PLDA+: Parallel Latent Dirichlet Allocation with Data Placement and Pipeline Processing
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| Authors | Zhiyuan Liu, Yuzhou Zhang, E. Chang, Maosong Sun |
| Journal/Conference Name | A |
| Paper Category | Artificial Intelligence |
| Paper Abstract | Previous methods of distributed Gibbs sampling for LDA run into either memory or communication bottlenecks. To improve scalability, we propose four strategies data placement, pipeline processing, word bundling, and priority-based scheduling. Experiments show that our strategies significantly reduce the unparallelizable communication bottleneck and achieve good load balancing, and hence improve scalability of LDA. |
| Date of publication | 2011 |
| Code Programming Language | C++ |
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