Efficient scaling of dynamic graph neural networks
Author:
Affiliation:
1. IBM Research, India
2. IBM T.J. Watson Research Center
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3458817.3480858
Reference31 articles.
Cited by 8 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. DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks;Proceedings of the ACM on Management of Data;2023-12-08
2. A Comprehensive Survey on Distributed Training of Graph Neural Networks;Proceedings of the IEEE;2023-12
3. DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training;Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis;2023-11-11
4. BLAD: Adaptive Load Balanced Scheduling and Operator Overlap Pipeline For Accelerating The Dynamic GNN Training;Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis;2023-11-11
5. Redundancy-Free High-Performance Dynamic GNN Training with Hierarchical Pipeline Parallelism;Proceedings of the 32nd International Symposium on High-Performance Parallel and Distributed Computing;2023-08-07
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