한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

한국컴퓨터정보학회 (Korean Society of Computer Information)
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고속의 클러스터 추정을 위한 매니코어 프로세서의 디자인 공간 탐색

Design Space Exploration of Many-Core Processor for High-Speed Cluster Estimation

  • 서준상 (울산대학교 전기공학부) ;
  • 김철홍 (전남대학교 전자컴퓨터공학부) ;
  • 김종면 (울산대학교 전기공학부)
  • Seo, Jun-Sang (School of Electrical Engineering, University of Ulsan) ;
  • Kim, Cheol-Hong (School of Electronics and Computer Engineering, Chonnam National University) ;
  • Kim, Jong-Myon (School of Electrical Engineering, University of Ulsan)
  • 투고 : 2014.06.10
  • 심사 : 2014.09.17
  • 발행 : 2014.10.31
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초록

본 논문에서는 단일 명령어, 다중 데이터 처리 기반의 매니코어 프로세서를 이용하여 높은 계산량이 요구되는 차감 클러스터링 알고리즘을 병렬 구현하고 성능을 향상시킨다. 또한 차감 클러스터링 알고리즘을 위한 최적의 매니코어 프로서서 구조를 선택하기 위해 다섯 가지의 프로세싱 엘리먼트 (processing element, PE) 구조 (PEs=16, 64, 256, 1,024, 4,096)를 모델링하고, 각 PE구조에 대해 실행시간 및 에너지 효율을 측정한다. 두 가지 의료 영상 및 각 영상의 세 가지 해상도(($128{\times}128$, $256{\times}256$, $512{\times}512$)를 이용하여 모의 실험한 결과, 모든 경우에 대해 PEs=4,096구조에서 최고의 성능 및 에너지 효율을 보였다.

This paper implements and improves the performance of high computational subtractive clustering algorithm using a single instruction, multiple data (SIMD) based many-core processor. In addition, this paper implements five different processing element (PE) architectures (PEs=16, 64, 256, 1,024, 4,096) to select an optimal PE architecture for the subtractive clustering algorithm by estimating execution time and energy efficiency. Experimental results using two different medical images and three different resolutions ($128{\times}128$, $256{\times}256$, $512{\times}512$) show that PEs=4,096 achieves the highest performance and energy efficiency for all the cases.

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참고문헌

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