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A graph neural network (GNN) enables deep learning on structured graph data.There are two major GNN training obstacles: 1) it relies on high-end serverswith many GPUs which are expensive to purchase and maintain, and 2) limitedmemory on GPUs cannot scale to today's billion-edge graphs. This paper presentsDorylus: a distributed system for training GNNs. Uniquely, Dorylus can takeadvantage of serverless computing to increase scalability at a low cost.  The key insight guiding our design is computation separation. Computationseparation makes it possible to construct a deep, bounded-asynchronous pipelinewhere graph and tensor parallel tasks can fully overlap, effectively hiding thenetwork latency incurred by Lambdas. With the help of thousands of Lambdathreads, Dorylus scales GNN training to billion-edge graphs. Currently, forlarge graphs, CPU servers offer the best performance-per-dollar over GPUservers. Just using Lambdas on top of CPU servers offers up to 2.75x moreperformance-per-dollar than training only with CPU servers. Concretely, Dorylusis 1.22x faster and 4.83x cheaper than GPU servers for massive sparse graphs.Dorylus is up to 3.8x faster and 10.7x cheaper compared to existingsampling-based systems.

Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads