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Reducing Communication in Graph Neural Network Training

Reducing Communication in Graph Neural Network Training

7 May 2020
Alok Tripathy
Katherine Yelick
A. Buluç
    GNN
ArXivPDFHTML

Papers citing "Reducing Communication in Graph Neural Network Training"

15 / 15 papers shown
Title
Sparsity-Aware Communication for Distributed Graph Neural Network Training
Sparsity-Aware Communication for Distributed Graph Neural Network Training
Ujjaini Mukhodopadhyay
Alok Tripathy
Oguz Selvitopi
Katherine Yelick
A. Buluç
39
1
0
07 Apr 2025
PID-Comm: A Fast and Flexible Collective Communication Framework for
  Commodity Processing-in-DIMM Devices
PID-Comm: A Fast and Flexible Collective Communication Framework for Commodity Processing-in-DIMM Devices
Si Ung Noh
Junguk Hong
Chaemin Lim
Seong-Yeol Park
Jeehyun Kim
Hanjun Kim
Youngsok Kim
Jinho Lee
29
6
0
13 Apr 2024
RDMA-Based Algorithms for Sparse Matrix Multiplication on GPUs
RDMA-Based Algorithms for Sparse Matrix Multiplication on GPUs
Benjamin Brock
A. Buluç
Katherine Yelick
13
2
0
29 Nov 2023
LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation
LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation
Rui Xue
Haoyu Han
MohamadAli Torkamani
Jian Pei
Xiaorui Liu
GNN
25
19
0
03 Feb 2023
Extreme Acceleration of Graph Neural Network-based Prediction Models for
  Quantum Chemistry
Extreme Acceleration of Graph Neural Network-based Prediction Models for Quantum Chemistry
Hatem Helal
J. Firoz
Jenna A. Bilbrey
M. M. Krell
Tom Murray
Ang Li
S. Xantheas
Sutanay Choudhury
GNN
25
5
0
25 Nov 2022
Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency
  Analysis
Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis
Maciej Besta
Torsten Hoefler
GNN
32
55
0
19 May 2022
Scalable algorithms for physics-informed neural and graph networks
Scalable algorithms for physics-informed neural and graph networks
K. Shukla
Mengjia Xu
N. Trask
George Karniadakis
PINN
AI4CE
59
39
0
16 May 2022
BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks
  with Partition-Parallelism and Random Boundary Node Sampling
BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling
Cheng Wan
Youjie Li
Ang Li
Namjae Kim
Yingyan Lin
GNN
27
75
0
21 Mar 2022
Distributed Hybrid CPU and GPU training for Graph Neural Networks on
  Billion-Scale Graphs
Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Graphs
Da Zheng
Xiang Song
Chengrun Yang
Dominique LaSalle
George Karypis
3DH
GNN
17
54
0
31 Dec 2021
GraphTheta: A Distributed Graph Neural Network Learning System With
  Flexible Training Strategy
GraphTheta: A Distributed Graph Neural Network Learning System With Flexible Training Strategy
Yongchao Liu
Houyi Li
Guowei Zhang
Xintan Zeng
Yongyong Li
...
Peng Zhang
Zhao Li
Kefeng Deng
Changhua He
Wenguang Chen
GNN
42
11
0
21 Apr 2021
GMLP: Building Scalable and Flexible Graph Neural Networks with
  Feature-Message Passing
GMLP: Building Scalable and Flexible Graph Neural Networks with Feature-Message Passing
Wentao Zhang
Yu Shen
Zheyu Lin
Yang Li
Xiaosen Li
Wenbin Ouyang
Yangyu Tao
Zhi-Xin Yang
Bin Cui
19
9
0
20 Apr 2021
DistGNN: Scalable Distributed Training for Large-Scale Graph Neural
  Networks
DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks
Vasimuddin
Sanchit Misra
Guixiang Ma
Ramanarayan Mohanty
E. Georganas
A. Heinecke
Dhiraj D. Kalamkar
Nesreen Ahmed
Sasikanth Avancha
GNN
15
119
0
14 Apr 2021
DistDGL: Distributed Graph Neural Network Training for Billion-Scale
  Graphs
DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs
Da Zheng
Chao Ma
Minjie Wang
Jinjing Zhou
Qidong Su
Xiang Song
Quan Gan
Zheng-Wei Zhang
George Karypis
FedML
GNN
14
241
0
11 Oct 2020
Demystifying Graph Databases: Analysis and Taxonomy of Data
  Organization, System Designs, and Graph Queries
Demystifying Graph Databases: Analysis and Taxonomy of Data Organization, System Designs, and Graph Queries
Maciej Besta
Robert Gerstenberger
E. Peter
Marc Fischer
Michal Podstawski
Claude Barthels
Gustavo Alonso
Torsten Hoefler
GNN
17
92
0
20 Oct 2019
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph
  Neural Networks
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
Minjie Wang
Da Zheng
Zihao Ye
Quan Gan
Mufei Li
...
J. Zhao
Haotong Zhang
Alex Smola
Jinyang Li
Zheng-Wei Zhang
AI4CE
GNN
189
743
0
03 Sep 2019
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