23 citations · 27 across the 3 of their papers we have counts for
3 papers
cs.DC2023★ 23 cited
HongTu: Scalable Full-Graph GNN Training on Multiple GPUs (via communication-optimized CPU data offloading)
Qiange Wang, Yao Chen, Weng-Fai Wong +1
Full-graph training on graph neural networks (GNN) has emerged as a promising training method for its effectiveness. Full-graph training requires extensive memory and computation r…
cs.AR2022★ 1 cited
ReGraph: Scaling Graph Processing on HBM-enabled FPGAs with Heterogeneous Pipelines
Xinyu Chen, Yao Chen, Feng Cheng +3
The use of FPGAs for efficient graph processing has attracted significant interest. Recent memory subsystem upgrades including the introduction of HBM in FPGAs promise to further a…
cs.DC2021★ 3 cited
ThundeRiNG: Generating Multiple Independent Random Number Sequences on FPGAs
Hongshi Tan, Xinyu Chen, Yao Chen +2
In this paper, we propose ThundeRiNG, a resource-efficient and high-throughput system for generating multiple independent sequences of random numbers (MISRN) on FPGAs. Generating M…