Genetic Improvement (GI) is shown to optimise, in some cases by more than 35%, a critical component of health-care industry software across a diverse range of six nVidia graphics processing units (GPUs). GP and other search based software engineering techniques can automatically op

Abstract. This paper deals with the mapping of the parallel island-based genetic algorithm with unidirectional ring migrations to nVidia CUDA software model. The proposed mapping is tested using Rosen-brock’s, Griewank’s and Michalewicz’s benchmark functions. The ob-tained results indicate that our

. This paper characterizes several non-graphics applications written in NVIDIA’s CUDA programming model by running them on a novel detailed microarchitecture performance simulator that runs NVIDIA’s parallel thread execution (PTX) virtual instruction set. For this study, we selected twelve non-trivial CUDA

Abstract. Genetic Programming (GP) may dramatically increase the performance of software written by domain experts. GP and autotuning are used to optimise and refactor legacy GPGPU C code for modern parallel graphics hardware and software. Speed ups of more than six times on recent nVidia GPU cards

role in iterative methods for solving sparse linear systems and eigenvalue problems, sparse matrix-vector multiplication (SpMV) is of singular importance in sparse linear algebra. In this paper we discuss data structures and algorithms for SpMV that are efficiently implemented on the CUDA platform

warranty for the use of its products and assumes no responsibility for any errors that may appear, or for damages resulting from the use of the information contained herein. The Portland Group retains the right to make changes to this information at any time, without notice. The software described

warranty for the use of its products and assumes no responsibility for any errors that may appear, or for damages resulting from the use of the information contained herein. The Portland Group retains the right to make changes to this information at any time, without notice. The software described

warranty for the use of its products and assumes no responsibility for any errors that may appear, or for damages resulting from the use of the information contained herein. The Portland Group retains the right to make changes to this information at any time, without notice. The software described

. The software described in this document is distributed under license from STMicroelectronics and/or The Portland Group and may be used or copied only in accordance with the terms of the license agreement ("EULA").

Recent increases in the programmability and performance of GPUs have led to a surge of interest in utilizing them for general-purpose computations. Tools such as NVIDIA’s Cuda allow programmers to use a C-like language to code algorithms for execution on the GPU. Unfortunately, parallel programs