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 nowadays’s mainstream structures. a novel parallel approach for NVIDIA’s hardware is speed up the algorithm. A comparison sequential code, vectorized code and performed. We have successfully reduced computation time using...

Paper Abstract: The recent advances in GPU hardware have resulted in the wide availability of programmable commodity hardware suited to massively parallel computation. This paper examines the application of the evolutionary computation approach of Genetic Programming to explore the space of general purpose GPU programs.

Biological computations like electrocardiological modelling and simulation usually require high-performance computing environments. This paper introduces an implementation of parallel computation for computer simulation of electrocardiograms (ECGs) in a personal computer environment with an Intel CPU of Core (TM) 2 Quad Q6600 and a GPU of Geforce 8800GT, with software support by OpenMP and CUDA...

Mingzhe Wang, Bo Wang, Qiu He, Xiuxiu Liu, Kunshuai Zhu (School of Computer and Control Engineering, University of Chinese Academy of Sciences, Huairou, Beijing 101408, China) Abstract: Matlab is very widely used in scientific computing, but Matlab computational efficiency is lower than C language program. In order to improve the computing speed, some toolbox can use GPU to accelerate the compu...

A novel efficient parallel algorithm for the near-field computation in N-body problem on the Graphics Processing Unit (GPU) architecture is proposed in this paper. This algorithm evolved from the BPB algorithm [1] which is proposed in the author’s previous work. This novel algorithm is based on the Newton’s third law and Z-order Space Filling Curve (Z-SFC). Half of the computations are reduced ...

Many research works have recently experimented with GPU to accelerate packet processing in network applications. Most works have shown that GPU brings a significant performance boost when it is compared to the CPUonly approach, thanks to its highly-parallel computation capacity and large memory bandwidth. However, a recent work argues that for many applications, the key enabler for high perform...

This paper presents some new implementations of parallel Hopfield neural network model -Cauchy machineon Graphic Processor Units (GPU). The main operators in the parallel Hopfield neural work are loaded into RGBA textures so that input calculation, output update and terminal condition check are implemented by fragment processors on GPU. In addition, the matrix-vector multiplication is realized ...

Since the physical constraints on micro computing devices have forced the researchers to design next generation chips, the significance of the parallelization and distributed computing grow in importance. In this study, a sequential implementation of the Particle Swarm Optimization algorithm is converted into a concurrent version, which is executed on the cores of both CPU and GPU. For this rea...

Scan (or prefix sum) is a fundamental and widely used primitive in parallel computing. In this paper, we present LightScan, a faster parallel scan primitive for CUDA-enabled GPUs, which investigates a hybrid model combining intrablock computation and inter-block communication to perform a scan. Our algorithm employs warp shuffle functions to implement fast intra-block computation and takes adva...

In this paper, we explore the limits of graphics processors (GPUs) for general purpose parallel computing by studying problems that require highly irregular data access patterns: parallel graph algorithms for list ranking and connected components. Such graph problems represent a worst case scenario for coalescing parallel memory accesses on GPUs which is critical for good GPU performance. Our e...