HGL: Accelerating Heterogeneous GNN Training with Holistic Representation and Optimization

Author:

Gui Yuntao1,Wu Yidi1,Yang Han1,Jin Tatiana1,Li Boyang1,Zhou Qihui1,Cheng James1,Yu Fan2

Affiliation:

1. The Chinese-University of Hong Kong,Hong Kong SAR,China

2. Huawei Technologies Co. Ltd,Shenzhen,China

Publisher

IEEE

Reference51 articles.

1. Fast graph representation learning with PyTorch Geometric;fey;ICLR Workshop on Representation Learning on Graphs and Manifolds,2019

2. Heterogeneous Graph Attention Network

3. Modeling Relational Data with Graph Convolutional Networks

4. Seastar

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1. HiHGNN: Accelerating HGNNs Through Parallelism and Data Reusability Exploitation;IEEE Transactions on Parallel and Distributed Systems;2024-07

2. FASTEN: Fast GPU-accelerated Segmented Matrix Multiplication for Heterogenous Graph Neural Networks;Proceedings of the 38th ACM International Conference on Supercomputing;2024-05-30

3. A Coordinated Strategy for GNN Combining Computational Graph and Operator Optimizations;Proceedings of the 38th ACM International Conference on Supercomputing;2024-05-30

4. A Survey on Graph Neural Network Acceleration: A Hardware Perspective;Chinese Journal of Electronics;2024-05

5. Scalable and Efficient Full-Graph GNN Training for Large Graphs;Proceedings of the ACM on Management of Data;2023-06-13

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