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An Efficient Particle Tracking Algorithm for Large-Scale Parallel Pseudo-Spectral Simulations of Turbulence

MPG-Autoren
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Lalescu,  Christian C.
Max Planck Research Group Theory of Turbulent Flows, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Wilczek,  Michael
Max Planck Research Group Theory of Turbulent Flows, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Zitation

Lalescu, C. C., Bramas, B., Rampp, M., & Wilczek, M. (2022). An Efficient Particle Tracking Algorithm for Large-Scale Parallel Pseudo-Spectral Simulations of Turbulence. Computer Physics Communications, 278: 108406. doi:10.1016/j.cpc.2022.108406.


Zitierlink: https://hdl.handle.net/21.11116/0000-0008-CBFB-9
Zusammenfassung
Particle tracking in large-scale numerical simulations of turbulent flows
presents one of the major bottlenecks in parallel performance and scaling
efficiency. Here, we describe a particle tracking algorithm for large-scale
parallel pseudo-spectral simulations of turbulence which scales well up to
billions of tracer particles on modern high-performance computing
architectures. We summarize the standard parallel methods used to solve the
fluid equations in our hybrid MPI/OpenMP implementation. As the main focus, we
describe the implementation of the particle tracking algorithm and document its
computational performance. To address the extensive inter-process communication
required by particle tracking, we introduce a task-based approach to overlap
point-to-point communications with computations, thereby enabling improved
resource utilization. We characterize the computational cost as a function of
the number of particles tracked and compare it with the flow field computation,
showing that the cost of particle tracking is very small for typical
applications.