• DocumentCode
    1466667
  • Title

    EcoG: A Power-Efficient GPU Cluster Architecture for Scientific Computing

  • Author

    Showerman, Michael ; Enos, Jeremy ; Steffen, Craig ; Treichler, Sean ; Gropp, William ; Hwu, Wen-Mei W.

  • Author_Institution
    Stanford Univ., Stanford, CA, USA
  • Volume
    13
  • Issue
    2
  • fYear
    2011
  • Firstpage
    83
  • Lastpage
    87
  • Abstract
    Researchers built the EcoG GPU-based cluster to show that a system can be designed around GPU computing and still be power efficient.
  • Keywords
    computer graphic equipment; coprocessors; pattern clustering; power aware computing; EcoG; power efficient GPU cluster architecture; scientific computing; Computer architecture; Computer science; Graphics processing unit; Green products; Hardware; Power measurement; Scientific computing; CUDA; GPUs; Graphics processing; Nvidia; scientific computing;
  • fLanguage
    English
  • Journal_Title
    Computing in Science & Engineering
  • Publisher
    ieee
  • ISSN
    1521-9615
  • Type

    jour

  • DOI
    10.1109/MCSE.2011.30
  • Filename
    5725240
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1466667