• DocumentCode
    2189827
  • Title

    Memory dependence prediction using store sets

  • Author

    Chrysos, George Z. ; Emer, Joel S.

  • Author_Institution
    Digital Equip. Corp., Hudson, MA, USA
  • fYear
    1998
  • fDate
    27 Jun-1 Jul 1998
  • Firstpage
    142
  • Lastpage
    153
  • Abstract
    For maximum performance, an out-of-order processor must issue load instructions as early as possible, while avoiding memory-order violations with prior store instructions that write to the same memory location. One approach is to use memory dependence prediction to identify the stores upon which a load depends, and communicate that information to the instruction scheduler. We designate the set of stores upon which each load has depended as the load´s “store set”. The processor can discover and use a load´s store set to accurately predict the earliest time the load can safely execute. We show that store sets accurately predict memory dependencies in the context of large instruction window, superscalar machines, and allow for near-optimal performance compared to an instruction scheduler with perfect knowledge of memory dependencies. In addition, we explore the implementation aspects of store sets, and describe a low cost implementation that achieves nearly optimal performance
  • Keywords
    computer architecture; performance evaluation; instruction scheduler; load instructions; maximum performance; memory dependence prediction; memory-order violations; out-of-order processor; store sets; superscalar machines; Computer aided instruction; Cost function; Decoding; Ear; Electrical capacitance tomography; Identity-based encryption; Monitoring; Parallel processing; Read-write memory; Registers;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Architecture, 1998. Proceedings. The 25th Annual International Symposium on
  • Conference_Location
    Barcelona
  • ISSN
    1063-6897
  • Print_ISBN
    0-8186-8491-7
  • Type

    conf

  • DOI
    10.1109/ISCA.1998.694770
  • Filename
    694770