Journal of the Korean Data and Information Science Society

한국데이터정보과학회 (The Korean Data and Information Science Society)
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GPU를 이용한 범용 계산의 소개

Introduction to general purpose GPU computing

  • Yu, Donghyeon (Department of Statistics, Seoul National University) ;
  • Lim, Johan (Department of Statistics, Seoul National University)
  • 투고 : 2013.07.09
  • 심사 : 2013.09.06
  • 발행 : 2013.09.30
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초록

최근 과학 기술의 빠른 발전에 따라 대용량 자료가 출현하였고 이에 대한 분석의 중요도도 높아졌다. 대용량 자료의 분석에 가장 중요한 부분중 하나가 고성능 컴퓨팅 기법이고 본 논문에서는 최근 통계학계의 많은 관심을 받고 있는 GPU (graphics processing unit)기반 병렬 계산에 대한 기초적인 소개를 한다.

Recent advances in computer technology introduce massive data and their analysis becomes important. The high performance computing is one of the most essential part in analysis of massive data. In this paper, we review the general purpose of the graphics processing unit and its application to parallel computing, which has been of great interest in statistics communities.

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참고문헌

  1. Jacob, P., Robert, C. P. and Smith, M.H. (2011). Using parallel computation to improve independent Metropolis-Hastings based estimation. Journal of Computational and Graphical Statistics, 20, 616-635. https://doi.org/10.1198/jcgs.2011.10167
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  5. Suchard, M., Wang, Q., Chan, C., Frelinger, J., Cron, A. and West, M. (2010). Understanding GPU programming for statistical computation: Studies in massively parallel massive mixture. Journal of Computational and Graphical Statistics, 19, 419-438. https://doi.org/10.1198/jcgs.2010.10016
  6. Yu, D., Won, J-H., Lee, T., Lim, J. and Yoon, S-R. (2013). High-dimensional fused lasso regression using majorization-minimization and parallel processing. http://arxiv.org/abs/1306.1970.
  7. Zhou, H., Lange, K. and Suchard, M.A. (2010). Graphics processing units and high-dimensional optimization. Statistical Science, 25, 311-324. https://doi.org/10.1214/10-STS336

피인용 문헌

  1. BCDR algorithm for network estimation based on pseudo-likelihood with parallelization using GPU vol.27, pp.2, 2016, https://doi.org/10.7465/jkdi.2016.27.2.381