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

TL;DR

This paper introduces a scalable particle tracking algorithm for large-scale turbulence simulations that efficiently overlaps communication and computation, significantly improving performance on high-performance computing systems.

Contribution

The paper presents a novel task-based parallel particle tracking algorithm that enhances scalability and efficiency in large-scale turbulence simulations.

Findings

Scales well up to billions of particles on modern HPC architectures.

Overlapping communication with computation improves resource utilization.

Particle tracking cost is minimal compared to flow field computation.

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.