Search Results for author: Mingyu Yan

Found 11 papers, 3 papers with code

A Comprehensive Survey on Distributed Training of Graph Neural Networks

no code implementations10 Nov 2022 Haiyang Lin, Mingyu Yan, Xiaochun Ye, Dongrui Fan, Shirui Pan, WenGuang Chen, Yuan Xie

This situation poses a considerable challenge for newcomers, hindering their ability to grasp a comprehensive understanding of the workflows, computational patterns, communication strategies, and optimization techniques employed in distributed GNN training.

Cyber-physical interdependent restoration scheduling for active distribution network via ad hoc wireless communication

no code implementations5 Nov 2022 Chongyu Wang, Mingyu Yan, Kaiyuan Pang, Fushuan Wen, Fei Teng

This paper proposes a post-disaster cyber-physical interdependent restoration scheduling (CPIRS) framework for active distribution networks (ADN) where the simultaneous damages on cyber and physical networks are considered.

Scheduling

Towards Joint Electricity and Data Trading: A Scalable Cooperative Game Theoretic Approach

no code implementations8 Oct 2022 Mingyu Yan, Fei Teng

This paper, for the first time, proposes a joint electricity and data trading mechanism based on cooperative game theory.

Imputation

Rethinking Efficiency and Redundancy in Training Large-scale Graphs

no code implementations2 Sep 2022 Xin Liu, Xunbin Xiong, Mingyu Yan, Runzhen Xue, Shirui Pan, Xiaochun Ye, Dongrui Fan

Thereby, we propose to drop redundancy and improve efficiency of training large-scale graphs with GNNs, by rethinking the inherent characteristics in a graph.

Simple and Efficient Heterogeneous Graph Neural Network

2 code implementations6 Jul 2022 Xiaocheng Yang, Mingyu Yan, Shirui Pan, Xiaochun Ye, Dongrui Fan

Heterogeneous graph neural networks (HGNNs) have powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations.

Node Property Prediction

Characterizing and Understanding Distributed GNN Training on GPUs

no code implementations18 Apr 2022 Haiyang Lin, Mingyu Yan, Xiaocheng Yang, Mo Zou, WenMing Li, Xiaochun Ye, Dongrui Fan

Graph neural network (GNN) has been demonstrated to be a powerful model in many domains for its effectiveness in learning over graphs.

Survey on Graph Neural Network Acceleration: An Algorithmic Perspective

no code implementations10 Feb 2022 Xin Liu, Mingyu Yan, Lei Deng, Guoqi Li, Xiaochun Ye, Dongrui Fan, Shirui Pan, Yuan Xie

Next, we provide comparisons from aspects of the efficiency and characteristics of these methods.

GNNSampler: Bridging the Gap between Sampling Algorithms of GNN and Hardware

1 code implementation26 Aug 2021 Xin Liu, Mingyu Yan, Shuhan Song, Zhengyang Lv, WenMing Li, Guangyu Sun, Xiaochun Ye, Dongrui Fan

Extensive experiments show that our method is universal to mainstream sampling algorithms and helps significantly reduce the training time, especially in large-scale graphs.

Sampling methods for efficient training of graph convolutional networks: A survey

no code implementations10 Mar 2021 Xin Liu, Mingyu Yan, Lei Deng, Guoqi Li, Xiaochun Ye, Dongrui Fan

Graph Convolutional Networks (GCNs) have received significant attention from various research fields due to the excellent performance in learning graph representations.

Rubik: A Hierarchical Architecture for Efficient Graph Learning

no code implementations26 Sep 2020 Xiaobing Chen, yuke wang, Xinfeng Xie, Xing Hu, Abanti Basak, Ling Liang, Mingyu Yan, Lei Deng, Yufei Ding, Zidong Du, Yunji Chen, Yuan Xie

Graph convolutional network (GCN) emerges as a promising direction to learn the inductive representation in graph data commonly used in widespread applications, such as E-commerce, social networks, and knowledge graphs.

Hardware Architecture

HyGCN: A GCN Accelerator with Hybrid Architecture

1 code implementation7 Jan 2020 Mingyu Yan, Lei Deng, Xing Hu, Ling Liang, Yujing Feng, Xiaochun Ye, Zhimin Zhang, Dongrui Fan, Yuan Xie

In this work, we first characterize the hybrid execution patterns of GCNs on Intel Xeon CPU.

Distributed, Parallel, and Cluster Computing

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