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Open AccessArticle

Dynamic Load Balancing Techniques for Particulate Flow Simulations

1
Chair for System Simulation, Friedrich–Alexander–Universität Erlangen–Nürnberg, Cauerstraße 11, 91058 Erlangen, Germany
2
CERFACS, 42 Avenue Gaspard Coriolis, 31057 Toulouse, France
*
Author to whom correspondence should be addressed.
Computation 2019, 7(1), 9; https://doi.org/10.3390/computation7010009
Received: 30 November 2018 / Revised: 18 January 2019 / Accepted: 20 January 2019 / Published: 23 January 2019
(This article belongs to the Section Computational Engineering)
Parallel multiphysics simulations often suffer from load imbalances originating from the applied coupling of algorithms with spatially and temporally varying workloads. It is, thus, desirable to minimize these imbalances to reduce the time to solution and to better utilize the available hardware resources. Taking particulate flows as an illustrating example application, we present and evaluate load balancing techniques that tackle this challenging task. This involves a load estimation step in which the currently generated workload is predicted. We describe in detail how such a workload estimator can be developed. In a second step, load distribution strategies like space-filling curves or graph partitioning are applied to dynamically distribute the load among the available processes. To compare and analyze their performance, we employ these techniques to a benchmark scenario and observe a reduction of the load imbalances by almost a factor of four. This results in a decrease of the overall runtime by 14% for space-filling curves. View Full-Text
Keywords: high performance computing; multiphysics simulation; lattice Boltzmann method; rigid particle dynamics; particulate flow; load balancing; parallel computing high performance computing; multiphysics simulation; lattice Boltzmann method; rigid particle dynamics; particulate flow; load balancing; parallel computing
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MDPI and ACS Style

Rettinger, C.; Rüde, U. Dynamic Load Balancing Techniques for Particulate Flow Simulations. Computation 2019, 7, 9.

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