Since its introduction, Word2Vec and its variants are widely used to learn semantics-preserving representations of words or entities in an embedding space, which can be used to produce state-of-art results for various Natural Language Processing tasks. Existing implementations aim to learn efficiently by running multiple threads in parallel while operating on a single model in shared memory, ignoring incidental memory update collisions. We show that these collisions can degrade the efficiency of parallel learning, and propose a straightforward caching strategy that improves the efficiency by a factor of 4.
@article{arxiv.1606.07822,
title = {Efficient Parallel Learning of Word2Vec},
author = {Jeroen B. P. Vuurens and Carsten Eickhoff and Arjen P. de Vries},
journal= {arXiv preprint arXiv:1606.07822},
year = {2016}
}