Acceleration of a 3D Euler Solver Using Commodity Graphics Hardware

Abstract

The porting of two-and three-dimensional Euler solvers from a conventional CPU implementation to the novel target platform of the Graphics Processing Unit (GPU) is described. The motivation for such an effort is the impressive performance that GPUs offer: typically 10 times more floating point operations per second than a modern CPU, with over 100 processing cores and all at a very modest financial cost. Both codes were found to generate the same results on the GPU as the FORTRAN versions did on the CPU. The 2D solver ran up to 29 times quicker on the GPU than on the CPU; the 3D solver 16 times faster.

An MPI-CUDA Implementation for Massively Parallel Incompressible Flow Computations on Multi-GPU Clusters

“…The advent of DirectX 9 hardware in 2003 with floating point support, combined with early work on high level language support such as BrookGPU, Cg, and Sh, led to a rapid expansion of the field. [14][15][16][17][18][19] Brandvik and Pullan 20 show the implementation of 2D and 3D Euler solver on a single GPU, showing 29× speedup for the 2D solver and 16× speedup for the 3D solver. One unique feature of their paper is the implementation of the solvers in both BrookGPU and CUDA.…”

On the performance of a 2D unstructured computational rheology code on a GPU

“…Brandvik and Pullan [9] reported speed-ups of 29x (2D) and 16x (3D) when solving the Euler equations using structured grids. Elsen et al [7] also solved the Euler equations for hypersonic flows with speed-ups of 40x (simple geometries) or 20x (complex geometries).…”

Multidisciplinary Simulation Acceleration using Multiple Shared-Memory Graphical Processing Units

“…Research has shown that GPUs can reliably produce an order of magnitude speedup, when measured against comparable high performance CPUs, for a wide range of scientific applications [5][6][7][8][9][10][11][12][13][14][15] . Bolz et al…”