Structure Learning
Huaming Ling, Chenglong Bao, Xin Liang +1
Most existing semi-supervised graph-based clustering methods exploit the supervisory information by either refining the affinity matrix or directly constraining the low-dimensional…
FTK: A Simplicial Spacetime Meshing Framework for Robust and Scalable Feature Tracking
Hanqi Guo, David Lenz, Jiayi Xu +7
We present the Feature Tracking Kit (FTK), a framework that simplifies, scales, and delivers various feature-tracking algorithms for scientific data. The key of FTK is our high-dim…