28 citations · 39 across the 6 of their papers we have counts for
12 papers
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…
MGARD+: Optimizing Multilevel Methods for Error-bounded Scientific Data Reduction
Xin Liang, Ben Whitney, Jieyang Chen +8
Data management is becoming increasingly important in dealing with the large amounts of data produced by large-scale scientific simulations and instruments. Existing multilevel com…
Revisiting Huffman Coding: Toward Extreme Performance on Modern GPU Architectures
Jiannan Tian, Cody Rivera, Sheng Di +4
Today's high-performance computing (HPC) applications are producing vast volumes of data, which are challenging to store and transfer efficiently during the execution, such that da…