The realm of high-performance computing has expanded to include machine learning tasks of many forms. Graph Neural Networks (GNNs) are becoming increasingly popular, with their applications expanding across diverse domains. As the scale of graph data continues to grow, including larger numbers of nodes, edges, and higher embedding dimensions, standardized libraries such as DGL and PyG have been developed to facilitate GNN computation. These libraries allow the computation to be described a a high level of abstraction, providing high productivity on the part of the developer. However, with the rapid increase in the number of processor cores and the evolution of multicore architectures, these libraries often show poor scalability and fail to execute GNN inference efficiently on the latest multicore systems, particularly those with upwards of a hundred cores. To address this limitation, we introduce FrontGI, a fast and scalable set of techniques that advances the frontier of GNN inference on large-scale graphs (over 100,000 vertices) by leveraging graph-property- and architecture-aware design. FrontGI employs different parallelization strategies optimized for diverse graph structures, maximizing the utilization of multi-level cache hierarchies in multicore systems. We evaluate a number of GNN models with FrontGI on a 128-core AMD EPYC system. Compared to state-of-the-art libraries, FrontGI achieves up to 3.35$\times$, 3.20$\times$, 2.89$\times$ and 3.10$\times$ speedups over DGL for GCN, GraphSAGE, GIN, and SGC models, respectively, and even greater improvements over PyG, across a range of large-scale, high-dimensional graph datasets with diverse structural properties.

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