Summary 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× 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× are possible with these algorithms. Copyright © 2016 John Wiley & Sons, Ltd.

We discuss data production rates and their impact on the performance of scientific applications using parallel computers. On one hand, too high rates of data production can be overwhelming, exceeding logistical capacities for transfer, storage and analysis. On the other hand, the rate limiting step in a computationally-based study should be the human-guided analysis, not the calculation. We present performance data for a biomolecular simulation of the enzyme, acetylcholinesterase, which uses the parallel molecular dynamics program EulerGROMOS. The actual production rates are compared against a typical time frame for results analysis where we show that the rate limiting step is the simulation, and that to overcome this will require improved output rates.