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 2× ∼ 5× speedup over the best competing approaches. DGL-KE is available on https://github.com/awslabs/dgl-ke.
|Original language||English (US)|
|Title of host publication||SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||10|
|State||Published - Jul 25 2020|
|Event||43rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020 - Virtual, Online, China|
Duration: Jul 25 2020 → Jul 30 2020
|Name||SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval|
|Conference||43rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020|
|Period||7/25/20 → 7/30/20|
Bibliographical notePublisher Copyright:
© 2020 ACM.
- distributed training
- knowledge graph embeddings
- large scale