GNN is a powerful tool to analyze graph structure data. This feature is designed to improve GNN inference and training performance on Intel® CPUs, including the new 4th Gen Intel® Xeon® Scalable processors.
PyTorch Geometric (PyG) is a very popular library built upon PyTorch to perform GNN workflows. Currently on CPU, GNN models of PyG run slowly due to the lack of GNN-related sparse matrix multiplication operations (i.e., SpMM_reduce) and the lack of several critical kernel-level optimizations (scatter/gather, etc.) tuned for GNN compute.
To address this, optimizations are provided for message passing between adjacent neural network nodes:

scatter_reduce: performance hotspot in message-passing when the edge index is stored in coordinate format (COO).
gather: backward computation of scatter_reduce, specially tuned for the GNN compute when the index is an expanded tensor.
