17 citations · 57 across the 31 of their papers we have counts for
8 papers · 1 filter
A Comprehensive Survey on Distributed Training of Graph Neural Networks
Haiyang Lin, Mingyu Yan, Xiaochun Ye +4
Graph neural networks (GNNs) have been demonstrated to be a powerful algorithmic model in broad application fields for their effectiveness in learning over graphs. To scale GNN tra…
Rethinking Efficiency and Redundancy in Training Large-scale Graphs
Xin Liu, Xunbin Xiong, Mingyu Yan +4
Large-scale graphs are ubiquitous in real-world scenarios and can be trained by Graph Neural Networks (GNNs) to generate representation for downstream tasks. Given the abundant inf…
Characterizing and Understanding HGNNs on GPUs
Mingyu Yan, Mo Zou, Xiaocheng Yang +4
Heterogeneous graph neural networks (HGNNs) deliver powerful capacity in heterogeneous graph representation learning. The execution of HGNNs is usually accelerated by GPUs. Therefo…
Multi-node Acceleration for Large-scale GCNs
Gongjian Sun, Mingyu Yan, Duo Wang +5
Limited by the memory capacity and compute power, singe-node graph convolutional neural network (GCN) accelerators cannot complete the execution of GCNs within a reasonable amount…
Simple and Efficient Heterogeneous Graph Neural Network
Xiaocheng Yang, Mingyu Yan, Shirui Pan +2
Heterogeneous graph neural networks (HGNNs) have powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations. Existing…
Characterizing and Understanding Distributed GNN Training on GPUs
Haiyang Lin, Mingyu Yan, Xiaocheng Yang +4
Graph neural network (GNN) has been demonstrated to be a powerful model in many domains for its effectiveness in learning over graphs. To scale GNN training for large graphs, a wid…
Alleviating Datapath Conflicts and Design Centralization in Graph Analytics Acceleration
Haiyang Lin, Mingyu Yan, Duo Wang +5
Previous graph analytics accelerators have achieved great improvement on throughput by alleviating irregular off-chip memory accesses. However, on-chip side datapath conflicts and…
Survey on Graph Neural Network Acceleration: An Algorithmic Perspective
Xin Liu, Mingyu Yan, Lei Deng +5
Graph neural networks (GNNs) have been a hot spot of recent research and are widely utilized in diverse applications. However, with the use of huger data and deeper models, an urge…