mlsanityData to 14 Oct 2026

Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networks

arXiv 2308.11825 ↗First posted 22 Aug 2023cs.AR cs.LGcited by 22

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Abstract (arXiv)

Graph Convolutional Networks (GCNs) are pivotal in extracting latent information from graph data across various domains, yet their acceleration on mainstream GPUs is challenged by workload imbalance and memory access irregularity. To address these challenges, we present Accel-GCN, a GPU accelerator architecture for GCNs. The design of Accel-GCN encompasses: (i) a lightweight degree sorting stage to group nodes with similar degree; (ii) a block-level partition strategy that dynamically adjusts warp workload sizes, enhancing shared memory locality and workload balance, and reducing metadata overhead compared to designs like GNNAdvisor; (iii) a combined warp strategy that improves memory coalescing and computational parallelism in the column dimension of dense matrices. Utilizing these principles, we formulated a kernel for sparse matrix multiplication (SpMM) in GCNs that employs block-level partitioning and combined warp strategy. This approach augments performance and multi-level memory efficiency and optimizes memory bandwidth by exploiting memory coalescing and alignment. Evaluation of Accel-GCN across 18 benchmark graphs reveals that it outperforms cuSPARSE, GNNAdvisor, and graph-BLAST by factors of 1.17 times, 1.86 times, and 2.94 times respectively. The results underscore Accel-GCN as an effective solution for enhancing GCN computational efficiency.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). Builds on 31 of its 65 references that are in the map. First posted on arXiv 22 Aug 2023.

Builds on

31 of 65 references.

References · 31

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Inductive Representation Learning on Large Graphscited by 6,320
  3. 2018How Powerful are Graph Neural Networks?cited by 4,393
  4. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  5. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
  6. 2020Graph Neural Networks in Recommender Systems: A Surveycited by 494
  7. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 261
  8. 2022Brain Network Transformercited by 94
  9. 2022What Makes Convolutional Models Great on Long Sequence Modeling?cited by 87
  10. 2020Molecular graph generation with Graph Neural Networkscited by 80
  11. 2022FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generationcited by 67
  12. 2022Accelerating Dataset Distillation via Model Augmentationcited by 62
19 more
  1. 2020GE-SpMM: General-purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networkscited by 42
  2. 2018Dissecting the NVIDIA Volta GPU Architecture via Microbenchmarkingcited by 35
  3. 2021Accelerating Framework of Transformer by Hardware Design and Model Compression Co-Optimizationcited by 27
  4. 2020Fast OSCAR and OWL Regression via Safe Screening Rulescited by 25
  5. 2022A Length Adaptive Algorithm-Hardware Co-design of Transformer on FPGA Through Sparse Attention and Dynamic Pipeliningcited by 22
  6. 2021Zero-Shot Scene Graph Relation Prediction through Commonsense Knowledge Integrationcited by 21
  7. 2021Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigmcited by 21
  8. 2022The Larger The Fairer? Small Neural Networks Can Achieve Fairness for Edge Devicescited by 21
  9. 2022I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandizationcited by 21
  10. 2018Design Principles for Sparse Matrix Multiplication on the GPUcited by 19
  11. 2020Intermittent Inference with Nonuniformly Compressed Multi-Exit Neural Network for Energy Harvesting Powered Devicescited by 19
  12. 2022Towards Sparsification of Graph Neural Networkscited by 18
  13. 2022Dynamic Sparse Training via Balancing the Exploration-Exploitation Trade-offcited by 15
  14. 2021Generic Neural Architecture Search via Regressioncited by 14
  15. 2021Binary Complex Neural Network Acceleration on FPGAcited by 14
  16. 2022An Automatic and Efficient BERT Pruning for Edge AI Systemscited by 13
  17. 2020Guiding Cascading Failure Search with Interpretable Graph Convolutional Networkcited by 7
  18. 2023PASNet: Polynomial Architecture Search Framework for Two-party Computation-based Secure Neural Network Deploymentcited by 7
  19. 2018Implementing Push-Pull Efficiently in GraphBLAScited by 1

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Citing papers per year: 2023: 4; 2024: 17; 2025: 1.

22 papers.

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