Abstract
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.
Highlights
The primary purpose of interpolation is to interpolate known data from discrete points
It is noticeable that when the temperature increases, the Ri density decreases, and the execution time on the central processing unit (CPU) is greater than the execution time on the Graphics Processing Units (GPUs)
The GPU processes the data in parallel, which implies the efficiency of the GPU processors for parallel computing
Summary
The primary purpose of interpolation is to interpolate known data from discrete points In this case, we can estimate the value of the function between these points. That is why we need processors that treat these physical problems in a very efficient way and minimizes the time of execution CUDA, as a high-level language, has changed the whole perspective of GPU programming It has reinforced interest in accelerating tasks usually performed by general-purpose processors GPUs. It has reinforced interest in accelerating tasks usually performed by general-purpose processors GPUs Despite these languages, it is not easy to exploit these complex architectures efficiently. Each generation brings its share of new features dedicated to high-performance computing acceleration The details of these architectures remain secret because of the manufacturers' reluctance to disclose their implementations. The evolution of the GPU over the years requires a very specific programming language such as CUDA C to improve computing performance
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
