Parallel implementations of probabilistic latent semantic analysis on graphic processing units
Chen, Hang
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https://hdl.handle.net/2142/18415
Description
Title
Parallel implementations of probabilistic latent semantic analysis on graphic processing units
Author(s)
Chen, Hang
Issue Date
2011-01-14T22:50:06Z
Director of Research (if dissertation) or Advisor (if thesis)
Zhai, ChengXiang
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Probabilistic latent semantic analysis (PLSA)
Graphics processing unit (GPU)
Compute Unified Device Architecture (CUDA)
speedup
parallel
workload balance
memory schema
optimization
Abstract
Probabilistic Latent Semantic Analysis (PLSA) has been successfully applied to many text mining tasks such as retrieval, clustering, summarization, etc. PLSA involves iterative computation for a large number of parameters and may take hours or even days to process a large dataset, thus speeding up PLSA is highly motivated in the domain of text mining. Recently, the general purpose graphic processing units (GPGPU) have become a powerful parallel computing platform, not only because of GPU's multi-core structure and high memory bandwidth, but also because of the recent efforts devoted into building a programming framework to enable developers to easily manipulate GPU's computing power. In this paper, we introduced two methods to parallelize and speed up PLSA via GPGPU. Related issues are addressed including workload balance, block-thread layout, memory and data access optimization, etc. The GPU in use is NVidia GTX480 (costs $450 in market). Experimental results show that our methods can process 300,000 documents in 12 seconds which is a 33x speedup compared with traditional PLSA implementation running on 3.0GHz Intel Xeon CPU. The significant speedup can bring researchers in the text mining domain brand new experience.
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