SHS Web Conf.
Volume 119, 20213rd International Conference on Quantitative and Qualitative Methods for Social Sciences (QQR’21)
|Number of page(s)||7|
|Section||Technology and Society / Covid-19 Innovations|
|Published online||24 August 2021|
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
University of Ibn Tofail, Faculty of Sciences, Department of Physics, Laboratory of Electronic Systems, Information Processing, Mechanics and Energy, B.P 242 Kenitra, Morocco
* Corresponding author: firstname.lastname@example.org
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.
© The Authors, published by EDP Sciences, 2021
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