Acceleration of iterative Navier-Stokes solvers on graphics processing units

2013 ◽  
Vol 27 (4-5) ◽  
pp. 201-209 ◽  
Author(s):  
Tadeusz Tomczak ◽  
Katarzyna Zadarnowska ◽  
Zbigniew Koza ◽  
Maciej Matyka ◽  
Łukasz Mirosław
Keyword(s):  
Graphics Processing Units ◽  
Navier Stokes ◽  
Graphics Processing

An Accelerated 3D Navier–Stokes Solver for Flows in Turbomachines

2010 ◽  
Vol 133 (2) ◽  
Author(s):  
Tobias Brandvik ◽  
Graham Pullan
Keyword(s):  
Graphics Processing Units ◽  
Three Dimensional ◽  
Navier Stokes ◽  
Linear Scaling ◽  
Test Case ◽  
Processing Unit ◽  
Central Processing ◽  
Order Of Magnitude ◽  
Graphics Processing ◽  
Good Agreement

A new three-dimensional Navier–Stokes solver for flows in turbomachines has been developed. The new solver is based on the latest version of the Denton codes but has been implemented to run on graphics processing units (GPUs) instead of the traditional central processing unit. The change in processor enables an order-of-magnitude reduction in run-time due to the higher performance of the GPU. The scaling results for a 16 node GPU cluster are also presented, showing almost linear scaling for typical turbomachinery cases. For validation purposes, a test case consisting of a three-stage turbine with complete hub and casing leakage paths is described. Good agreement is obtained with previously published experimental results. The simulation runs in less than 10 min on a cluster with four GPUs.

An Accelerated 3D Navier-Stokes Solver for Flows in Turbomachines

2009 ◽  
Author(s):  
Tobias Brandvik ◽  
Graham Pullan
Keyword(s):  
Graphics Processing Units ◽  
Three Dimensional ◽  
Navier Stokes ◽  
Linear Scaling ◽  
Test Case ◽  
Processing Unit ◽  
Central Processing ◽  
Order Of Magnitude ◽  
Graphics Processing ◽  
Good Agreement

A new three-dimensional Navier-Stokes solver for flows in turbomachines has been developed. The new solver is based on the latest version of the Denton codes, but has been implemented to run on Graphics Processing Units (GPUs) instead of the traditional Central Processing Unit (CPU). The change in processor enables an order-of-magnitude reduction in run-time due to the higher performance of the GPU. Scaling results for a 16 node GPU cluster are also presented, showing almost linear scaling for typical turbomachinery cases. For validation purposes, a test case consisting of a three-stage turbine with complete hub and casing leakage paths is described. Good agreement is obtained with previously published experimental results. The simulation runs in less than 10 minutes on a cluster with four GPUs.

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.

A Performance Study of Moving Particle Semi-Implicit Method for Incompressible Fluid Flow on GPU

2020 ◽  
Vol 11 (1) ◽  
pp. 83-94
Author(s):  
Kirankumar V Kataraki ◽  
Satyadhyan Chickerur
Keyword(s):  
Graphics Processing Units ◽  
Computing System ◽  
Navier Stokes ◽  
Performance Study ◽  
Governing Equations ◽  
Navier Stokes Equation ◽  
Gpu Processing ◽  
Moving Particle ◽  
Particle Search ◽  
Graphics Processing

The aim of moving particle semi-implicit (MPS) is to simulate the incompressible flow of fluids in free surface. MPS, when implemented, consumes a lot of time and thus, needs a very powerful computing system. Instead of using parallel computing system, the performance level of the MPS model can be improved by using graphics processing units (GPUs). The aim is to have a computing system that is capable of performing at high levels thereby enhancing the speed of processing the numerical computations required in MPS. The primary aim of the study is to build a GPU-accelerated MPS model using CUDA aimed at reducing the time taken to perform the search for neighboring particles. In order to increase the GPU processing speed, specific consideration is given towards the optimization of a neighboring particle search process. The numerical model of MPS is performed using the governing equations, notably the Navier-Stokes equation. The simulation model indicates that using GPU based MPS produce better performance compared to the traditional arrangement of using CPUs.

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.

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