GNNPerf: Towards Effective Performance Profiling and Analysis Across GNN Frameworks

Graph Neural Networks (GNNs) have been successfully adopted in various application domains and accelerated by parallel processors such as GPUs. Despite the existence of popular frameworks such as Deep Graph Library (DGL) and PyTorch Geometric (PyG), the inconsistent programming paradigms and the lack of a unified analysis toolkit both hinder effective performance comparison among different GNN frameworks. This missing capability not only complicates the selection of the most suitable framework for users, but also impedes developers from optimizing framework implementations. In this paper, we propose GNNPerf, a performance profiling and analysis toolkit for effective performance comparison across GNN frameworks. GNNPerf provides a domain-specific language enabling unified GNN design expression and automatic generation to frameworkspecific implementations. GNNPerf also provides full workflow support for comprehensively evaluating GNN models with easy-to-use profiling, visualization, and analysis. The experimental results demonstrate that the GNNPerf can identify performance bottlenecks and empower users to derive actionable insights, enhancing both GNN model design and framework implementation.