35 citations · 45 across the 8 of their papers we have counts for
10 papers
Improving Progressive Compression with Adaptive Interpolation and Coefficient Decomposition
Wenbo Li, Xuan Wu, Qian Gong +6
Exascale simulations generate data far faster than it can be stored or analyzed, making efficient data reduction essential. Error-controlled lossy compression offers high compressi…
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…
Write-Read Decoupling in Modern Large-Scale Search Engines: Architectures, Techniques, and Emerging Approaches
Xin Liang, Qing Yang, Wenru Qiu +4
Large-scale search engines face a fundamental tension: the index must be updated frequently to maintain freshness, yet updates create resource contention that inflates query latenc…
Enabling Homomorphic Analytical Operations on Compressed Scientific Data with Multi-stage Decompression
Xuan Wu, Sheng Di, Tripti Agarwal +3
Error-controlled lossy compressors have been widely used in scientific applications to reduce the unprecedented size of scientific data while keeping data distortion within a user-…
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…
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…