Many applications today, such as natural language processing, network and code analysis, rely on semantically embedding objects into low-dimensional fixed-length vectors. Such embeddings naturally provide a way to perform useful downstream tasks, such as identifying relations among objects and predicting objects for a given context. Unfortunately, training accurate embeddings is usually computationally intensive and requires processing large amounts of data.
This paper presents a distributed training framework for a class of applications that use Skip-gram-like models to generate embeddings. We call this class Any2Vec and it includes Word2Vec (Gensim), and Vertex2Vec (DeepWalk and Node2Vec) among others. We first formulate Any2Vec training algorithm as a graph application. We then adapt the state-of-the-art distributed graph analytics framework, D-Galois, to support dynamic graph generation and re-partitioning, and incorporate novel communication optimizations. We show that on a cluster of 32 48-core hosts our framework GraphAny2Vec matches the accuracy of the state-of-the-art shared-memory implementations of Word2Vec and Vertex2Vec, and gives geo-mean speedups of 12x and 5x respectively. Furthermore, GraphAny2Vec is on average 2x faster than DMTK, the state-of-the-art distributed Word2Vec implementation, on 32 hosts while yielding much better accuracy.