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arXiv · 1206.2190

Communication-Efficient Parallel Belief Propagation for Latent Dirichlet Allocation

Jian-feng Yan·Zhi-Qiang Liu·Yang Gao·Jia Zeng
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Abstract

This paper presents a novel communication-efficient parallel belief propagation (CE-PBP) algorithm for training latent Dirichlet allocation (LDA). Based on the synchronous belief propagation (BP) algorithm, we first develop a parallel belief propagation (PBP) algorithm on the parallel architecture. Because the extensive communication delay often causes a low efficiency of parallel topic modeling, we further use Zipf's law to reduce the total communication cost in PBP. Extensive experiments on different data sets demonstrate that CE-PBP achieves a higher topic modeling accuracy and reduces more than 80% communication cost than the state-of-the-art parallel Gibbs sampling (PGS) algorithm.

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Jian-feng Yan, Zhi-Qiang Liu, Yang Gao, Jia Zeng. 2012-06-11. Communication-Efficient Parallel Belief Propagation for Latent Dirichlet Allocation. https://arxiv.org/abs/1206.2190

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