IoT 21(24): e3

Research Article

A Comparative Study of the Implementation of SJF and SRT Algorithms on the GPU Processor Using CUDA

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  • @ARTICLE{10.4108/eai.8-2-2021.168689,
        author={Youness Rtal and Abdelkader Hadjoudja},
        title={A Comparative Study of the Implementation of SJF and SRT Algorithms on the GPU Processor Using CUDA},
        journal={EAI Endorsed Transactions on Internet of Things},
        volume={6},
        number={24},
        publisher={EAI},
        journal_a={IOT},
        year={2021},
        month={2},
        keywords={CUDA, GPU, CPU, SRT, SJF, thread},
        doi={10.4108/eai.8-2-2021.168689}
    }
    
  • Youness Rtal
    Abdelkader Hadjoudja
    Year: 2021
    A Comparative Study of the Implementation of SJF and SRT Algorithms on the GPU Processor Using CUDA
    IOT
    EAI
    DOI: 10.4108/eai.8-2-2021.168689
Youness Rtal1,*, Abdelkader Hadjoudja1
  • 1: Department of Physics, Laboratory of Electronic Systems, Information Processing, Mechanics and Energy, Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco
*Contact email: youness.pc4@gmail.com

Abstract

GPU (Graphical Processing Units) have become in a few years very powerful tools for parallel computing. They are currently used in several fields such as image processing, bioinformatics, medical applications and numerical computation...etc. Their advantages are faster processing and lower power consumption compared to CPU power. It is simple to program a GPU processor using the CUDA C language to perform tasks that are typically computed in parallel. But you need to understand the different architectural aspects of the GPU. In this paper, we will define and implement the two operating system algorithms the SJF (Shortest Job First) algorithm and the SRT (Shortest Remaining Time) algorithm in a single-wire CPU environment using the C language, and then the same algorithms will be implemented on the GPU using the CUDA C language, in order to compare the different performances of the implementation of the two algorithms on GPU and CPU processors and to verify the efficiency of this study.