Efficient Data Loader for Fast Sampling-Based GNN Training on Large Graphs-Reference-Cited by-同舟云学术

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Efficient Data Loader for Fast Sampling-Based GNN Training on Large Graphs

Published:2021-10-01 Issue:10 Volume:32 Page:2541-2556
ISSN:1045-9219
Container-title:IEEE Transactions on Parallel and Distributed Systems
language:
Short-container-title:IEEE Trans. Parallel Distrib. Syst.

Author:

Bai Youhui,Li Cheng^ORCID,Lin Zhiqi^ORCID,Wu Yufei^ORCID,Miao Youshan^ORCID,Liu Yunxin^ORCID,Xu Yinlong

Funder

National Key R&D Program of China

National Natural Science Foundation of China

USTC Research Funds of the Double First-Class Initiative

Huawei and Microsoft

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Hardware and Architecture,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/71/9399068/09376972.pdf?arnumber=9376972

Cited by 20 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Expediting Distributed GNN Training with Feature-only Partition and Optimized Communication Planning;IEEE INFOCOM 2024 - IEEE Conference on Computer Communications;2024-05-20

2. Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis;IEEE Transactions on Pattern Analysis and Machine Intelligence;2024-05

3. Distributed Graph Neural Network Training: A Survey;ACM Computing Surveys;2024-04-10

4. Trustworthy Graph Neural Networks: Aspects, Methods, and Trends;Proceedings of the IEEE;2024-02

5. GDL-GNN: Applying GPU Dataloading of Large Datasets for Graph Neural Network Inference;Lecture Notes in Computer Science;2024

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