  Pipelined Krylov methods seek to ameliorate the latency due to inner products
necessary for projection by overlapping it with the computation associated with
sparse matrix-vector multiplication. We clarify a folk theorem that this can
only result in a speedup of $2\times$ over the naive implementation. Examining
many repeated runs, we show that stochastic noise also contributes to the
latency, and we model this using an analytical probability distribution. Our
analysis shows that speedups greater than $2\times$ are possible with these
algorithms.
