Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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GPU Implementation Techniques of Genetic Algorithm and Comparative Studies

유전 알고리즘의 GPU 구현 기법 및 비교 연구

  • 현병용 (서경대학교 전자공학과) ;
  • 서기성 (서경대학교 전자공학과)
  • Received : 2010.07.15
  • Accepted : 2011.03.13
  • Published : 2011.04.01
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Abstract

GPU (Graphics Processing Units) is consists of SIMD (Single Instruction Multiple Data) architecture and provides fast parallel processing. A GA (Genetic Algorithm), which requires large computations, is implemented in GPU using CUDA (Compute Unified Device Architecture). Three kinds of execution models are presented according to different combinations of processing modules in GPU. Comparison experiments between GPU models and CPU are tested for a couple of benchmark problems by variation of population sizes and complexity of problem sizes.

Keywords

References

  1. 이주석, 류현곤, "GPU 병렬 컴퓨팅 기술을 이용한 개인용 슈퍼 컴퓨터 현황과 전망," 전자공학회지, 36권 제5호, 2009.
  2. NVIDIA, CUDA Programming Guide, NVIDIA Corporation, 2009.
  3. NVIDIA, CUDA C Programming Best Practices Guide, NVIDIA Corporation, 2009.
  4. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA, 1989.
  5. O. Maitre, L. A. Baumes, N. Lachiche, A. Corma, and P. Collet, "Coarse grain parallelization of evolutionary algorithms on GPU cards with EASEA," Proc. of the Genetic and Evolutionary Computation Conference, GECCO-2009, Montreal Quebec, Canada, pp. 1403-1410, Jul. 2009.
  6. O. Maitre, N. Lachiche, P. Clauss, L. Baumes, A. Corma, and P. Collet, "Efficient parallel implementation of evolutionary algorithms on GPU cards," Proc. of the 15th International Euro-Par Conference, Delf, Netherlands, 5704, pp. 974-985, Aug. 2009.
  7. Q. Yu, C. Chen, and Z. Pan, "Parallel genetic algorithms on programmable graphics hardware," Proc. of the First International Conference, ICNC 2005, Changsha, China, vol. 3612, pp. 1051-1059, Aug. 2005.
  8. J. M. Li, X. J. Wang, R. S. He, and Z. X. Chi, "An efficient fine-grained parallel genetic algorithm based on GPU-accelerated," Proc. of the IFIP International Conference, Network and Parallel Computing Workshops-2007, pp. 855-862, Sep. 2007.
  9. A. Munawar, M. Wahib, M. Munetomo, and K. Akama, "Hybrid of genetic algorithm and local search to solve MAX-SAT problem using nVidia CUDA framework," Genetic Programming and Evolable Machines, vol. 10, no. 4, Dec. 2009.
  10. V. Podlozhnyuk, Parallel Mersenne Twister, CUDA SDK Documentation, 2007.
  11. M. Matsumoto and T. Nishimura, "Mersenne twister: A 623-dimensionally equidistributed uniform pseudo-random number generator," ACM Trans. Model. Comput. Simul, vol. 8, no. 1, pp. 3-30, Jan. 1998. https://doi.org/10.1145/272991.272995
  12. 현병용, 권오성, 현수환, 서기성, "유전 알고리즘의 GPU 구현," 한국지능시스템학회 학술발표 논문집, 마산, 한국, vol. 20, no. 1, pp. 3-6, Apr. 2010.
  13. 현병용, 김영균, 서기성, "전체 유전알고리즘의 GPGPU 구현 및 문제크기 변화에 따른 수행시간 비교," ICROS 학술대회, 춘천, 한국, pp. 436-438, May 2010.

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