35 citations · 49 across the 15 of their papers we have counts for
16 papers
BlockMGARD: Accelerating Adaptive Scientific Data Reduction with Region-of-Interest Error Control on GPUs
Yanliang Li, Qian Gong, Qing Liu +5
The growing scale of scientific data makes lossy compression essential for reducing data volume under controllable error. Transformation-based compressors using multilevel decompos…
QoI-Aware Provisional Rollout and Retrospective Reconciliation for Reduced-State Scientific Twins
Liangji Zhu, Scott Klasky, Jaemoon Lee +3
Scientific twins may need to continue operating when updates from an authoritative primary system are temporarily unavailable. Once synchronization resumes, the new boundary can al…
NIMROD-to-IMAS workflow for extended-magnetohydrodynamic data with reusable datasets and implications for IMAS schema development
Alexei Y. Pankin, Fatima Ebrahimi, Qian Gong +5
Extended magnetohydrodynamic (MHD) simulations of tokamak plasmas regularly produce outputs in multi-dimensional, multiple-field formats; these code-specific formats make it diffic…
HP-MDR: High-performance and Portable Data Refactoring and Progressive Retrieval with Advanced GPUs
Yanliang Li, Wenbo Li, Qian Gong +5
Scientific applications produce vast amounts of data, posing grand challenges in the underlying data management and analytic tasks. Progressive compression is a promising way to ad…
HPDR: High-Performance Portable Scientific Data Reduction Framework
Jieyang Chen, Qian Gong, Yanliang Li +5
The rapid growth of scientific data is surpassing advancements in computing, creating challenges in storage, transfer, and analysis, particularly at the exascale. While data reduct…
A General Framework for Error-controlled Unstructured Scientific Data Compression
Qian Gong, Zhe Wang, Viktor Reshniak +10
Data compression plays a key role in reducing storage and I/O costs. Traditional lossy methods primarily target data on rectilinear grids and cannot leverage the spatial coherence…
Error-controlled Progressive Retrieval of Scientific Data under Derivable Quantities of Interest
Xuan Wu, Qian Gong, Jieyang Chen +4
The unprecedented amount of scientific data has introduced heavy pressure on the current data storage and transmission systems. Progressive compression has been proposed to mitigat…
A framework for compressing unstructured scientific data via serialization
Viktor Reshniak, Qian Gong, Rick Archibald +2
We present a general framework for compressing unstructured scientific data with known local connectivity. A common application is simulation data defined on arbitrary finite eleme…
Lifting MGARD: construction of (pre)wavelets on the interval using polynomial predictors of arbitrary order
Viktor Reshniak, Evan Ferguson, Qian Gong +3
MGARD (MultiGrid Adaptive Reduction of Data) is an algorithm for compressing and refactoring scientific data, based on the theory of multigrid methods. The core algorithm is built…
Uncertainty Visualization of Critical Points of 2D Scalar Fields for Parametric and Nonparametric Probabilistic Models
Tushar M. Athawale, Zhe Wang, David Pugmire +5
This paper presents a novel end-to-end framework for closed-form computation and visualization of critical point uncertainty in 2D uncertain scalar fields. Critical points are fund…
Machine Learning Techniques for Data Reduction of Climate Applications
Xiao Li, Qian Gong, Jaemoon Lee +3
Scientists conduct large-scale simulations to compute derived quantities-of-interest (QoI) from primary data. Often, QoI are linked to specific features, regions, or time intervals…
Machine Learning Techniques for Data Reduction of CFD Applications
Jaemoon Lee, Ki Sung Jung, Qian Gong +5
We present an approach called guaranteed block autoencoder that leverages Tensor Correlations (GBATC) for reducing the spatiotemporal data generated by computational fluid dynamics…
MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring
Qian Gong, Jieyang Chen, Ben Whitney +13
We describe MGARD, a software providing MultiGrid Adaptive Reduction for floating-point scientific data on structured and unstructured grids. With exceptional data compression capa…
Spatiotemporally adaptive compression for scientific dataset with feature preservation -- a case study on simulation data with extreme climate events analysis
Qian Gong, Chengzhu Zhang, Xin Liang +10
Scientific discoveries are increasingly constrained by limited storage space and I/O capacities. For time-series simulations and experiments, their data often need to be decimated…
Scalable Hybrid Learning Techniques for Scientific Data Compression
Tania Banerjee, Jong Choi, Jaemoon Lee +5
Data compression is becoming critical for storing scientific data because many scientific applications need to store large amounts of data and post process this data for scientific…
Scalable Multigrid-based Hierarchical Scientific Data Refactoring on GPUs
Jieyang Chen, Lipeng Wan, Xin Liang +10
Rapid growth in scientific data and a widening gap between computational speed and I/O bandwidth makes it increasingly infeasible to store and share all data produced by scientific…