本研究用於流場計算的平行化工具，是利用一般市面上容易購得的個人電腦經由Gigabit乙太網路自行組裝成叢集電腦，使用Linux作業系統與MPI (Messages Passing Interface)平行函式庫，配合平行演算法中的區域分割法來進行平行計算，以解決計算複雜的流場問題所需要之大量記憶體空間及計算時間。本研究模擬問題為三維上蓋牽動空穴流，使用的演算法為時間步進法，最多使用到八台電腦進行平行效率測試與耗時分析，比較應用一維拓撲學與二維拓撲學進行區域分割法的差異性。此外，本研究亦探討相異硬體配備的選擇標準對於平行效率的影響。
　　針對本研究所測試的問題，結果顯示出平行效率會隨著參與計算的處理器數目下降，在平行計算的過程中網路傳輸量與計算量的比值越低則平行效率越好，所以決定處理器數目的準則在於要使處理器的使用率達到高負載；受限於所使用的時間步進演算法，可藉由適度的減少網路傳輸部分來提升平行效率。而切割區域的方法對於平行效率亦有很大的影響，對於三維問題建議使用二維拓撲學會有較佳的平行效率。而自行組裝的叢集電腦往往會有各主機硬體配備不同的情況，而叢集電腦的整體計算能力主要取決於其中計算能力最差之主機。為了減少計算資源的浪費，應選擇硬體配備與計算速度相近的主機，才能充分發揮處理器的計算能力。

In order to overcome the large demand of computer memory and computation time required for the complex flow computations, a homemade PC cluster which is constructed by nine personal computers (PC), available in the electronic hardware stores, together with Gigabit Ethernet serves as the computational tool for flow simulation. This parallel computational platform is operated under the Linux operating system on which the MPI (Messages Passing Interface) libraries are installed. The test problem is a three dimensional (3D) lid-driven cavity flow and is numerically solved with the fractional time-step method. Two domain decomposition methods based on 1D and 2D topologies are investigated at various combination of (from one to eight) computers by comparing their parallel efficiencies.
It shows that the parallel efficiency is decreased with the increasing number of PC. The lower the ratio of the data transfer quantity through the network to the computational load of a processor is, the higher the parallel efficiency is in the course of parallel computing. The criteria for determining the number of processors in a computational job is to assure high computational loading for every processor in the PC cluster. With the fractional time-step method employed in this study, the parallel efficiency can be improved by reducing the data transfer quantity through network transmission. For the presently investigated 3D problem, the use of the 2D topology of domain decomposition method leads to better parallel efficiency than the use of the 1D topology of domain decomposition method. One drawback of the homemade PC cluster is stemmed from the combination of different versions of processors. The overall computational efficiency of a PC cluster is primarily dominated by the processor possessing lowest computational efficiency. It is therefore suggested to construct a PC cluster by selecting the processors with same level of computational speed.

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