IoT 23(4): e4

Research Article

Implementation of Beeman's algorithm to calculate execution time on GPU using CUDA

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  • @ARTICLE{10.4108/eetiot.v8i4.2937,
        author={Youness Rtal and Abdelkader Hadjoudja},
        title={Implementation of Beeman's algorithm to calculate execution time on GPU using CUDA},
        journal={EAI Endorsed Transactions on Internet of Things},
        volume={8},
        number={4},
        publisher={EAI},
        journal_a={IOT},
        year={2022},
        month={12},
        keywords={Beeman algorithm, GPU, CPU, Thread, Bloc, Grille, CUDA C/C},
        doi={10.4108/eetiot.v8i4.2937}
    }
    
  • Youness Rtal
    Abdelkader Hadjoudja
    Year: 2022
    Implementation of Beeman's algorithm to calculate execution time on GPU using CUDA
    IOT
    EAI
    DOI: 10.4108/eetiot.v8i4.2937
Youness Rtal1,*, Abdelkader Hadjoudja1
  • 1: Universit√© Ibn-Tofail
*Contact email: youness.pc4@gmail.com

Abstract

Graphics processing units (GPUs) are microprocessors designed to the operation of display and manipulation of graphics data. . Currently, these graphics processor are found on all graphics hardware and have become very important instruments for parallel computing. GPUs are practical tools for the development of several fields like decoding and encoding, solving differential equations. Their advantages are increase in performance, faster data processing and reduced power consumption. It is simple to program a GPU with CUDA C to run parallel calculations. But it is necessary to have an understanding of the architectural aspects of the GPU and CUDA C. This paper, we will describe and implement Beeman's algorithm on GPU and CPU using CUDA C to solve the differential equation of charged particles in an electromagnetic field. Our goal is to evaluate the performances of the implementation on GPU and CPU processors and to deduce the efficiency of the use of GPUs.