Reducing Memory Requirements in Scientific Computing and Optimal Control

  • Sebastian Götschel
  • Christoph von Tycowicz
  • Konrad Polthier
  • Martin Weiser
Conference paper
Part of the Contributions in Mathematical and Computational Sciences book series (CMCS, volume 9)

Abstract

In high accuracy numerical simulations and optimal control of time-dependent processes, often both many timesteps and fine spatial discretizations are needed. Adjoint gradient computation, or post-processing of simulation results, requires the storage of the solution trajectories over the whole time, if necessary together with the adaptively refined spatial grids. In this paper we discuss various techniques to reduce the memory requirements, focusing first on the storage of the solution data, which are typically double precision floating point values. We highlight advantages and disadvantages of the different approaches. Moreover, we present an algorithm for the efficient storage of adaptively refined, hierarchic grids, and the integration with the compressed storage of solution data.

Keywords

Optimal Control Problem Proper Orthogonal Decomposition Adjoint Equation Memory Bandwidth Normalize Root Mean Square Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Notes

Acknowledgements

The authors gratefully acknowledge support by the DFG Research Center Matheon, project F9.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sebastian Götschel
    • 1
  • Christoph von Tycowicz
    • 2
  • Konrad Polthier
    • 2
  • Martin Weiser
    • 1
  1. 1.Zuse Institute BerlinBerlinGermany
  2. 2.Freie Universität BerlinBerlinGermany

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