# Characterizing and Understanding GCNs on GPU

arXiv: https://arxiv.org/abs/2001.10160 · HTML: https://mlsanity.com/paper/2001.10160/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2020.

## Built on by (most cited first, 22 of 22)

- [Trustworthy Graph Neural Networks: Aspects, Methods and Trends](https://mlsanity.com/paper/2205.07424/) (2022)
- [Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis](https://mlsanity.com/paper/2205.09702/) (2022)
- [Survey on Graph Neural Network Acceleration: An Algorithmic Perspective](https://mlsanity.com/paper/2202.04822/) (2022)
- [A Comprehensive Survey on Distributed Training of Graph Neural Networks](https://mlsanity.com/paper/2211.05368/) (2022)
- [Hardware-Aware Graph Neural Network Automated Design for Edge Computing Platforms](https://mlsanity.com/paper/2303.10875/) (2023)
- [Characterizing and Understanding Distributed GNN Training on GPUs](https://mlsanity.com/paper/2204.08150/) (2022)
- [Optimizing Memory Efficiency of Graph Neural Networks on Edge Computing Platforms](https://mlsanity.com/paper/2104.03058/) (2021)
- [GNNSampler: Bridging the Gap between Sampling Algorithms of GNN and Hardware](https://mlsanity.com/paper/2108.11571/) (2021)
- [HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning](https://mlsanity.com/paper/2403.05763/) (2024)
- [GNNBENCH: Fair and Productive Benchmarking for Single-GPU GNN System](https://mlsanity.com/paper/2404.04118/) (2024)
- [Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence](https://mlsanity.com/paper/2407.15320/) (2024)
- [SENSEi: Input-Sensitive Compilation for Accelerating GNNs](https://mlsanity.com/paper/2306.15155/) (2023)
- [Single-GPU GNN Systems: Traps and Pitfalls](https://mlsanity.com/paper/2402.03548/) (2024)
- [Characterizing and Understanding HGNN Training on GPUs](https://mlsanity.com/paper/2407.11790/) (2024)
- [ProGNNosis: A Data-driven Model to Predict GNN Computation Time Using Graph Metrics](https://mlsanity.com/paper/2206.08258/) (2022)
- [Towards Real-Time Temporal Graph Learning](https://mlsanity.com/paper/2210.04114/) (2022)
- [gSuite: A Flexible and Framework Independent Benchmark Suite for Graph Neural Network Inference on GPUs](https://mlsanity.com/paper/2210.11601/) (2022)
- [Architectural Implications of Embedding Dimension during GCN on CPU and GPU](https://mlsanity.com/paper/2212.00827/) (2022)
- [Accelerating Generic Graph Neural Networks via Architecture, Compiler, Partition Method Co-Design](https://mlsanity.com/paper/2308.08174/) (2023)
- [HGNAS: Hardware-Aware Graph Neural Architecture Search for Edge Devices](https://mlsanity.com/paper/2408.12840/) (2024)
- [SiHGNN: Leveraging Properties of Semantic Graphs for Efficient HGNN Acceleration](https://mlsanity.com/paper/2408.15089/) (2024)
- [GCoDE: Efficient Device-Edge Co-Inference for GNNs via Architecture-Mapping Co-Search](https://mlsanity.com/paper/2512.11856/) (2025)

## 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.