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Computing and Visualization in Science
Erschienen in: Computing and Visualization in Science 1-2/2018

29.05.2018 | Special Issue Parallel-in-Time Methods

Lossy data compression reduces communication time in hybrid time-parallel integrators

verfasst von: Lisa Fischer, Sebastian Götschel, Martin Weiser

Erschienen in: Computing and Visualization in Science | Ausgabe 1-2/2018

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Abstract

Parallel-in-time methods for solving initial value problems are a means to increase the parallelism of numerical simulations. Hybrid parareal schemes interleaving the parallel-in-time iteration with an iterative solution of the individual time steps are among the most efficient methods for general nonlinear problems. Despite the hiding of communication time behind computation, communication has in certain situations a significant impact on the total runtime. Here we present strict, yet not sharp, error bounds for hybrid parareal methods with inexact communication due to lossy data compression, and derive theoretical estimates of the impact of compression on parallel efficiency of the algorithms. These and some computational experiments suggest that compression is a viable method to make hybrid parareal schemes robust with respect to low bandwidth setups.
Vorheriger Artikel Wave propagation characteristics of Parareal
Nächster Artikel Time-parallel simulation of the decay of homogeneous turbulence using Parareal with spatial coarsening

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Metadaten
Titel
Lossy data compression reduces communication time in hybrid time-parallel integrators
verfasst von
Lisa Fischer
Sebastian Götschel
Martin Weiser
Publikationsdatum
29.05.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Computing and Visualization in Science / Ausgabe 1-2/2018
Print ISSN: 1432-9360
Elektronische ISSN: 1433-0369
DOI
https://doi.org/10.1007/s00791-018-0293-2