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Workshops on Parallel-in-Time Integration

PinT 2020: Parallel-in-Time Integration Methods pp 81–94Cite as

Twelve Ways to Fool the Masses When Giving Parallel-in-Time Results

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Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS,volume 356)

Abstract

Getting good speedup—let alone high parallel efficiency—for parallel-in-time (PinT) integration examples can be frustratingly difficult. The high complexity and large number of parameters in PinT methods can easily (and unintentionally) lead to numerical experiments that overestimate the algorithm’s performance. In the tradition of Bailey’s article “Twelve ways to fool the masses when giving performance results on parallel computers”, we discuss and demonstrate pitfalls to avoid when evaluating the performance of PinT methods. Despite being written in a light-hearted tone, this paper is intended to raise awareness that there are many ways to unintentionally fool yourself and others and that by avoiding these fallacies more meaningful PinT performance results can be obtained.

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Notes

  1. 1.

    One exception are so-called “parallel-across-the-method” PinT methods in the terminology by Gear [8] that can deliver smaller scale parallelism.

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Acknowledgements

The work of Minion was supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Applied Mathematics program under contract number DE-AC02005CH11231. Part of the simulations was performed using resources of the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.

Author information

Authors and Affiliations

  1. Chair Computational Mathematics, Institute of Mathematics, Hamburg University of Technology, Am Schwarzenberg-Campus 3, 21073, Hamburg, Germany

    Sebastian Götschel & Daniel Ruprecht

  2. Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA

    Michael Minion

  3. Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, 52425, Jülich, Germany

    Robert Speck

Authors
  1. Sebastian Götschel
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  2. Michael Minion
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  3. Daniel Ruprecht
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  4. Robert Speck
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Corresponding author

Correspondence to Michael Minion .

Editor information

Editors and Affiliations

  1. Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA

    Assist. Prof. Benjamin Ong

  2. Department of Mathematics and Statistics, University of New Mexico, Albuquerque, NM, USA

    Dr. Jacob Schroder

  3. Department of Mathematics, University of Exeter, Exeter, UK

    Dr. Jemma Shipton

  4. Department of Mathematics, University of Wuppertal, Wuppertal, Nordrhein-Westfalen, Germany

    Dr. Stephanie Friedhoff

Appendix 1

Appendix 1

The value of \(\sigma \) in the first example is 0.02.

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Götschel, S., Minion, M., Ruprecht, D., Speck, R. (2021). Twelve Ways to Fool the Masses When Giving Parallel-in-Time Results. In: Ong, B., Schroder, J., Shipton, J., Friedhoff, S. (eds) Parallel-in-Time Integration Methods. PinT 2020. Springer Proceedings in Mathematics & Statistics, vol 356. Springer, Cham. https://doi.org/10.1007/978-3-030-75933-9_4

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