A GPU-ready pseudo-spectral method for direct numerical simulations of multiphase turbulence

TL;DR

This paper presents a GPU-optimized pseudo-spectral solver for large-scale direct numerical simulations of multiphase turbulence, enabling efficient heterogeneous computing with minimal code modifications.

Contribution

The work introduces a GPU-ready, modular pseudo-spectral method for simulating multiphase turbulence, combining MPI and OpenACC for scalable performance across architectures.

Findings

Achieves significant speed-up on GPU-based systems.

Maintains modularity for easy adaptation to different architectures.

Enables large-scale, high-fidelity simulations of multiphase flows.

Abstract

In this work, we detail the GPU-porting of an in-house pseudo-spectral solver tailored towards large-scale simulations of interface-resolved simulation of drop- and bubble-laden turbulent flows. The code relies on direct numerical simulation of the Navier-Stokes equations, used to describe the flow field, coupled with a phase-field method, used to describe the shape, deformation, and topological changes of the interface of the drops or bubbles. The governing equations -Navier-Stokes and Cahn-Hilliard equations-are solved using a pseudo-spectral method that relies on transforming the variables in the wavenumber space. The code targets large-scale simulations of drop- and bubble-laden turbulent flows and relies on a multilevel parallelism. The first level of parallelism relies on the message-passing interface (MPI) and is used on multi-core architectures in CPU-based infrastructures. A second level of parallelism relies on OpenACC directives and cuFFT libraries and is used to accelerate the code execution when GPU-based infrastructures are targeted. The resulting multiphase flow solver can be efficiently executed in heterogeneous computing infrastructures and exhibits a remarkable speed-up when GPUs are employed. Thanks to the modular structure of the code and the use of a directive-based strategy to offload code execution on GPUs, only minor code modifications are required when targeting different computing architectures. This improves code maintenance, version control and the implementation of additional modules or governing equations.