High-performance korean morphological analyzer using the mapreduce framework on the GPU

Shiwon Cho, Dong Wook Lee

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

To meet the scalability and performance requirements of data analyses, which often involve voluminous data, efficient parallel or concurrent algorithms and frameworks are essential. We present a high-performance Korean morphological analyzer which employs the MapReduce framework on the graphics processing unit (GPU). MapReduce is a programming framework introduced by Google to aid the development of web search applications on a large number of central processing units (CPUs). GPUs are designed as a special-purpose co-processor. Their programming interfaces are typically formulated for graphics applications. Compared to CPUs, GPUs have greater computation power and memory bandwidth; however, GPUs are more difficult to program because of the design of their architectures. The performance of the Korean morphological analyzer using the MapReduce framework on the GPU is evaluated in comparison with the CPU-based model. The proposed Korean Morphological analyzer shows promising scalable performance on distributed computing with the GPU.

Original languageEnglish
Pages (from-to)573-579
Number of pages7
JournalJournal of Electrical Engineering and Technology
Volume6
Issue number4
DOIs
StatePublished - Jul 2011

Keywords

  • Distributed processing
  • GPGPU
  • Korean morphological analyzer
  • MapReduce
  • Natural language processing

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