Graph neural networks (GNNs) are gaining increasing popularity as a promising approach to machine learning on graphs. Unlike traditional graph workloads where each vertex/edge is associated with a scalar, GNNs attach a feature tensor to each vertex/edge. This additional feature dimension, along with consequently more complex vertex- and edge-wise computations, has enormous implications on locality and parallelism, which existing graph processing systems fail to exploit. This paper proposes FeatGraph to accelerate GNN workloads by co-optimizing graph traversal and feature dimension computation. FeatGraph provides a flexible programming interface to express diverse GNN models by composing coarse-grained sparse templates with fine-grained user-defined functions (UDFs) on each vertex/edge. FeatGraph incorporates optimizations for graph traversal into the sparse templates and allows users to specify optimizations for UDFs with a feature dimension schedule (FDS). FeatGraph speeds up end-to-end GNN training and inference by up to 32x on CPU and 7x on GPU.
@article{arxiv.2008.11359,
title = {FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems},
author = {Yuwei Hu and Zihao Ye and Minjie Wang and Jiali Yu and Da Zheng and Mu Li and Zheng Zhang and Zhiru Zhang and Yida Wang},
journal= {arXiv preprint arXiv:2008.11359},
year = {2020}
}