Distributed-memory simulations of turbulent flows on modern GPU systems using an adaptive pencil decomposition library
Joshua Romero, Pedro Costa, Massimiliano Fatica
Abstract
This paper presents a performance analysis of pencil domain decomposition methodologies for three-dimensional Computational Fluid Dynamics (CFD) codes for turbulence simulations, on several large GPU-accelerated clusters. The performance was assessed for the numerical solution of the Navier-Stokes equations in two codes which require the calculation of Fast-Fourier Transforms (FFT): a tri-periodic pseudo-spectral solver for isotropic turbulence, and a finite-difference solver for canonical turbulent flows, where the FFTs are used in its Poisson solver. Both codes use a newly developed transpose library that automatically determines the optimal domain decomposition and communication backend on each system. We compared the performance across systems with very different node topologies and available network bandwidth, to show how these characteristics impact decomposition selection for best performance. Additionally, we assessed the performance of several communication libraries available on these systems, such as Open-MPI, IBM Spectrum MPI, Cray MPI, the NVIDIA Collective Communication Library (NCCL), and NVSHMEM. Our results show that the optimal combination of communication backend and domain decomposition is highly system-dependent, and that the adaptive decomposition library is key in ensuring efficient resource usage with minimal user effort.