• DocumentCode
    1310711
  • Title

    Dynamic Multicore Resource Management: A Machine Learning Approach

  • Author

    Martínez, José F. ; Ipek, Engin

  • Author_Institution
    Electr. & Comput. Eng., Cornell Univ., Ithaca, NY, USA
  • Volume
    29
  • Issue
    5
  • fYear
    2009
  • Firstpage
    8
  • Lastpage
    17
  • Abstract
    A machine learning approach to multicore resource management produces self-optimizing on-chip hardware agents capable of learning, planning, and continuously adapting to changing workload demands. Machine learning is the study of computer programs and algorithms that learn about their environment and improve automatically with experience.This approach thus contrasts with today´s predominant approach of directly specifying at design time how the hardware should accomplish the desired goal. This results in more efficient and flexible management of critical hardware resources at runtime.
  • Keywords
    learning (artificial intelligence); microprocessor chips; multiprocessing systems; planning (artificial intelligence); computer algorithm; computer program; critical hardware resource; dynamic multicore resource management; flexible management; machine learning approach; planning approach; self-optimizing on-chip hardware agent; Algorithm design and analysis; Hardware; Machine learning; Machine learning algorithms; Multicore processing; Resource management; Runtime; dynamic resource management; machine learning.; multicore;
  • fLanguage
    English
  • Journal_Title
    Micro, IEEE
  • Publisher
    ieee
  • ISSN
    0272-1732
  • Type

    jour

  • DOI
    10.1109/MM.2009.77
  • Filename
    5325152