• DocumentCode
    265932
  • Title

    GPU cluster for accelerated processing and visualisation of scientific and engineering data

  • Author

    Newall, Matthew ; Holmes, Violeta ; Lunn, Paul

  • Author_Institution
    Sch. of Comput. & Eng., Univ. of Huddersfield, Huddersfield, UK
  • fYear
    2014
  • fDate
    27-29 Aug. 2014
  • Firstpage
    140
  • Lastpage
    145
  • 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 ongoing 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
    data visualisation; graphics processing units; CPU system; GPU architectures; GPU cluster; accelerated processing; accelerated visualisation; data visualisation; engineering data; graphical processing units; higher education institutions; scientific data; scientific research; video game industry; Acceleration; Data visualization; Educational institutions; Graphics processing units; Hardware; CUDA; GPU; GPU Cluster; Visualisation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Science and Information Conference (SAI), 2014
  • Conference_Location
    London
  • Print_ISBN
    978-0-9893-1933-1
  • Type

    conf

  • DOI
    10.1109/SAI.2014.6918182
  • Filename
    6918182