DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs

10/11/2020
by   Da Zheng, et al.
1

Graph neural networks (GNN) have shown great success in learning from graph-structured data. They are widely used in various applications, such as recommendation, fraud detection, and search. In these domains, the graphs are typically large, containing hundreds of millions of nodes and several billions of edges. To tackle this challenge, we develop DistDGL, a system for training GNNs in a mini-batch fashion on a cluster of machines. DistDGL is based on the Deep Graph Library (DGL), a popular GNN development framework. DistDGL distributes the graph and its associated data (initial features and embeddings) across the machines and uses this distribution to derive a computational decomposition by following an owner-compute rule. DistDGL follows a synchronous training approach and allows ego-networks forming the mini-batches to include non-local nodes. To minimize the overheads associated with distributed computations, DistDGL uses a high-quality and light-weight min-cut graph partitioning algorithm along with multiple balancing constraints. This allows it to reduce communication overheads and statically balance the computations. It further reduces the communication by replicating halo nodes and by using sparse embedding updates. The combination of these design choices allows DistDGL to train high-quality models while achieving high parallel efficiency and memory scalability. We demonstrate our optimizations on both inductive and transductive GNN models. Our results show that DistDGL achieves linear speedup without compromising model accuracy and requires only 13 seconds to complete a training epoch for a graph with 100 million nodes and 3 billion edges on a cluster with 16 machines.

READ FULL TEXT
12/31/2021

Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Graphs

Graph neural networks (GNN) have shown great success in learning from gr...
04/14/2021

DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks

Full-batch training on Graph Neural Networks (GNN) to learn the structur...
02/17/2020

Ripple Walk Training: A Subgraph-based training framework for Large and Deep Graph Neural Network

Graph neural networks (GNNs) have achieved outstanding performance in le...
11/10/2021

Graph Neural Network Training with Data Tiering

Graph Neural Networks (GNNs) have shown success in learning from graph-s...
09/14/2022

Tuple Packing: Efficient Batching of Small Graphs in Graph Neural Networks

When processing a batch of graphs in machine learning models such as Gra...
10/16/2021

Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining

Improving the training and inference performance of graph neural network...
04/18/2020

DGL-KE: Training Knowledge Graph Embeddings at Scale

Knowledge graphs have emerged as a key abstraction for organizing inform...