In this paper a hybrid implementation for Bellman-Ford to solve shortest path problems is proposed using OpenCL. Here first parallel implementation for Bellman-Ford for single source shortest path (SSSP) problem and all pair shortest path (APSP) are analyzed on CPU and GPU and based on this analysis work is divided among CPU and GPU and hybrid implementation is done. As proper resource utilizat...

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 ...

In this paper, a comprehensive survey on various applications of Fuzzy Logic system has been carried out. GPU (Graphics Processing Units) is used for speed up purpose, otherwise commonly used for graphics applications. However, recent trends show use of GPU in various other general computational applications to run them parallel to reduce overall execution time. In this paper, it is discussed t...

Graphics Processing Units in today’s desktops can well be thought of as a high performance parallel processor. Each single processor within the GPU is able to execute different tasks independently but concurrently. Such computational capabilities of the GPU are being exploited in the domain of Data mining. Two types of Hierarchical clustering algorithms are realized on GPU using CUDA. Speed gai...

Over the past few years, as part of the Oak Ridge Leadership Class Facility project (OLCF-3), Oak Ridge National Laboratory (ORNL) has been engaged with several third party tools vendors with the aim of enhancing the tool offerings for ORNLs GPU-based platform, Titan. This effort has resulted in enhancements to CAPS’ HMPP compiler, Allinea’s DDT debugger, and the Vampir suite of performance ana...

Traffic simulation is a growing domain of computational physics. Many life and industrial applications would benefit from traffic simulation to establish reliable transportation systems. A core challenge of this science research, however, is its unbounded scale of computation. This paper explores an advantage of using the graphics processing unit (GPU) for this computational challenge. We study...

Recently, there have been tremendous interests in the acceleration of general computing applications using a Graphics Processing Unit (GPU). Now the GPU provides the computing powers not only for fast processing of graphics applications, but also for general computationally complex data intensive applications. On the other hand, power and energy consumptions are also becoming important design c...

SaP::GPU is a solver developed in the Simulation Based Engineering Lab (SBEL) [1] to solve large banded and sparse linear systems on the GPU. This report contributes the performance comparison of the banded solver of SaP::GPU and Intel’s Math Kernel Library [2] on a large set of synthetic problems. The results of several numerical experiments indicate that when it is used in conjunction with la...

This work describes an implementation of canonical and Gibbs ensemble Monte Carlo simulations on graphics processing units (GPUs). The pair-wise energy calculations, which consume the majority of the computational effort, are parallelized using the energetic decomposition algorithm. While energetic decomposition is relatively inefficient for traditional CPU-bound codes, the algorithm is ideally...

The Graphics Processor Unit (GPU) has expanded its role from an accelerator for rendering graphics into an efficient parallel processor for general purpose computing. The GPU, an indispensable component in desktop and server-class computers as well as game consoles, has also become an integrated component in handheld devices, such as smartphones. Since the handheld devices are mostly powered by...