G <sup>3</sup>
Husong Liu, Shengliang Lu, Xinyu Chen, Bingsheng He
Abstract
This paper demonstrates G 3 , a framework for <u>G</u>raph Neural Network (GNN) training, tailored from <u>G</u>raph processing systems on <u>G</u>raphics processing units (GPUs). G 3 aims at improving the efficiency of GNN training by supporting graph-structured operations using parallel graph processing systems. G 3 enables users to leverage the massive parallelism and other architectural features of GPUs in the following two ways: building GNN layers by writing sequential C/C++ code with a set of flexible APIs (Application Programming Interfaces); creating GNN models with essential GNN operations and layers provided in G 3 . The runtime system of G 3 automatically executes the user-defined GNNs on the GPU, with a series of graph-centric optimizations enabled. We demonstrate the steps of developing some popular GNN models with G 3 , and the superior performance of G 3 against existing GNN training systems, i.e., PyTorch and TensorFlow.