• DocumentCode
    2615420
  • Title

    Combining cluster sampling with single pass methods for efficient sampling regimen design

  • Author

    Bryan, Paul D. ; Conte, Thomas M.

  • Author_Institution
    Center for Efficient, North Carolina State Univ., Raleigh, NC
  • fYear
    2007
  • fDate
    7-10 Oct. 2007
  • Firstpage
    472
  • Lastpage
    479
  • Abstract
    Microarchitectural simulation is orders of magnitude slower than native execution. As more elements are accurately modeled, problems associated with slow simulation are further exacerbated. Given these issues, many researchers have devised sampling techniques to reduce simulation time. When cluster sampling techniques are used, care must be taken to remove sampling and non-sampling biases. Researchers have devised clever methods for effectively reducing non-sampling bias, but little work has been proposed for efficient reduction of sampling bias (sampling regimen design). Traditionally, sampling regimen design has been an iterative process that required a full workload simulation for error comparison. In this study, a single-pass simulation technique for sampling regimen design is proposed. Using this method, thousands of sampling regimen candidates can be simultaneously evaluated. With this technique, simulation speed was increased by an average factor of 17 with a maximum increase of 73 times relative to the total workload simulation. Additionally, this technique allows the user to effectively estimate the sample error without running the entire workload.
  • Keywords
    memory architecture; microprocessor chips; cluster sampling; sampling regimen design; single pass methods; total workload simulation; Algorithm design and analysis; Computational modeling; Data mining; Hardware; Iterative algorithms; Microarchitecture; Pipelines; Predictive models; Process design; Sampling methods;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Design, 2007. ICCD 2007. 25th International Conference on
  • Conference_Location
    Lake Tahoe, CA
  • ISSN
    1063-6404
  • Print_ISBN
    978-1-4244-1257-0
  • Electronic_ISBN
    1063-6404
  • Type

    conf

  • DOI
    10.1109/ICCD.2007.4601941
  • Filename
    4601941