本文選題：并行計(jì)算 + 微機(jī)集群��； 參考：《南京郵電大學(xué)》2013年碩士論文

【摘要】：采用傳統(tǒng)的單個(gè)處理器進(jìn)行運(yùn)算已經(jīng)無法滿足人們對(duì)計(jì)算能力的需求，而基于向量處理機(jī)和對(duì)稱多處理機(jī)的高性能計(jì)算機(jī)存在擴(kuò)展性差、價(jià)格昂貴、整體性能較低的問題。針對(duì)上述問題，探討了集群系統(tǒng)的相關(guān)理論、技術(shù)和方法，該系統(tǒng)易于實(shí)現(xiàn)，具有良好的可擴(kuò)展性、可用性以及很高的性價(jià)比使其在商業(yè)和科學(xué)研究的各個(gè)領(lǐng)域里受到人們的青睞。 本文使用普通以太網(wǎng)交換機(jī)和四臺(tái)PC在Linux環(huán)境下完成集群系統(tǒng)的構(gòu)建，該集群系統(tǒng)包括NFS文件共享系統(tǒng)、SSH遠(yuǎn)程登陸系統(tǒng)、MPI并行編程庫以及PBS作業(yè)管理系統(tǒng)等軟件模塊。利用C+MPI編程模式，以計(jì)算PI值的串行算法和并行算法為例，通過比較不同節(jié)點(diǎn)數(shù)、不同處理器數(shù)的程序運(yùn)算時(shí)間，，驗(yàn)證了該集群系統(tǒng)的并行性。 算法的并行度容易受矩陣劃分方法的影響，常見的矩陣劃分方法主要有帶狀劃分和棋盤劃分，通過矩陣相乘的算例分析并比較了這兩種劃分方法的時(shí)間和加速比，實(shí)驗(yàn)結(jié)果表明，棋盤劃分方法能開發(fā)更高的并行度。 HPL是測(cè)試集群系統(tǒng)浮點(diǎn)性能的最佳選擇，通過調(diào)整HPL.dat中的矩陣規(guī)模、LU分解的分塊大小、處理器網(wǎng)格大小以及參加計(jì)算的節(jié)點(diǎn)數(shù)等參數(shù)，總結(jié)了HPL測(cè)試參數(shù)的選取原則，優(yōu)化了集群系統(tǒng)的性能，評(píng)測(cè)出集群系統(tǒng)的最優(yōu)運(yùn)算速度為13.61Gflops。
[Abstract]:The traditional single processor is no longer able to meet the demand for computing power, but the high performance computer based on vector processor and symmetric multiprocessor has the problems of poor expansibility, high price and low overall performance. Aiming at the above problems, the related theories, techniques and methods of cluster system are discussed. The system is easy to realize and has good expansibility. Availability and high cost-effectiveness make it popular in all fields of commercial and scientific research. In this paper, the cluster system is constructed in Linux environment by using ordinary Ethernet switch and four PCs. The cluster system includes NFS file sharing system, NFS remote landing system, MPI parallel programming library, PBS job management system and so on. Using C MPI programming mode, the parallelism of the cluster system is verified by comparing the program operation time of different nodes and different processors, taking the serial algorithm and parallel algorithm for calculating Pi value as examples. The parallelism of the algorithm is easy to be affected by matrix partitioning methods. The common matrix partitioning methods mainly include banded partition and chessboard partitioning. The time and speedup ratio of these two partitioning methods are analyzed and compared by an example of matrix multiplication. Experimental results show that the chessboard partition method can develop a higher degree of parallelism. HPL is the best choice for testing floating-point performance of cluster system. By adjusting the size of matrix in HPL.dat and the partition size of LU decomposition, the size of processor grid and the number of nodes participating in the calculation, the selection principle of HPL test parameters is summarized. The performance of cluster system is optimized and the optimal operation speed of cluster system is determined to be 13.61 Gflops.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：TP338;TP316.81

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本文編號(hào)：1833852