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Optimizing Communications of Data Parallel Programs in Scalable Cluster Systems

机译:在可伸缩集群系统中优化数据并行程序的通信

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With the improvement of personal computers and high-speed network, clusters have become the trend in designing high performance computing environments. As the researches of relative hardware and software technology of Cluster and Grid are constantly improved, the application of Cluster is growing popular. Due to information progress and increasing calculation capacity required by all kind of applications; the calculation has also extended to cross-network calculation. Through the Internet, which connects several Clusters, the mass calculation platform is combined into Cluster Grid. As a result of data partition and exchange that may happen during the executing program, communication localization becomes important in programming efficiency. This paper, then, proposes a mathematical method which achieves excellent data partitioning and maintains data calculation in local environment. We also conduct some theoretical analysis on the amounts of computing nodes and data partition in the hope of being applied to practical parallel environment and further to reduce communication cost.
机译:随着个人计算机和高速网络的改进,集群已成为设计高性能计算环境的趋势。随着对集群和网格相关硬件和软件技术的研究不断提高,集群的应用日益普及。由于信息的进步和各种应用程序所需的计算能力的提高;计算也扩展到跨网络计算。通过连接多个集群的Internet,海量计算平台被组合到Cluster Grid中。由于在执行程序期间可能发生数据分区和交换,因此通信本地化对于编程效率至关重要。然后,本文提出了一种数学方法,该方法可实现出色的数据分区并在本地环境中保持数据计算。我们还对计算节点的数量和数据分区进行了一些理论分析,希望将其应用于实际的并行环境并进一步降低通信成本。

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