MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training

Author:

Peng Hongwu1ORCID,Xie Xi2ORCID,Shivdikar Kaustubh3ORCID,Hasan Md Amit2ORCID,Zhao Jiahui2ORCID,Huang Shaoyi1ORCID,Khan Omer1ORCID,Kaeli David3ORCID,Ding Caiwen1ORCID

Affiliation:

1. University of Connecticut, Storrs, Connecticut, United States of America

2. University of Connecticut, Storrs, Connecticut, USA

3. Northeastern University, Boston, Massachusetts, United States of America

Funder

NSF (National Science Foundation)

Northeastern University Institute for Experiential AI

NSF IUCRC Center for Hardware and Embedded Systems Security and Trust (CHEST)

Semiconductor Research Corporation (SRC) Artificial Intelligence Hardware program

Advanced Micro Devices (AMD)

Publisher

ACM

Reference64 articles.

1. Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. Open graph benchmark: Datasets for machine learning on graphs. Advances in neural information processing systems, 33:22118--22133, 2020.

2. Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016.

3. Graphsage-based traffic speed forecasting for segment network with sparse data;Liu Jielun;IEEE Transactions on Intelligent Transportation Systems,2020

4. Yuxiao Liu, Ning Zhang, Dan Wu, Audun Botterud, Rui Yao, and Chongqing Kang. Guiding cascading failure search with interpretable graph convolutional network. Computing Research Repository (CoRR) in arXiv, abs/2001.11553, 2020.

5. Graph neural network for traffic forecasting: A survey

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