  Many appplications in computational science are sufficiently
compute-intensive that they depend on the power of parallel computing for
viability. For all but the "embarrassingly parallel" problems, the performance
depends upon the level of granularity that can be achieved on the computer
platform.
  Our computational particle physics applications require machines that can
support a wide range of granularities, but in general, compute-intensive
state-of-the-art projects will require finely grained distributions. Of the
different types of machines available for the task, we consider cluster
computers.
  The use of clusters of commodity computers in high performance computing has
many advantages including the raw price/performance ratio and the flexibility
of machine configuration and upgrade. Here we focus on what is usually
considered the weak point of cluster technology; the scaling behaviour when
faced with a numerically intensive parallel computation. To this end we examine
the scaling of our own applications from numerical quantum field theory on a
cluster and infer conclusions about the more general case.
