Characterizing the Efficiency of Graph Neural Network Frameworks with a Magnifying Glass
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 a...
Saved in:
Main Authors | , , , |
---|---|
Format | Journal Article |
Language | English |
Published |
06.11.2022
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | 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. |
---|---|
DOI: | 10.48550/arxiv.2211.03021 |