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)

摘要： 分层分布式狄利克雷分布(HD-LDA)算法是一个对潜在狄利克雷分布(LDA)进行改进的基于概率增长模型的文本分类算法，与只能在单机上运行的LDA算法相比，可以运行在分布式框架下，进行分布式并行处理。Mahout在Hadoop框架下实现了HD-LDA算法，但是因为单节点算法的计算量大，仍然存在对大数据分类运行时间太长的问题。而大规模文本集合分散到多个节点上迭代推导，单个节点上文档集合的推导仍是顺序进行的，所以处理大规模文本集合时仍然需要很长时间才能完成全部文本的分类。为此，提出将Hadoop与图形处理器(GPU)相结合，将单节点文本集合的推导过程转移到GPU上运行，实现单节点多个文档并行推导，利用多台并行的GPU对HD-LDA算法进行加速。应用结果表明，使用该方法能使分布式框架下的HD-LDA算法对大规模文本集合处理达到7倍的加速比。

关键词: 分层分布式狄利克雷分布, 潜在狄利克雷分布, 文本分类, 分布式框架, 并行图形处理器