19 citations · 33 across the 3 of their papers we have counts for
3 papers
cs.DC2021★ 19 cited
Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads
John Thorpe, Yifan Qiao, Jonathan Eyolfson +8
A graph neural network (GNN) enables deep learning on structured graph data. There are two major GNN training obstacles: 1) it relies on high-end servers with many GPUs which are e…
cs.LG2020★ 5 cited
IOS: Inter-Operator Scheduler for CNN Acceleration
Yaoyao Ding, Ligeng Zhu, Zhihao Jia +2
To accelerate CNN inference, existing deep learning frameworks focus on optimizing intra-operator parallelization. However, a single operator can no longer fully utilize the availa…
cs.LG2019★ 9 cited
Redundancy-Free Computation Graphs for Graph Neural Networks
Zhihao Jia, Sina Lin, Rex Ying +3
Graph Neural Networks (GNNs) are based on repeated aggregations of information across nodes' neighbors in a graph. However, because common neighbors are shared between different no…