Acceleration algorithms in gnns: A survey

Zhezheng Hao

Abstract:

Graph Neural Networks (GNNs) have demonstrated effectiveness in various graph-based tasks.
However, their inefficiency in training and inference presents challenges for scaling up to realworld and large-scale graph applications. To address the critical challenges, a range of algorithms
have been proposed to accelerate training and inference of GNNs, attracting increasing attention
from the research community. In this paper, we
present a systematic review of acceleration algorithms in GNNs, which can be categorized into
three main topics based on their purpose: training acceleration, inference acceleration, and execution acceleration. Specifically, we summarize
and categorize the existing approaches for each
main topic, and provide detailed characterizations
of the approaches within each category. Additionally, we review several libraries related to acceleration algorithms in GNNs and discuss our Scalable Graph Learning (SGL) library. Finally, we
propose promising directions for future research.