doi:

DOI: 10.3724/SP.J.1087.2013.03313

Journal of Computer Applications (计算机应用) 2013/33:12 PP.3313-3316

Accelerating hierarchical distributed latent Dirichlet allocation algorithm by parallel GPU


Abstract:
Hierarchical Distributed Latent Dirichlet Allocation (HD-LDA), a popular topic modeling technique for exploring collections, is an improved Latent Dirichlet Allocation (LDA) algorithm running in distributed environment. Mahout has realized HD-LDA algorithm in the framework of Hadoop. However the algorithm processed the whole documents of a single node in sequence, and the execution time of the HD-LDA program was very long when processing a large amount of documents. A new method was proposed to combine Hadoop with Graphic Processing Unit (GPU) to solve the above problem when transferring the computation from CPU to GPU. The application results show that combining the Hadoop with GPU which processes many documents in parallel can decrease the execution time of HD-LDA program greatly and achieve seven times speedup.

Key words:Hierarchical Distributed Latent Dirichlet Allocation (HD-LDA),Latent Dirichlet Allocation (LDA),text classification,distributed environment,parallel Graphic Processing Unit (GPU)

ReleaseDate:2014-07-21 16:59:35



PDF