  The Deep Graph Library (DGL) was designed as a tool to enable structure
learning from graphs, by supporting a core abstraction for graphs, including
the popular Graph Neural Networks (GNN). DGL contains implementations of all
core graph operations for both the CPU and GPU. In this paper, we focus
specifically on CPU implementations and present performance analysis,
optimizations and results across a set of GNN applications using the latest
version of DGL(0.4.3). Across 7 applications, we achieve speed-ups ranging
from1 1.5x-13x over the baseline CPU implementations.
