  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.
