  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.
