English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

An efficient particle tracking algorithm for large-scale parallel pseudo-spectral simulations of turbulence

MPS-Authors
/persons/resource/persons260823

Lalescu,  Cristian C.
Max Planck Computing and Data Facility, Max Planck Society;

/persons/resource/persons214444

Bramas,  Berenger
Max Planck Computing and Data Facility, Max Planck Society;

/persons/resource/persons110221

Rampp,  Markus
Max Planck Computing and Data Facility, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

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.


Cite as: https://hdl.handle.net/21.11116/0000-000A-81C7-3
Abstract
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.