本文是以平行處理技巧增大分子動力學模擬尺度以及提升模擬效率，使用以原子軌域線性結合法(LCAO, Linear Combination of Atomic Orbital)，又稱為緊束法(Tight-Binding Method)的半經驗勢能函數，來進行無法以週期性邊界條件(Periodic Boundary Condition)達成的大尺度分子動力學模擬(Large Scale Molecular Dynamics Simulation)。
　　本文所採用的平行演算法有原子分散法(Atom-Decomposition Method)、空間分散法(Spatial-Decomposition Method)兩種，能成功將系統模擬粒子數由數萬顆增加到數百萬顆甚至千萬顆。我們會先展示使用平行運算法的模擬的可靠性，接著再橫向探討對固定大小的模擬系統，採用不同演算法所造成的效率差異，以及討論造成此現象的原因。同時也縱向對同一種演算法效率與參與模擬計算處理器數目的關係進行探討。最後提出平行分子動力學模擬的瓶頸與改進的方向。

　　This study is .focused on the efficiency improvement of molecular dynamics simulation and the simulation enlargement with parallel computing technique. Using semi-empirical potiential derived from LCAO(Linear Combination of Atomic Orbital) to accomplish the Large Scale Molecular Dynamics Simulation which can’t be done with Periodic Boundary Condition on personal computer. Two parallel algorithms, Atom-Decomposition Method and Spatial-Decomposition Method, for classical short-range molecular dynamics simulation are used. The two algorithms are successfully tested on a standard tight-binding benchmark problem for system sizes ranging from 20,000 to 1,6000,000 atoms on distributed-memory parallel machine which allows for message-passing of data between independently executing processors, such as linux cluster. We discuss the efficiency difference of two parallel algorithms in the same simulation. The relation between efficiency and computing processors are also discussed. Finally, the bottlenecks of parallel molecular dynamics simulation will be carried out, and the state-of-the-art ways overcomings the bottlenecks are also shown to be our future works.

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