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Method of Multi Thread Management based on Shader Instruction for Mobile GPGPU

GPGPU를 위한 쉐이더 명령어기반 멀티 스레드 관리 기법

  • Received : 2012.08.27
  • Accepted : 2012.11.06
  • Published : 2012.12.31

Abstract

This thesis is intended to design multi thread mobile GPGPU optimized in mobile environment, and to verify an effective thread management method of the multi thread mobile processor. In thread management, there is no management hardware and implement with software instructions. For the verification of the multi thread management method, Lane detection algorithm was implemented to compare nVidia's CUDA Architecture and the designed GPGPU in terms of thread management efficiency. The number of thread is normalized to 48 threads. An implemented Land Detection Algorithm is composed of Gaussian filter algorithm and Sobel Edge Detection algorithm. As a result, the designed GPGPU's thread efficiency is up to 2 times higher than CUDA's thread efficiency.

본 논문에서는 모바일환경에 최적화 된 멀티 스레드 모바일 GPGPU를 설계하고, 멀티 스레드로 구성된 모바일 프로세서에서의 효과적인 스레드 관리 기법을 검증한다. 스레드의 제어에는 별도의 하드웨어 없이 소프트웨어 명령어를 기반으로 설계하였다. 스레드 관리 기법의 검증은 차선 검출 알고리즘을 구현하여 nVidia의 CUDA Architecture와 설계한 GPGPU의 스레드 관리 효율을 비교 분석한다. 스레드 효율에서는 CUDA와 비교했을 때 최대 2배까지 높은 효율을 보인다.

Keywords

References

  1. HyungKi Jeong, "A Design of a Multi-Threaded & Multi-Core GP-GPU using a dynamic Thread management techniques", The Graduate School of Seokyeong Universiry, August 2010.
  2. NVIDIA, "NVIDIA CUDA C Programming Guide", November 2011.
  3. NVIDIA, "CUDA API REFERENCE MANUAL", January 2012.
  4. Hongfei Yu, Wei Liu, Jianghua Pu, Bobo Duan, Huai Yuan, Hong Zhao, "Lane recognition based on location of raised pavement markers", Intelligent Vehicles Symposium (IV), July 2011.
  5. Shih-Shinh Huang, Chung-Jen Chen, Pei-Yung Hsiao, and Li-Chen Fu, "On-Board Vision System for Lane Recognition and Front-Vehicle Detection to Enhance Driver's Awareness", IEEE Intermtbnal confennu on Robdm a Automlltlon, April 2004.

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