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    Parallel Monte Carlo simulation and model reduction

    Author
    Tan, Yixuan
    View/Open
    178223_Tan_rpi_0185N_11035.pdf (4.122Mb)
    Other Contributors
    Fox, Peter A.; Shephard, Mark S.; Carothers, Christopher D.;
    Date Issued
    2017-05
    Subject
    Computer science
    Degree
    MS;
    Terms of Use
    This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.;
    Metadata
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    URI
    https://hdl.handle.net/20.500.13015/1957
    Abstract
    The high portability and robustness of Monte Carlo algorithm come with its high computational cost, as every dimension in the problem space needs to be sampled randomly. Therefore, parallelizing the Monte Carlo algorithm is necessary for improving performance.; We investigated the simulation problem for predicting columnar clustering. A reduced model was built using logistic regression to improve the computing performance. A large number of computer experiments were performed on Blue Gene Q and the results were used to train the logistic regression model using the machine learning library in Spark. The logistic regression model was evaluated by area under receiver operating characteristics curve, precision and recall.; In this work, a C++ software package is developed for running large scale Potts Monte Carlo simulation in parallel based on the MMSP framework[1]. The package is applied to model material microstructure evolution. Specifically, a new feature of parallel biased Monte Carlo sampling is developed and implemented. The biased Monte Carlo sampling can be used to simulate field gradients, e.g. temperature gradients. This enables solving more general problems, since gradients often present in physical processes. Also, we validated the parallel algorithm by two test cases. Besides, the scalability of the parallel algorithm was investigated on Blue Gene Q at RPI with different computing configurations.;
    Description
    May 2017; School of Science
    Department
    Dept. of Computer Science;
    Publisher
    Rensselaer Polytechnic Institute, Troy, NY
    Relationships
    Rensselaer Theses and Dissertations Online Collection;
    Access
    Restricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.;
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