Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
This provides a high-performance, framework-neutral library for researchers and practitioners working with graph neural networks, enabling easier development and scaling of GNN applications.
The paper tackles the need for efficient tools in deep graph learning by introducing the Deep Graph Library (DGL), which uses graph-centric abstractions and sparse tensor operations to optimize performance, resulting in significant speed and memory improvements over other frameworks in benchmarks.
Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs. In this paper, we present the design principles and implementation of Deep Graph Library (DGL). DGL distills the computational patterns of GNNs into a few generalized sparse tensor operations suitable for extensive parallelization. By advocating graph as the central programming abstraction, DGL can perform optimizations transparently. By cautiously adopting a framework-neutral design, DGL allows users to easily port and leverage the existing components across multiple deep learning frameworks. Our evaluation shows that DGL significantly outperforms other popular GNN-oriented frameworks in both speed and memory consumption over a variety of benchmarks and has little overhead for small scale workloads.