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。 / In 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.