Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Heterogeneous Graphs-Reference-Cited by-同舟云学术

Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Heterogeneous Graphs

Published:2022-08-14 Issue: Volume: Page:
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Container-title:Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Zheng Da¹,Song Xiang¹,Yang Chengru²,LaSalle Dominique³,Karypis George¹

Affiliation:

1. Amazon, Santa Clara, CA, USA

2. Amazon, Shanghai, China

3. NVIDIA Corporation, Santa Clara, CA, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3534678.3539177

Reference28 articles.

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3. Wei-Lin Chiang , Xuanqing Liu , Si Si , Yang Li , Samy Bengio , and Cho-Jui Hsieh. Cluster-gcn. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , Jul 2019 . Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh. Cluster-gcn. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Jul 2019.

4. Matthias Fey and Jan Eric Lenssen . Fast graph representation learning with pytorch geometric. arXiv preprint arXiv:1903.02428 , 2019 . Matthias Fey and Jan Eric Lenssen. Fast graph representation learning with pytorch geometric. arXiv preprint arXiv:1903.02428, 2019.

5. Swapnil Gandhi and Anand Padmanabha Iyer . P3 : Distributed deep graph learning at scale . In 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21) , pages 551 -- 568 . USENIX Association , July 2021 . Swapnil Gandhi and Anand Padmanabha Iyer. P3: Distributed deep graph learning at scale. In 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21), pages 551--568. USENIX Association, July 2021.

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