Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  title =        "{PERFORMANCE} {MODELING} {USING} {A} {GENETIC}
  author =       "Kavitha Raghavachar",
  year =         "2006",
  month =        aug # "~10",
  school =       "North Carolina State University",
  address =      "USA",
  contributor =  "Dr.Ranji S Ranjithan and Dr.John W Baugh and Dr.G
  language =     "en",
  oai =          "oai:NCSU:etd-08072006-014705",
  rights =       "unrestricted; I hereby certify that, if appropriate, I
                 have obtained and attached hereto a written permission
                 statement from the owner(s) of each third party
                 copyrighted matter to be included in my thesis, dis
                 sertation, or project report, allowing distribution as
                 specified below. I certify that the version I submitted
                 is the same as that approved by my advisory committee.
                 I hereby grant to NC State University or its agents the
                 non-exclusive license to archive and make accessible,
                 under the conditions specified below, my thesis,
                 dissertation, or project report in whole or in part in
                 all forms of media, now or hereafter known. I retain
                 all other ownership rights to the copyright of the
                 thesis, dissertation or project report. I also retain
                 the right to use in future works (such as articles or
                 books) all or part of this thesis, dissertation, or
                 project repor t.",
  keywords =     "genetic algorithms, genetic programming, Civil
  URL =          "",
  URL =          "",
  size =         "36 pages",
  abstract =     "Application performance models provide insight to
                 designers of high performance computing (HPC) systems
                 on the role of subsystems such as the processor or the
                 network in determining application performance and
                 allow HPC centres to more accurately target
                 procurements to resource requirements. Performance
                 models can also be used to identify application
                 performance bottlenecks and to provide insights about
                 scalability issues. The suitability of a performance
                 model, however, for a particular performance
                 investigation is a function of both the accuracy and
                 the cost of the model. A semi-empirical model developed
                 in an earlier publication for an astrophysics
                 application was shown to be inaccurate when predicting
                 communication cost for large numbers of processors. It
                 was hypothesised that this deficiency is due to the
                 inability of the model to adequately capture
                 communication contention (threshold effects) as well as
                 other un-modeled components such as noise and I/O
                 contention. This thesis demonstrates a new approach to
                 capture these unknown features to improve the
                 predictive capabilities of the model. This approach
                 uses a systematic model error correction procedure that
                 uses evolutionary algorithms to find an error
                 correction term to augment the existing model. Four
                 variations of this procedure were investigated and all
                 were shown to produce improved results than the old
                 model. Successful cross-platform application of this
                 approach showed that it adequately captures machine
                 dependent characteristics. This approach was then
                 extended to a second application, which too showed
                 improved results than the standard semi-empirical
                 modelling approach.",

Genetic Programming entries for Kavitha Raghavachar