A pencil-distributed finite-difference solver for extreme-scale calculations of turbulent wall flows at high Reynolds number

TL;DR

This paper introduces a scalable finite-difference solver optimized for high-performance GPU clusters, enabling extremely large turbulent flow simulations at high Reynolds numbers with improved efficiency and reduced communication overhead.

Contribution

The paper develops a pencil-distributed Poisson solver and a reworked parallel cyclic reduction method, significantly enhancing scalability and performance for large-scale turbulent flow simulations.

Findings

Achieved simulations with up to 80 billion grid points on 1024 GPUs.

Pencil-distributed approach doubles the speed over full-transpose algorithms at scale.

Solver maintains high performance with 2D domain decompositions, comparable to smaller system configurations.

Abstract

We present a computational method for extreme-scale simulations of incompressible turbulent wall flows at high Reynolds numbers. The numerical algorithm extends a popular method for solving second-order finite differences Poisson/Helmholtz equations using a pencil-distributed parallel tridiagonal solver to improve computational performance at scale. The benefits of this approach were investigated for high-Reynolds-number turbulent channel flow simulations, with up to about 80 billion grid points and 1024 GPUs on the European flagship supercomputers Leonardo and LUMI. An additional GPU porting effort of the entire solver had to be undertaken for the latter. Our results confirm that, while 1D domain decompositions are favorable for smaller systems, they become inefficient or even impossible at large scales. This restriction is relaxed by adopting a pencil-distributed approach. The results show that, at scale, the revised Poisson solver is about twice as fast as the baseline approach with the full-transpose algorithm for 2D domain decompositions. Strong and weak scalability tests show that the performance gains are due to the lower communication footprint. Additionally, to secure high performance when solving for wall-normal implicit diffusion, we propose a reworked flavor of parallel cyclic reduction (PCR) that is split into pre-processing and runtime steps. During pre-processing, small sub-arrays with independent 1D coefficients are computed by parallel GPU threads, without any global GPU communication. Then, at runtime, the reworked PCR enables a fast solution of implicit 1D diffusion without computational overhead. Our results show that the entire numerical solver, coupled with the PCR algorithm, enables extreme-scale simulations with 2D pencil decompositions, which do not suffer performance losses even when compared to the best 1D slab configurations available for smaller systems.