  Knowledge graphs have emerged as a key abstraction for organizing information
in diverse domains and their embeddings are increasingly used to harness their
information in various information retrieval and machine learning tasks.
However, the ever growing size of knowledge graphs requires computationally
efficient algorithms capable of scaling to graphs with millions of nodes and
billions of edges. This paper presents DGL-KE, an open-source package to
efficiently compute knowledge graph embeddings. DGL-KE introduces various novel
optimizations that accelerate training on knowledge graphs with millions of
nodes and billions of edges using multi-processing, multi-GPU, and distributed
parallelism. These optimizations are designed to increase data locality, reduce
communication overhead, overlap computations with memory accesses, and achieve
high operation efficiency. Experiments on knowledge graphs consisting of over
86M nodes and 338M edges show that DGL-KE can compute embeddings in 100 minutes
on an EC2 instance with 8 GPUs and 30 minutes on an EC2 cluster with 4 machines
with 48 cores/machine. These results represent a 2x~5x speedup over the best
competing approaches. DGL-KE is available on https://github.com/awslabs/dgl-ke.
