35 citations · 84 across the 13 of their papers we have counts for
13 papers · 1 filter
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…
MSREP: A Fast yet Light Sparse Matrix Framework for Multi-GPU Systems
Jieyang Chen, Chenhao Xie, Jesun S Firoz +5
Sparse linear algebra kernels play a critical role in numerous applications, covering from exascale scientific simulation to large-scale data analytics. Offloading linear algebra k…
Improving I/O Performance for Exascale Applications through Online Data Layout Reorganization
Lipeng Wan, Axel Huebl, Junmin Gu +12
The applications being developed within the U.S. Exascale Computing Project (ECP) to run on imminent Exascale computers will generate scientific results with unprecedented fidelity…
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…
Fast and Scalable Sparse Triangular Solver for Multi-GPU Based HPC Architectures
Chenhao Xie, Jieyang Chen, Jesun S Firoz +5
Designing efficient and scalable sparse linear algebra kernels on modern multi-GPU based HPC systems is a daunting task due to significant irregular memory references and workload…