PERFORMANCE MODELING USING A GENETIC PROGRAMMING BASED MODEL ERROR CORRECTION PROCEDURE

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@MastersThesis{oai:NCSU:etd-08072006-014705,

• title = "{PERFORMANCE} {MODELING} {USING} {A} {GENETIC} {PROGRAMMING} {BASED} {MODEL} {ERROR} {CORRECTION} {PROCEDURE}",
• 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 Mahinthakumar",
• 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 Engineering",
• URL = "http://www.lib.ncsu.edu/theses/available/etd-08072006-014705/unrestricted/etd.pdf",
• URL = "http://www.lib.ncsu.edu/theses/available/etd-08072006-014705/",
• 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

Citations