Characterizing the Efficiency of Graph Neural Network Frameworks with a Magnifying Glass
Not on the map. Why?
Abstract (arXiv)
Graph neural networks (GNNs) have received great attention due to their success in various graph-related learning tasks. Several GNN frameworks have then been developed for fast and easy implementation of GNN models. Despite their popularity, they are not well documented, and their implementations and system performance have not been well understood. In particular, unlike the traditional GNNs that are trained based on the entire graph in a full-batch manner, recent GNNs have been developed with different graph sampling techniques for mini-batch training of GNNs on large graphs. While they improve the scalability, their training times still depend on the implementations in the frameworks as sampling and its associated operations can introduce non-negligible overhead and computational cost. In addition, it is unknown how much the frameworks are 'eco-friendly' from a green computing perspective. In this paper, we provide an in-depth study of two mainstream GNN frameworks along with three state-of-the-art GNNs to analyze their performance in terms of runtime and power/energy consumption. We conduct extensive benchmark experiments at several different levels and present detailed analysis results and observations, which could be helpful for further improvement and optimization.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 26 of its 40 references that are in the map. First posted on arXiv 6 Nov 2022.
Builds on
26 of 40 references.
References · 26
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
- 2017Graph Attention Networkscited by 8,923
- 2017Inductive Representation Learning on Large Graphscited by 6,320
- 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
- 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
- 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,089
- 2019Simplifying Graph Convolutional Networkscited by 1,477
- 2018Relational inductive biases, deep learning, and graph networkscited by 1,360
- 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,254
- 2020Simple and Deep Graph Convolutional Networkscited by 860
- 2021How Attentive are Graph Attention Networks?cited by 835
14 more
- 2020Benchmarking Graph Neural Networkscited by 680
- 2019Green AIcited by 627
- 2019GraphSAINT: Graph Sampling Based Inductive Learning Methodcited by 618
- 2018FastGCN: Fast Learning with Graph Convolutional Networks via Importance Samplingcited by 584
- 2019Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networkscited by 541
- 2020Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networkscited by 260
- 2019Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networkscited by 177
- 2017Topology Adaptive Graph Convolutional Networkscited by 113
- 2021DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networkscited by 59
- 2020Accelerating Graph Sampling for Graph Machine Learning using GPUscited by 29
- 2021Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphscited by 22
- 2021Efficient Graph Deep Learning in TensorFlow with tf_geometriccited by 17
- 2020Architectural Implications of Graph Neural Networkscited by 12
- 2022Characterizing and Understanding Distributed GNN Training on GPUscited by 7
Built on by
Citing papers per year: 2024: 2; 2025: 1.
3 papers.