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 mas-ter/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 de-crease. 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.

中文翻译：

---

BSF：用于集群计算系统上迭代数值算法的可扩展性估计的并行计算模型

本文研究了一种称为批量同步场 (BSF) 的新并行计算模型，该模型侧重于估计针对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，计算机是一组通过网络连接并根据主/从模式组织的处理器节点。介绍了 BSF 模型的成本度量。此成本度量要求以列表操作的形式表示算法。这使我们能够推导出一个方程来预测并行程序的可扩展性边界：最大处理器节点数，在此之后加速开始下降。本文包括几个应用 BSF 模型来设计和分析并行数值算法的示例。