  The solution of the path structured multimarginal Schr\"{o}dinger bridge
problem (MSBP) is the most-likely measure-valued trajectory consistent with a
sequence of observed probability measures or distributional snapshots. We
leverage recent algorithmic advances in solving such structured MSBPs for
learning stochastic hardware resource usage by control software. The solution
enables predicting the time-varying distribution of hardware resource
availability at a desired time with guaranteed linear convergence. We
demonstrate the efficacy of our probabilistic learning approach in a model
predictive control software execution case study. The method exhibits rapid
convergence to an accurate prediction of hardware resource utilization of the
controller. The method can be broadly applied to any software to predict
cyber-physical context-dependent performance at arbitrary time.
