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

ASPLOS2025顶会

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 出版方

2025年份

2被引次数

2顶会引用

摘要

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,