Profiling Analysis for Enhancing the Performance of Graph Neural Networks.

Since the turn of the century, Graph Neural Networks (GNNs) have come into the forefront for collaborative filtering (CF). While not immediately recognized by the average user, GNNs are providing users CF recommendations across all aspects of life. The Graph Convolutional Network (GCN) has provided a strong foundation for CF and node classification in recent years. A great number of models are based on the design of the GCN. GNN models are highly complex and require large labeled datasets making training costly. Analyzing the performance characteristics of GNNs provides insight into opportunities for model enhancement and hardware acceleration. In this paper, we describe and analyze two GNN models: LightGCN and ExpressGNN. In LightGCN we find an abundance of elementwise operations yielding to high levels of pipeline stalls. With ExpressGNN, we find most kernels are based in GEMM (General Matrix Multiplication) and GEMV (General Matrix-Vector Multiplication) operations. ExpressGNN has the possibility to greatly benefit from accelerators, including the use of tensor cores in Nvidia's GPUs.