A Language For Describing Predictors And Its Application To Automatic Synthesis

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@InProceedings{Emer.1997.ISCA,
  author =       "Joel Emer and Nikolas Gloy",
  title =        "A Language For Describing Predictors And Its
                 Application To Automatic Synthesis",
  booktitle =    "Conference Proceedings. The 24th Annual International
                 Symposium on Computer Architecture",
  year =         "1997",
  pages =        "304--314",
  address =      "Denver, USA",
  month =        jun,
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming, SBSE, CPU
                 branch prediction",
  ISSN =         "1063-6897",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:10.1.1.598.7312",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.598.7312",
  broken =       "http://courses.engr.illinois.edu/ece512/Papers/Emer.1997.ISCA.pdf",
  DOI =          "doi:10.1145/384286.264212",
  size =         "11 pages",
  abstract =     "As processor architectures have increased their
                 reliance on speculative execution to improve
                 performance, the importance of accurate prediction of
                 what to execute speculatively has increased.
                 Furthermore, the types of values predicted have
                 expanded from the ubiquitous branch and call/return
                 targets to the prediction of indirect jump targets,
                 cache ways and data values. In general, the prediction
                 process is one of identifying the current state of the
                 system, and making a prediction for some as yet
                 uncomputed value based on that state. Prediction
                 accuracy is improved by learning what is a good
                 prediction for that state using a feedback process at
                 the time the predicted value is actually computed.
                 While there have been a number of efforts to formally
                 characterize this process, we have taken the approach
                 of providing a simple algebraic-style notation that
                 allows one to express this state identification and
                 feedback process. This notation allows one to describe
                 a wide variety of predictors in a uniform way. It also
                 facilitates the use of an efficient search technique
                 called genetic programming, which is loosely modelled
                 on the natural evolutionary process, to explore the
                 design space. In this paper we describe our notation
                 and the results of the application of genetic
                 programming to the design of branch and indirect jump
                 predictors.",
  notes =        "DEC Alpha 21264.

                 also known as \cite{604727}",
}

Genetic Programming entries for Joel Emer Nikolas Gloy

Citations