Acceleration of the data-parallel lower-upper relaxation time-integration method on GPU for an unstructured CFD solver

Abstract

The acceleration of CFD solvers on GPUs allows for large speedups, which enable more complex and detailed simulations. However, not all numerical methods can be efficiently executed in a massively parallel fashion. The Runge–Kutta (RK) and Lower-Upper Symmetric Gauss–Seidel (LU-SGS) time-integration methods are widely used on GPUs and multicore CPUs, respectively; however, the RK method suffers from low convergence speed, while the LU-SGS method is not adapted to a many-core environment. In this paper, we propose to accelerate the matrix-free version of the Data-Parallel Lower-Upper Relaxation (DP-LUR) time-integration method on the unstructured solver FaSTAR, developed by JAXA. The DP-LUR method outperformed the RK and LU-SGS methods on an NVIDIA Tesla V100 GPU when tested on the ONERA M6 and NASA CRM geometries, and reached up to 3.83% of the performance peak of one device.