CPU–GPU heterogeneous code acceleration of a finite volume Computational Fluid Dynamics solver

Abstract

This research focuses on accelerating the finite-volume Computational Fluid Dynamics (CFD) solver, SENSEI, through concurrent CPU–GPU heterogeneous computing, leveraging multiple CPUs and GPUs. An overview of SENSEI, its discretization, and the heterogeneous computing workflow utilizing MPI and OpenACC are provided. A performance model for CPU–GPU heterogeneous computing, incorporating ghost cell exchange usually applied in stencil computations, is introduced to estimate the performance. This work explores the scaling performance of CPU–GPU heterogeneous computing in comparison to a pure multi-CPU/GPU setup using a supersonic inlet test case. The results emphasize the advantages of leveraging both CPU and GPU computational power concurrently. Highlighting the collaborative synergy of CPUs and GPUs as co-workers, this work shows improved performance over exclusive use of pure CPUs or GPUs, with the proposed performance model ensuring a fair estimation of these advantages. This work concludes by offering suggestions for application users interested in accelerating scientific computing simulations and providing feedback for hardware architects designing improved CPU–GPU heterogeneous systems for heterogeneous computing.