论文标题
BSF：一个并行计算模型，用于估计集群计算系统上迭代数值算法的估计
BSF: a parallel computation model for scalability estimation of iterative numerical algorithms on cluster computing systems
论文作者
论文摘要
本文研究了一种称为批量同步农场（BSF）的新的并行计算模型，该模型的重点是估计旨在集群计算系统的计算密集型迭代算法的可扩展性。在BSF模型中，计算机是由网络连接并根据主/从范式组织的一组处理器节点。提出了BSF模型的成本度量。此成本度量要求算法以列表的操作形式表示。这使我们能够得出一个预测并行程序的可伸缩性边界的方程式：加速器开始降低的最大处理器节点数量。本文包括将BSF模型应用于设计和分析并行NU算法的几个示例。在集群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适当性。
This paper examines a new parallel computation model called bulk synchronous farm (BSF) that focuses on estimating the scalability of compute-intensive iterative algorithms aimed at cluster computing systems. 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 several examples of applying the BSF model to designing and analyzing parallel nu-merical algorithms. The large-scale computational experiments conducted on a cluster computing system confirm the adequacy of the analytical estimations obtained using the BSF model.
