摘要
本研究旨在使用高階程式語言 (Julia、MATLAB、Python) 於單機上執行平行運算，以個體計量經濟學與總體經濟學中的五個應用作為範例，包括資料讀取、蒙地卡羅方法、最大概似估計法、最大模擬概似估計法，以及價值函數迭代法。本文透過多執行緒展現多核心 CPU 的效能，亦透過 GPU 運算展現 GPU 的數值運算能力。模擬結果發現多執行緒 (4 個執行緒) 的最大加速比落於 1.6 (最大概似估計法) 至 4.1 (資料讀取) 之間，而 GPU 運算的最大加速比落於 3.6 (蒙地卡羅方法) 至 118.8 (價值函數迭代法) 之間。這些發現對於研究人員撰寫高效率程式具有重大意義。
並列摘要
The thesis shows how to implement parallel computing in high-level languages, including Julia, MATLAB, and Python, on a single machine. We demonstrate five applications covering microeconometrics and macroeconomics, including data input, Monte Carlo methods, maximum likelihood estimation, maximum simulated likelihood estimation, and the value function iteration. We illustrate the performance gain from multicore CPUs through multithreading and the computational power of GPUs through GPU computing. We find the maximum speedup of multithreading (four threads) ranges from 1.6 (maximum likelihood estimation) to 4.1 (data input), and that of GPU computing ranges from 3.6 (Monte Carlo methods) to 118.8 (value function iteration). These findings have implications for researchers writing efficient programs more conveniently.