A parallel-in-time approach for accelerating direct-adjoint studies
Abstract
Parallel-in-time methods are developed to accelerate the direct-adjoint looping procedure. Particularly, we utilize the Paraexp algorithm, previously developed to integrate equations forward in time, to accelerate the direct-adjoint looping that arises from gradient-based optimization. We consider both linear and non-linear governing equations and exploit the linear, time-varying nature of the adjoint equations. Gains in efficiency are seen across all cases, showing that a Paraexp based parallel-in-time approach is feasible for the acceleration of direct-adjoint studies. This signifies a possible approach to further increase the run-time performance for optimization studies that either cannot be parallelized in space or are at their limit of efficiency gains for a parallel-in-space approach.
Cite
@article{arxiv.2004.00546,
title = {A parallel-in-time approach for accelerating direct-adjoint studies},
author = {Calum S. Skene and Maximilian F. Eggl and Peter J. Schmid},
journal= {arXiv preprint arXiv:2004.00546},
year = {2021}
}
Comments
37 pages, 9 figures