ezLDA: Efficient and Scalable LDA on GPUs

Latent Dirichlet Allocation (LDA) is a statistical approach for topic modeling with a wide range of applications. Attracted by the exceptional computing and memory throughput capabilities, this work introduces ezLDA which achieves efficient and scalable LDA training on GPUs with the following three...

詳細記述

書誌詳細

出版年:	IEEE Access
主要な著者:	Shilong Wang, Hang Liu, Anil Gaihre, Hengyong Yu
フォーマット:	論文
言語:	英語
出版事項:	IEEE 2023-01-01
主題:	Bayes methods GPU high performance computing latent dirichlet allocation LDA parallel algorithms
オンライン･アクセス:	https://ieeexplore.ieee.org/document/10250424/