Hybrid MPI and CUDA paralleled finite volume unstructured CFD simulations on a multi-GPU system

Porting unstructured Computational Fluid Dynamics (CFD) analysis of compressible flow to Graphics Processing Units (GPUs) confronts two difficulties. Firstly, non-coalescing access to the GPU's global memory is induced by indirect data access leading to performance loss. Secondly, data exchange among multi-GPU is complex due to data communication between processes and transfer between host and device, which degrades scalability. For increasing data locality on unstructured finite volume GPU simulations for compressible flow, we perform some optimizations, including cell and face renumbering, data dependence resolving, nested loops split, and loop mode adjustment. Then, a hybrid MPI-CUDA parallel framework with packing and unpacking exchange data on GPU is established for multi-GPU computing. Finally, after optimizations, the performance of the whole application on a GPU is increased by around 50%. Simulations of ONERA M6 cases on a single GPU (Nvidia Tesla V100) can achieve an average of 13.4 speedup compared to those on 28 CPU cores (Intel Xeon Gold 6132). On the baseline of 2 GPUs, strong scaling results show a parallel efficiency of 42% on 200 GPUs, while weak scaling tests give a parallel efficiency of 82.4% up to 200 GPUs.(c) 2022 Elsevier B.V. All rights reserved.