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
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 f...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2021-01-01
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| Series: | SHS Web of Conferences |
| Online Access: | https://www.shs-conferences.org/articles/shsconf/pdf/2021/30/shsconf_qqr2021_07002.pdf |
| _version_ | 1819135439437561856 |
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| author | Rtal Youness Hadjoudja Abdelkader |
| author_facet | Rtal Youness Hadjoudja Abdelkader |
| author_sort | Rtal Youness |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-22T10:19:06Z |
| format | Article |
| id | doaj.art-4f3921d6b7a74252b5f19ba2bd72473d |
| institution | Directory Open Access Journal |
| issn | 2261-2424 |
| language | English |
| last_indexed | 2024-12-22T10:19:06Z |
| publishDate | 2021-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | SHS Web of Conferences |
| spelling | doaj.art-4f3921d6b7a74252b5f19ba2bd72473d2022-12-21T18:29:39ZengEDP SciencesSHS Web of Conferences2261-24242021-01-011190700210.1051/shsconf/202111907002shsconf_qqr2021_07002Comparative study of the implementation of the Lagrange interpolation algorithm on GPU and CPU using CUDA to compute the density of a material at different temperaturesRtal Youness0Hadjoudja Abdelkader1University of Ibn Tofail, Faculty of Sciences, Department of Physics, Laboratory of Electronic Systems, Information Processing, Mechanics and EnergyUniversity of Ibn Tofail, Faculty of Sciences, Department of Physics, Laboratory of Electronic Systems, Information Processing, Mechanics and EnergyGraphics 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.https://www.shs-conferences.org/articles/shsconf/pdf/2021/30/shsconf_qqr2021_07002.pdf |
| spellingShingle | Rtal Youness Hadjoudja Abdelkader 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 |
| 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| url | https://www.shs-conferences.org/articles/shsconf/pdf/2021/30/shsconf_qqr2021_07002.pdf |
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