No abstract

Techniques used to implement an unstructured grid solver on modern graphics hardware are described. The three-dimensional Euler equations for inviscid, compressible flow are considered. Effective memory bandwidth is improved by reducing total global memory access and overlapping redundant computation, as well as using an appropriate numbering scheme and data layout. The applicability of per-block shared memory is also considered. The performance of the solver is demonstrated on two benchmark cases: a missile and the NACA0012 wing. For a variety of mesh sizes, an average speed-up factor of roughly 9.5x is observed over the equivalent parallelized OpenMP-code running on a quad-core CPU, and roughly 33x over the equivalent code running in serial.

Techniques used to implement an unstructured grid solver on modern graphics hardware are described. The three‐dimensional Euler equations for inviscid, compressible flow are considered. Effective memory bandwidth is improved by reducing total global memory access and overlapping redundant computation, as well as using an appropriate numbering scheme and data layout. The applicability of per‐block shared memory is also considered. The performance of the solver is demonstrated on two benchmark cases: a NACA0012 wing and a missile. For a variety of mesh sizes, an average speed‐up factor of roughly 9.5 × is observed over the equivalent parallelized OpenMP code running on a quad‐core CPU, and roughly 33 × over the equivalent code running in serial. Copyright © 2010 John Wiley & Sons, Ltd.

No abstract

A parallel linelet preconditioner has been implemented to accelerate finite element (FE) solvers for incompressible flows when highly anisotropic meshes are used. The convergence of the standard preconditioned conjugate gradient (PCG) solver that is commonly used to solve the discrete pressure equations, greatly deteriorates due to the presence of highly distorted elements, which are of mandatory use for high Reynolds‐number flows. The linelet preconditioner notably accelerates the convergence rate of the PCG solver in such situations, saving an important amount of CPU time. Unlike other more sophisticated preconditioners, parallelization of the linelet preconditioner is almost straighforward. Numerical examples and some comparisons with other preconditioners are presented to demonstrate the performance of the proposed preconditioner.