Abstract
This paper deals with GPU computing of special mathematical functions that are used in Fractional Calculus. The graphics processing unit (GPU) has grown to be an integral part of nowadays’s mainstream computing structures. The special mathematical functions are an integral part of Fractional Calculus. This paper deals with a novel parallel approach for computing special mathematical functions used in Fractional Calculus. NVIDIA’s GPU hardware is used to speed up the parallel algorithm. A comparison of the sequential code, vectorized code and GPU code is performed. We have successfully reduced the computation time of special mathematical functions using the parallel computing capabilities of GPU.
Highlights
Fractional calculus (FC) is widely used in the field of engineering because of its ability to model real-world systems with more precision [1]
In recent years the use of this technology has grown exponentially these days as a growing community of researchers has come together to revitalize the field with the help of advanced computing technology [3]. This leads to certain differential equations whose solutions are given by Special Mathematical Function
The general purpose use of GPUs for computing is commonly known as GPGPU (General Purpose Computing on Graphics Processing Unit) [3]
Summary
Fractional calculus (FC) is widely used in the field of engineering because of its ability to model real-world systems with more precision [1]. In recent years the use of this technology has grown exponentially these days as a growing community of researchers has come together to revitalize the field with the help of advanced computing technology [3] In most cases, this leads to certain differential equations whose solutions are given by Special Mathematical Function. The general purpose use of GPUs for computing is commonly known as GPGPU (General Purpose Computing on Graphics Processing Unit) [3]. The Graphics Processing Unit (GPU) is a many-core device used for parallel computing applications. The balance has shifted in the GPU, where more arithmetic and logic units are needed It is designed for compute-intensive, highly parallel computations and is specialized for them. Lithography No of Cores No.of Threads Processor Based Frequency Maz Turbo Frequency Cache
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