Voyager: Input-Adaptive Algebraic Transformations for High-Performance Graph Neural Networks

Yangjie Zhou; Wenting Shen; Jingwen Leng; Shuwen Lu; Zihan Liu; Weihao Cui; Zhendong Zhang; Wencong Xiao; Baole Ai; Yong Li; Wei Lin; Deze Zeng; Yun Liang; Quan Chen; Ning Liu; Minyi Guo

doi:10.1145/3676642.3736121

ASPLOS2025Top-tier venue

Voyager: Input-Adaptive Algebraic Transformations for High-Performance Graph Neural Networks

Yangjie Zhou, Wenting Shen, Jingwen Leng, Shuwen Lu, Zihan Liu, Weihao Cui, Zhendong Zhang, Wencong Xiao, Baole Ai, Yong Li, Wei Lin, Deze Zeng

DOI Publisher

2025Year

2Citations

2Top-tier citations

Abstract

Graph neural networks (GNNs) are gaining popularity in diverse application domains and growing in complexity.As a result, it is crucial to achieve high-performance GNN execution.Among various techniques, algebraic transformations, including operator reordering and operator fusion, have been successfully applied to improve the computation and memory access efficiencies of DNN models.However,