mlsanityData to 7 Oct 2026

Characterizing and Understanding GCNs on GPU

arXiv 2001.10160 ↗First posted 28 Jan 2020cs.DCcited by 22

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Abstract (arXiv)

Graph convolutional neural networks (GCNs) have achieved state-of-the-art performance on graph-structured data analysis. Like traditional neural networks, training and inference of GCNs are accelerated with GPUs. Therefore, characterizing and understanding the execution pattern of GCNs on GPU is important for both software and hardware optimization. Unfortunately, to the best of our knowledge, there is no detailed characterization effort of GCN workloads on GPU. In this paper, we characterize GCN workloads at inference stage and explore GCN models on NVIDIA V100 GPU. Given the characterization and exploration, we propose several useful guidelines for both software optimization and hardware optimization for the efficient execution of GCNs on GPU.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jan 2020.

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Citing papers per year: 2021: 2; 2022: 9; 2023: 3; 2024: 7; 2025: 1.

22 papers.

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  1. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
  2. 2022Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysiscited by 29
  3. 2022Survey on Graph Neural Network Acceleration: An Algorithmic Perspectivecited by 26
  4. 2022A Comprehensive Survey on Distributed Training of Graph Neural Networkscited by 15
  5. 2023Hardware-Aware Graph Neural Network Automated Design for Edge Computing Platformscited by 12
  6. 2022Characterizing and Understanding Distributed GNN Training on GPUscited by 7
  7. 2021Optimizing Memory Efficiency of Graph Neural Networks on Edge Computing Platformscited by 6
  8. 2021GNNSampler: Bridging the Gap between Sampling Algorithms of GNN and Hardwarecited by 6
  9. 2024HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoningcited by 3
  10. 2024GNNBENCH: Fair and Productive Benchmarking for Single-GPU GNN Systemcited by 3
  11. 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligencecited by 2
  12. 2023SENSEi: Input-Sensitive Compilation for Accelerating GNNscited by 1
10 more
  1. 2024Single-GPU GNN Systems: Traps and Pitfallscited by 1
  2. 2024Characterizing and Understanding HGNN Training on GPUscited by 1
  3. 2022ProGNNosis: A Data-driven Model to Predict GNN Computation Time Using Graph Metricscited by 0
  4. 2022Towards Real-Time Temporal Graph Learningcited by 0
  5. 2022gSuite: A Flexible and Framework Independent Benchmark Suite for Graph Neural Network Inference on GPUscited by 0
  6. 2022Architectural Implications of Embedding Dimension during GCN on CPU and GPUcited by 0
  7. 2023Accelerating Generic Graph Neural Networks via Architecture, Compiler, Partition Method Co-Designcited by 0
  8. 2024HGNAS: Hardware-Aware Graph Neural Architecture Search for Edge Devicescited by 0
  9. 2024SiHGNN: Leveraging Properties of Semantic Graphs for Efficient HGNN Accelerationcited by 0
  10. 2025GCoDE: Efficient Device-Edge Co-Inference for GNNs via Architecture-Mapping Co-Searchcited by 0