scholarly journals Efficient Finite Element Geometric Multigrid Solvers for Unstructured Grids on Graphics Processing Units

2011 ◽  
Author(s):  
M. Geveler ◽  
D. Ribbrock ◽  
D. Göddeke ◽  
P. Zajac ◽  
S. Turek
Keyword(s):  
Finite Element ◽  
Graphics Processing Units ◽  
Unstructured Grids ◽  
Multigrid Solvers ◽  
Graphics Processing ◽  
Geometric Multigrid

scholarly journals GPU IMPLEMENTATION OF A VISCOUS FLOW SOLVER ON UNSTRUCTURED GRIDS

2016 ◽  
Vol 42 ◽  
pp. 1660167
Author(s):  
TIANHAO XU ◽  
LONG CHEN
Keyword(s):  
Graphics Processing Units ◽  
Stokes Equations ◽  
Unstructured Grids ◽  
Flow Simulation ◽  
Navier Stokes ◽  
Competitive Advantages ◽  
Navier Stokes Equations ◽  
Flow Solver ◽  
Volume Method ◽  
Graphics Processing

Graphics processing units have gained popularities in scientific computing over past several years due to their outstanding parallel computing capability. Computational fluid dynamics applications involve large amounts of calculations, therefore a latest GPU card is preferable of which the peak computing performance and memory bandwidth are much better than a contemporary high-end CPU. We herein focus on the detailed implementation of our GPU targeting Reynolds-averaged Navier-Stokes equations solver based on finite-volume method. The solver employs a vertex-centered scheme on unstructured grids for the sake of being capable of handling complex topologies. Multiple optimizations are carried out to improve the memory accessing performance and kernel utilization. Both steady and unsteady flow simulation cases are carried out using explicit Runge-Kutta scheme. The solver with GPU acceleration in this paper is demonstrated to have competitive advantages over the CPU targeting one.

Unsteady CFD computations using vertex-centered finite volumes for unstructured grids on Graphics Processing Units

2010 ◽  
Vol 67 (2) ◽  
pp. 232-246 ◽  
Author(s):  
V. G. Asouti ◽  
X. S. Trompoukis ◽  
I. C. Kampolis ◽  
K. C. Giannakoglou
Keyword(s):  
Graphics Processing Units ◽  
Unstructured Grids ◽  
Finite Volumes ◽  
Graphics Processing

Implementation of an Edge-Based Navier-Stokes Solver for Unstructured Grids in Graphics Processing Units

2011 ◽  
Author(s):  
Fernando Gisbert ◽  
Roque Corral ◽  
Guillermo Pastor
Keyword(s):  
Graphics Processing Units ◽  
Unstructured Grids ◽  
Three Dimensional ◽  
Navier Stokes ◽  
Computational Time ◽  
Central Processing ◽  
Order Of Magnitude ◽  
Edge Based ◽  
Graphics Processing ◽  
Gpu Implementation

The implementation of an edge-based three-dimensional RANS equations solver for unstructured grids that runs on both central processing units (CPUs) and graphics processing units (GPUs) is presented. This CPU/GPU duality is kept without double-writing the code, reducing programming and maintenance costs. The GPU implementation is based on the standard OpenCL language. The code has been parallelized using MPI. Some turbomachinery benchmark cases are presented. For all cases, an order of magnitude reduction in computational time is achieved when the code is executed on GPUs instead of CPUs.

scholarly journals Numerical Study of Geometric Multigrid Methods on CPU-GPU Heterogeneous Computers

2014 ◽  
Vol 6 (01) ◽  
pp. 1-23 ◽  
Author(s):  
Chunsheng Feng ◽  
Shi Shu ◽  
Jinchao Xu ◽  
Chen-Song Zhang
Keyword(s):  
Graphics Processing Units ◽  
Multigrid Method ◽  
Numerical Study ◽  
Elliptic Partial Differential Equations ◽  
Poisson Solver ◽  
Numerical Studies ◽  
Computational Work ◽  
Graphics Processing ◽  
Geometric Multigrid Method ◽  
Geometric Multigrid

AbstractThe geometric multigrid method (GMG) is one of the most efficient solving techniques for discrete algebraic systems arising from elliptic partial differential equations. GMG utilizes a hierarchy of grids or discretizations and reduces the error at a number of frequencies simultaneously. Graphics processing units (GPUs) have recently burst onto the scientific computing scene as a technology that has yielded substantial performance and energy-efficiency improvements. A central challenge in implementing GMG on GPUs, though, is that computational work on coarse levels cannot fully utilize the capacity of a GPU. In this work, we perform numerical studies of GMG on CPU-GPU heterogeneous computers. Furthermore, we compare our implementation with an efficient CPU implementation of GMG and with the most popular fast Poisson solver, Fast Fourier Transform, in the cuFFT library developed by NVIDIA.

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