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Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/59638
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dc.contributor.advisor曾正男zh_TW
dc.contributor.advisorTzeng, Jengnanen_US
dc.contributor.author李沛承zh_TW
dc.contributor.authorLee, Pei Chengen_US
dc.creator李沛承zh_TW
dc.creatorLee, Pei Chengen_US
dc.date2012en_US
dc.date.accessioned2013-09-03T02:05:13Z-
dc.date.available2013-09-03T02:05:13Z-
dc.date.issued2013-09-03T02:05:13Z-
dc.identifierG0099751006en_US
dc.identifier.urihttp://nccur.lib.nccu.edu.tw/handle/140.119/59638-
dc.description碩士zh_TW
dc.description國立政治大學zh_TW
dc.description應用數學研究所zh_TW
dc.description99751006zh_TW
dc.description101zh_TW
dc.description.abstract近年來資料產生的數量遠超過過去可處理的數量，以現今的個人電腦使用傳統的方法已經無法處理大資料的運算與分析，所以改善傳統的方法與平行化為必經的方向，本論文以拆解合成-多元尺度法的平行化為主要討論對象，除了介紹Python程式語言及其相關套件如何撰寫平行化程式，我們將拆解合成-多元尺度法從原本的單核心版本改進為多核心版本，並且探索拆解合成-多元尺度法在平行化過程中的計算效能，藉以了解拆解合成-多元尺度法在平行化計算時的參數要如何設定，使得平行化的SC-MDS可以有最高的計算效率。經實驗證明多核心底下的SC-MDS平行化又把SC-MDS單核心的效能做個再次的提升。zh_TW
dc.description.abstractIn recent years, the number of generated data is growing fast such that it is infeasible to process by using traditional methods. So improving traditional methods and developing paralled computing methods are important issues. The main contribution of this thesis is to delelope the parallel version of the split-and-combine multidimensional scaling method(SC-MDS). We will fistly introduce fundamental python program, the basic python packages and the python multi-core program. Then we will implement the serial core version of SC-MDS to the multi-core version. Moreover, we will discover the efficiency of the multi-core version of SC-MDS. Then we can understand how to determine the parameters of the parllel version of SC-MDS. By our experimental results, we successfully implement the serial core of SC-MDS to the faster parallel version of SC-MDS.en_US
dc.description.tableofcontents目錄：\n論文口試委員審定書. . . . . . . . . . . . . . . . . . . . . ii\n授權書. . . . . . . . . . . . . . . . . . . . . . . . . . iii\n中文摘要. . . . . . . . . . . . . . . . . . . . . . . . . iv\n英文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . v\n誌謝. . . . . . . . . . . . . . . . . . . . . . . . . . . vi\n目錄. . . . . . . . . . . . . . . . . . . . . . . . . . vii\n表目錄. . . . . . . . . . . . . . . . . . . . . . . . . . ix\n圖目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . x\n第一章、簡介. . . . . . . . . . . . . . . . . . . . . . . . 1\n1.1 為何用Python . . . . . . . . . . . . . . . . . . . . . 1\n1.2 平行化的需求. . . . . . . . . . . . . . . . . . . . . . 3\n1.3 SCMDS的基本介紹. . . . . . . . . . . . . . . . . . . . 3\n第二章、Python之平行計算. . . . . . . . . . . . . . . . . . . 5\n2.1 Python基本運算之套件與工具. . . . . . . . . . . . . . . . 5\n2.2 Python平行計算之套件與相關介紹. . . . . . . . . . . . . . 9\n2.3 Python控制核心之套件. . . . . . . . . . . . . . . . . . 21\n第三章、SCMDS與其平行化. . . . . . . . . . . . . . . . . . . 22\n3.1 SCMDS以及單核心版本for Python. . . . . . . . . . . . . 22\n3.2 SCMDS的平行化. . . . . . . . . . . . . . . . . . . . . 28\n第四章、實驗結果. . . . . . . . . . . . . . . . . . . . . . 31\n4.1 SC-MDS與其平行化在多核心控制下之比較. . . . . . . . . . . 31\n4.2 多核心的操作對於SC-MDS的平行化在各個階段的影響. . . . . . . 38\n4.3 多核心的操作中MDS平行化的效能比. . . . . . . . . . . . . . 42\n第五章、結論. . . . . . . . . . . . . . . . . . . . . . . . 47\n參考文獻. . . . . . . . . . . . . . . . . . . . . . . . . 48\n附錄A：SC-MDS單核心版本的code. . . . . . . . . . . . . . . 50\n附錄B：SC-MDS拆解平行化版本的code. . . . . . . . . . . . . . 60zh_TW
dc.format.extent2340348 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen_US-
dc.source.urihttp://thesis.lib.nccu.edu.tw/record/#G0099751006en_US
dc.subjectSC-MDSzh_TW
dc.subjectPythonzh_TW
dc.subject多核心zh_TW
dc.titlePython平行化在SCMDS上之應用zh_TW
dc.titleThe application of parallel Python in SCMDSen_US
dc.typethesisen
dc.relation.reference[1] David Griffths、Paul Barry. 深入淺出程式設計. 歐萊禮, 2011.\n[2] Paul Barry. 深入淺出Python. 歐萊禮, 2011.\n[3] Ingwer Borg and Patrick J. F. Groenen. Modern multidimensional scaling. Springer Series in Statistics. Springer, New York, second edition, 2005. Theory and applications.\n[4] TOIBE Software BV. Tiobe programming community index, 2013. [online]http://www.tiobe.com/index.php/content/paperinfo/tpci/index.html.\n[5] Matthew Chalmers. A linear iteration time layout algorithm for visualising high-dimensional data. In Proceedings of the 7th conference on Visualization `96, VIS `96, pages 127-ff., Los Alamitos, CA, USA, 1996. IEEE Computer Society Press.\n[6] Pei-Chi Chen. Optimal grouping and missing data handling for split-and-combine multidimensional scaling. 2008.\n[7] Michael A. A. Cox and Trevor F. Cox. Multidimensional scaling. In Handbook of Data Visualization, Springer Handbooks Comp.Statistics, pages 315{347. Springer Berlin Heidelberg, 2008.\n[8] Pearu Peterson Eric Jones, Travis Oliphant et al. Open source scientific tools for python, 2001. [online] http://www.scipy.org/.\n[9] Python Software Foundation. About python, 2005. [online] http://www.python.org/about/.\n[10] Python Software Foundation. affinity 0.1.0, 2005. [online] https://pypi.python.org/pypi/affinity.\n[11] Python Software Foundation. Process-based \\threading" interface, 2005. [on-line] http://docs.python.org/2/library/multiprocessing.html.\n[12] Swaroop C H. Python入門, 2013. [online] http://files.swaroopch.com/python/byte_of_python.pdf.\n[13] Alistair Morrison, Greg Ross, and Matthew Chalmers. Fast multidimensional scaling through sampling, springs and interpolation. Information Visualization,2:68{77, 2003.\n[14] Mark Pilgrim. Dive into python, 2004. [online] http://www.diveintopython.net/toc/index.html.\n[15] Warren S. Torgerson. Multidimensional scaling. I. Theory and method. Psy-chometrika, 17:401{419, 1952.\n[16] Jengnan Tzeng. Python入門, 2009. [online] http://dl.dropboxusercontent.com/u/2688690/python_note.html.\n[17] Jengnan Tzeng. Split-and-combine singular value decomposition for large-scale matrix. J. Appl. Math., pages Art. ID 683053, 8, 2013.\n[18] Guido van Rossum. Python tutorial, 2008. [online] http://docs.python.org/2.5/tut/tut.html.zh_TW
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