Scalable Parallel EM Algorithms for LDA in Multi Core Systems 1.0.0

Xiaosheng Liu, Jia Zeng, Xi Yang, Jianfeng Yan, Qiang Yang — Fri, 05 Dec 2014 10:18:36 -0000

Latent Dirichlet allocation (LDA) is a widely-used probabilistic topic modeling tool for content analysis such as web mining. To handle web-scale content analysis on just a single PC, we propose multi-core parallel expectation-maximization (PEM) algorithms to infer and estimate LDA parameters in shared memory systems. By avoiding memory access conflicts, reducing the locking time among multiple threads and residual-based dynamic scheduling, we show that PEM algorithms are more scalable and accurate than the current state-of-the-art parallel LDA algorithms on a commodity PC.

mloss.org Scalable Parallel EM Algorithms for LDA in Multi Core Systems

Scalable Parallel EM Algorithms for LDA in Multi Core Systems 1.0.0