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

被引:0
|
作者
Newall, Matthew [1 ]
Holmes, Violeta [1 ]
Lunn, Paul [2 ]
机构
[1] Univ Huddersfield, Sch Comp & Engn, Huddersfield HD1 3DH, W Yorkshire, England
[2] Birmingham City Univ, Sch Digital Media Technol, Birmingham, W Midlands, England
关键词
GPU; CUDA; GPU Cluster; Visualisation;
D O I
暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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.
引用
收藏
页码:140 / 145
页数:6
相关论文
共 50 条
  • [21] Cluster-based visualisation of marketing data
    Lisboa, PJG
    Patel, S
    INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING IDEAL 2004, PROCEEDINGS, 2004, 3177 : 552 - 558
  • [22] CLIJ: GPU-accelerated image processing for everyone
    Haase, Robert
    Royer, Loic A.
    Steinbach, Peter
    Schmidt, Deborah
    Dibrov, Alexandr
    Schmidt, Uwe
    Weigert, Martin
    Maghelli, Nicola
    Tomancak, Pavel
    Jug, Florian
    Myers, Eugene W.
    NATURE METHODS, 2020, 17 (01) : 5 - 6
  • [23] A GPU-accelerated Framework for Processing Trajectory Queries
    Zhang, Bowen
    Shen, Yanyan
    Zhu, Yanmin
    Yu, Jiadi
    2018 IEEE 34TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE), 2018, : 1037 - 1048
  • [24] CLIJ: GPU-accelerated image processing for everyone
    Robert Haase
    Loic A. Royer
    Peter Steinbach
    Deborah Schmidt
    Alexandr Dibrov
    Uwe Schmidt
    Martin Weigert
    Nicola Maghelli
    Pavel Tomancak
    Florian Jug
    Eugene W. Myers
    Nature Methods, 2020, 17 : 5 - 6
  • [25] Parallel processing for accelerated mean shift algorithm with GPU
    Chen, Jia
    Wu, Xiaojun
    Cai, Rong
    Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics, 2010, 22 (03): : 461 - 466
  • [26] Tensor voting accelerated by graphics processing units (GPU)
    Min, Changki
    Medioni, Gerard
    18TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 3, PROCEEDINGS, 2006, : 1103 - +
  • [27] A Taste of Scientific Computing on the GPU-Accelerated Edge Device
    Kang, Pilsung
    Lim, Sungmin
    IEEE ACCESS, 2020, 8 (08): : 208337 - 208347
  • [28] Implementing Scientific Simulations on GPU-accelerated Edge Devices
    Lim, Sungmin
    Kang, Pilsung
    2020 34TH INTERNATIONAL CONFERENCE ON INFORMATION NETWORKING (ICOIN 2020), 2020, : 756 - 760
  • [29] Accelerated 3-D Monte Carlo light dosimetry using a graphics processing unit (GPU) cluster
    Lo, William Chun Yip
    Lilge, Lothar
    LASER APPLICATIONS IN LIFE SCIENCES, 2010, 7376
  • [30] GPU Accelerated Marine Data Visualization Method
    LI Bo
    CHEN Ge
    TIAN Fenglin
    SHAO Baomin
    JI Pengbo
    JournalofOceanUniversityofChina, 2014, 13 (06) : 964 - 970