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Performance Comparison of Parallel Programming Frameworks in Digital Image Transformation KCI 등재후보
국제인공지능학회(구 한국인터넷방송통신학회) International Journal of Internet, Broadcasting and Communication Vol.11 No.3 2019.08 pp.1-7
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
Previously, parallel computing was mainly used in areas requiring high computing performance, but nowadays, multicore CPUs and GPUs have become widespread, and parallel programming advantages can be obtained even in a PC environment. Various parallel programming frameworks using multicore CPUs such as OpenMP and PPL have been announced. Nvidia and AMD have developed parallel programming platforms and APIs for program developers to take advantage of multicore GPUs on their graphics cards. In this paper, we develop digital image transformation programs that runs on each of the major parallel programming frameworks, and measure the execution time. We analyze the characteristics of each framework through the execution time comparison. Also a constant K indicating the ratio of program execution time between different parallel computing environments is presented. Using this, it is possible to predict rough execution time without implementing a parallel program.
Performance Comparison of Parallel Programming Frameworks in Digital Image Transformation KCI 등재후보
국제인공지능학회(구 한국인터넷방송통신학회) International Journal of Internet, Broadcasting and Communication Vol.11 No.3 2019.08 pp.1-7
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
Previously, parallel computing was mainly used in areas requiring high computing performance, but nowadays, multicore CPUs and GPUs have become widespread, and parallel programming advantages can be obtained even in a PC environment. Various parallel programming frameworks using multicore CPUs such as OpenMP and PPL have been announced. Nvidia and AMD have developed parallel programming platforms and APIs for program developers to take advantage of multicore GPUs on their graphics cards. In this paper, we develop digital image transformation programs that runs on each of the major parallel programming frameworks, and measure the execution time. We analyze the characteristics of each framework through the execution time comparison. Also a constant K indicating the ratio of program execution time between different parallel computing environments is presented. Using this, it is possible to predict rough execution time without implementing a parallel program.
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