  Consider a number of workers running SGD independently on the same pool of
data and averaging the models every once in a while -- a common but not well
understood practice. We study model averaging as a variance-reducing mechanism
and describe two ways in which the frequency of averaging affects convergence.
For convex objectives, we show the benefit of frequent averaging depends on the
gradient variance envelope. For non-convex objectives, we illustrate that this
benefit depends on the presence of multiple globally optimal points. We
complement our findings with multicore experiments on both synthetic and real
data.
