@inproceedings{0891e6578886414088e439ed68c8b563,
title = "Acceleration of word2vec using GPUs",
abstract = "Word2vec is a widely used word embedding toolkit which generates word vectors by training input corpus. Since word vector can represent an exponential number of word cluster and enables reasoning of words with simple algebraic operations, it has become a widely used representation for the subsequent NLP tasks. In this paper, we present an efficient parallelization of word2vec using GPUs that preserves the accuracy. With two K20 GPUs, the proposed acceleration technique achieves 1.7M words/sec, which corresponds to about 20× of speedup compared to a single-threaded CPU execution.",
keywords = "CUDA, Machine learning, Natural language processing, Neural network, Word embedding, Word2vec",
author = "Seulki Bae and Youngmin Yi",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 23rd International Conference on Neural Information Processing, ICONIP 2016 ; Conference date: 16-10-2016 Through 21-10-2016",
year = "2016",
doi = "10.1007/978-3-319-46672-9_31",
language = "English",
isbn = "9783319466712",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "269--279",
editor = "Seiichi Ozawa and Kazushi Ikeda and Derong Liu and Akira Hirose and Kenji Doya and Minho Lee",
booktitle = "Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings",
address = "Germany",
}