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BSF: A parallel computation model for scalability estimation of iterative numerical algorithms on cluster computing systems

机译:BSF:群集计算系统迭代数值算法的可扩展性估计的并行计算模型

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This paper examines a novel parallel computation model called bulk synchronous farm (BSF) that focuses on estimating the scalability of compute-intensive iterative algorithms aimed at cluster computing systems. The main advantage of the proposed model is that it allows to estimate the scalability of a parallel algorithm before its implementation. Another important feature of the BSF model is the representation of problem data in the form of lists that greatly simplifies the logic of building applications. In the BSF model, a computer is a set of processor nodes connected by a network and organized according to the master/slave paradigm. A cost metric of the BSF model is presented. This cost metric requires the algorithm to be represented in the form of operations on lists. This allows us to derive an equation that predicts the scalability boundary of a parallel program: the maximum number of processor nodes after which the speedup begins to decrease. The paper includes examples of applying the BSF model to designing and analyzing parallel numerical algorithms. The large-scale computational experiments conducted on a cluster computing system confirm the adequacy of the analytical estimations obtained using the BSF model.
机译:本文介绍了一个名为批量同步农场(BSF)的新颖的并行计算模型,专注于估计旨在瞄准集群计算系统的计算密集型迭代算法的可扩展性。所提出的模型的主要优点是它允许在实现之前估计并行算法的可扩展性。 BSF模型的另一个重要特征是列表形式的问题数据的表示,这大大简化了构建应用程序的逻辑。在BSF模型中,计算机是由网络连接的一组处理器节点,并根据主/从地域组织组织。提出了BSF模型的成本度量。该成本度量要求算法以列表的操作形式表示。这允许我们推导出一种方程,其预测并行程序的可扩展性边界:后者的最大处理器节点数量开始减小。本文包括将BSF模型应用于设计和分析并行数值算法的示例。在群集计算系统上进行的大规模计算实验证实了使用BSF模型获得的分析估计的充分性。

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