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Comparative study of the implementation of the Lagrange interpolation algorithm on GPU and CPU using CUDA to compute the density of a material at different temperatures

SHS Web of Conferences, 2021, Vol.119, p.7002 [Peer Reviewed Journal]

2021. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2261-2424 ;ISSN: 2416-5182 ;EISSN: 2261-2424 ;DOI: 10.1051/shsconf/202111907002

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  • Title:
    Comparative study of the implementation of the Lagrange interpolation algorithm on GPU and CPU using CUDA to compute the density of a material at different temperatures
  • Author: Rtal, Youness ; Hadjoudja, Abdelkader
  • Moumen, A. ; Slimani, H. ; Mejjad, N.
  • Subjects: Microprocessors
  • Is Part Of: SHS Web of Conferences, 2021, Vol.119, p.7002
  • Description: Graphics Processing Units (GPUs) are microprocessors attached to graphics cards, which are dedicated to the operation of displaying and manipulating graphics data. Currently, such graphics cards (GPUs) occupy all modern graphics cards. In a few years, these microprocessors have become potent tools for massively parallel computing. Such processors are practical instruments that serve in developing several fields like image processing, video and audio encoding and decoding, the resolution of a physical system with one or more unknowns. Their advantages: faster processing and consumption of less energy than the power of the central processing unit (CPU). In this paper, we will define and implement the Lagrange polynomial interpolation method on GPU and CPU to calculate the sodium density at different temperatures Ti using the NVIDIA CUDA C parallel programming model. It can increase computational performance by harnessing the power of the GPU. The objective of this study is to compare the performance of the implementation of the Lagrange interpolation method on CPU and GPU processors and to deduce the efficiency of the use of GPUs for parallel computing.
  • Publisher: Les Ulis: EDP Sciences
  • Language: English
  • Identifier: ISSN: 2261-2424
    ISSN: 2416-5182
    EISSN: 2261-2424
    DOI: 10.1051/shsconf/202111907002
  • Source: Directory of Open Access Journals May need to register for free articles
    EDP Open
    Coronavirus Research Database
    ROAD: Directory of Open Access Scholarly Resources
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