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Graph neural network(GNN) has been widely applied in real-world applications,such as product recommendation in e-commerce platforms and risk control infinancial management systems. Several cache-based GNN systems have been builtto accelerate GNN training in a single machine with multiple GPUs. However,these systems fail to train billion-scale graphs efficiently, which is a commonchallenge in the industry. In this work, we propose Legion, a system thatautomatically pushes the envelope of multi-GPU systems for acceleratingbillion-scale GNN training. First, we design a hierarchical graph partitioningmechanism that significantly improves the multi-GPU cache performance. Second,we build a unified multi-GPU cache that helps to minimize the PCIe trafficincurred by caching both graph topology and features with the highest hotness.Third, we develop an automatic caching management mechanism that adapts themulti-GPU cache plan according to the hardware specifications and variousgraphs to maximize the overall training throughput. Evaluations on various GNNmodels and multiple datasets show that Legion supports training billion-scaleGNNs in a single machine and significantly outperforms the state-of-the-artcache-based systems on small graphs.

Legion: Automatically Pushing the Envelope of Multi-GPU System for Billion-Scale GNN Training