Graphiler: Optimizing Graph Neural Networks with Message Passing Data Flow Graph

Graph neural networks (GNNs) are a new class of powerful machine learning models, but easy programming and efficient computing is often at odds. Current GNN frameworks are based on a message passing paradigm, and allow the concise expression of GNN models using built-in primitives and user defined functions (UDFs). While built-in primitives offer high performance, they are limited in expressiveness; UDFs are flexible, but often have low performance and use excessive memory. In this paper, we propose Graphiler, a compiler stack for GNNs which achieves high performance while offering the flexibility of the UDF programming interface. At the core of Graphiler is a novel abstraction called Message Passing Data Flow Graph (MP-DFG), which enables optimizations that substantially reduce computational redundancy and memory footprint, and optimizes both homogeneous and heterogeneous GNNs under a unified framework. Experiments show Graphiler can accelerate UDF GNNs by up to two orders of magnitude, and achieve performance close to or superior to expert implementations, and do so with substantial memory savings.