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1. TC-GNN: Bridging Sparse GNN Computation and Dense Tensor Cores on GPUs

   Wang, Yuke; Feng, Boyuan; Wang, Zheng; Huang, Guyue; Ding, Yufei. (July 2023, USENIX Association)

   Recently, graph neural networks (GNNs), as the backbone of graph-based machine learning, demonstrate great success in various domains (e.g., e-commerce). However, the performance of GNNs is usually unsatisfactory due to the highly sparse and irregular graph-based operations. To this end, we propose TC-GNN, the first GNN acceleration framework based on GPU Tensor Core Units (TCUs). The core idea is to reconcile the "Sparse" GNN computation with the high-performance "Dense" TCUs. Specifically, we conduct an in-depth analysis of the sparse operations in mainstream GNN computing frameworks. We introduce a novel sparse graph translation technique to facilitate TCU processing of the sparse GNN workload. We implement an effective CUDA core and TCU collaboration design to fully utilize GPU resources. We integrate MGG with the PyTorch framework for high programmability. Rigorous experiments show an average of 1.70× speedup over the state-of-the-art DGL framework across various models and datasets. 

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2. HP-GNN: Generating High Throughput GNN Training Implementation on CPU-FPGA Heterogeneous Platform

   https://doi.org/10.1145/3490422.3502359  

   Lin, Yi-Chien; Zhang, Bingyi; Prasanna, Viktor (February 2022, The 30h ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA))
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   Yi-Chien Lin; Viktor Prasanna (January 2023, IEEE International Parallel & Distributed Processing Symposium)
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   Agarwal, Chirag; Zitnik, Marinka; Lakkaraju, Himabindu. (January 2022, International Conference on Artificial Intelligence and Statistics)
5. Multi-objective Explanations of GNN Predictions

   https://doi.org/10.1109/ICDM51629.2021.00052  

   Liu, Yifei; Chen, Chao; Liu, Yazheng; Zhang, Xi; Xie, Sihong (December 2021, 2021 IEEE International Conference on Data Mining (ICDM))