Search:
Computing and Library Services - delivering an inspiring information environment

GPU Cluster for Accelerating Processing and Visualisation of Scientific and Engineering Data

Newall, Matthew, Holmes, Violeta and Lunn, Paul (2014) GPU Cluster for Accelerating Processing and Visualisation of Scientific and Engineering Data. In: Proceedings of the Science and Information Conference. SAI 2014 . IEEE, London, UK, pp. 140-145. ISBN 978-0-9893-1933-1

[img] PDF
Restricted to Registered users only

Download (953kB)

Abstract

The ability to process, visualise, and work with large volumes of data in a way that is fast, meaningful, and accurate is an essential part of many fields of scientific research today. The success of video game industry has resulted in on-going developments in the complexity of Graphical Processing Units (GPU), as well as rapidly falling cost per core. Their characteristics make them excellently suited to any task exhibiting a high level of data parallelism. Recent development of GPU architectures is aimed at HPC systems and applications. In this paper we are presenting our experience in designing and deploying a small dedicated GPU based cluster for processing and visualising data generated by engineering and scientific application. This GPU cluster is helping our researchers to analyse complex data using visualisation, and to accelerate large data processing. We have shown that our GPU cluster solution can achieve five to ten times speed up compared to the CPU system. As a result of our work we can demonstrate that even a small GPU cluster can benefit Higher Education institutions.
Keywords—GPU, CUDA, GPU Cluster, Visualisation

Item Type: Book Chapter
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Schools: School of Computing and Engineering
School of Computing and Engineering > High-Performance Intelligent Computing > High Performance Computing Research Group
School of Computing and Engineering > Systems Engineering Research Group
Related URLs:
Depositing User: Violeta Holmes
Date Deposited: 07 Oct 2014 14:14
Last Modified: 04 Dec 2016 10:42
URI: http://eprints.hud.ac.uk/id/eprint/21907

Downloads

Downloads per month over past year

Repository Staff Only: item control page

View Item View Item

University of Huddersfield, Queensgate, Huddersfield, HD1 3DH Copyright and Disclaimer All rights reserved ©