GPU parallelization of unstructured/hybrid grid ALE multigrid unsteady solver for moving body problems. (30th March 2015)

• Record Type: Journal Article
• Authors: Ma, Wenpeng; Lu, Zhonghua; Zhang, Jian
• Is Part Of: Computers & fluids. Volume 110(2015)
• Page Start: 122
• Page End: 135
• Publication Date: 2015-03-30
• Subjects: Graphics Processing Units (GPUs) -- Unstructured/hybird grid -- Multigrid -- Mesh deformation; Fluid dynamics -- Data processing -- Periodicals; 532.050285
• DOI: 10.1016/j.compfluid.2014.11.012
• Languages: English
• ISSNs: 0045-7930

Abstract

Highlights: Single GPU implementation for MGM ALE unsteady solver based on moving grid. High efficient Multi-GPU implementation using overlapping communications. Detailed weak and strong scalability analysis. Abstract: Graphics Processing Units (GPUs) are currently being used to accelerate Computational Fluid Dynamics (CFD) codes and many GPU based CFD solvers have already showed that GPUs have great capabilities in numerical simulations. The development and validation of an unstructured/hybrid grid ALE (Arbitrary Lagrangian–Eulerian) multigrid unsteady solver on the GPU for moving body problems are present. This GPU based unsteady solver consists of two modules. A BICGSTAB based mesh deformation module which updates the mesh nodes to new positions as the interfaces of bodies move, and a geometrical multigrid (GMG) RANS (Reynolds-Averaged Navier–Stokes) solver which manages the flow computation using the dual time stepping approach. The GPU implementation and optimization for this two modules are discussed, respectively. Both 2D and 3D validation cases are carried out to analyze the accuracy and efficiency of this solver. The GPU results obtained agree well with the experimental measurements, as well as the numerical solutions presented by other researchers. A mixed MPI-CUDA implementation makes the unsteady flow solver capable of running on a multi-GPU cluster that consists of a set of GPUs and CPUs. Both the strong and weak scalability of the code are investigated on a heterogeneous computing platform.