  Linear regression models depend directly on the design matrix and its
properties. Techniques that efficiently estimate model coefficients by
partitioning rows of the design matrix are increasingly popular for large-scale
problems because they fit well with modern parallel computing architectures. We
propose a simple measure of {\em concordance} between a design matrix and a
subset of its rows that estimates how well a subset captures the
variance-covariance structure of a larger data set. We illustrate the use of
this measure in a heuristic method for selecting row partition sizes that
balance statistical and computational efficiency goals in real-world problems.
