Tag: CFD
Summer School on Accelerating Scientific Computation at ICL
This Summer School will provide an introduction to a new dataflow approach for accelerating computation
Accelerating incompressible flow computations with a Pthreads-CUDA implementation on small-footprint multi-GPU platforms
In this study, we describe the implementation of an incompressible flow Navier-Stokes solver for multi-GPU workstation platforms. A shared-memory parallel code with identical numerical methods is also developed for multi-core CPUs to provide a fair comparison between CPUs and GPUs.
OpenCL-based implementation of an unstructured edge-based finite element convection-diffusion solver on graphics hardware
Here, we concentrate on unstructured approaches which are less common in the GPU world. We employ a nonstandard FE approach which leverages an optimized edge-based data structure allowing a highly parallel implementation
Comparison of GPU architectures for asynchronous communication with finite-differencing applications
We report on some practical algorithmic and data layout approaches and on performance data on a range of GPUs with CUDA. We focus on the use of multiple GPU devices with a single CPU host and the asynchronous CPU/GPU communications issues involved.
A GPU application for high-order compact finite difference scheme
A high-order compact finite difference scheme for the solution of fluid flow problems is implemented to run on GPU using CUDA
Efficient shallow water simulations on GPUs: Implementation, visualization, verification, and validation
In this paper, we present an efficient implementation of a state-of-the-art high-resolution explicit scheme for the shallow water equations on graphics processing units.
Massive Data Parallel Computational Framework for Exascale Hybrid Platforms
In this paper we present an alternative approach: a new computational framework for the development of massively data parallel scientific codes applications suitable for use on such petascale/exascale hybrid systems built upon the highly scalable Cactus framework.
Lattice Boltzmann method on GPGPUs: Architectural requirements and performance results
We here presented a highly optimized LBM based kernel for GPGPUs in CUDA and OpenCL. Since the LBM is mainly memory bandwidth bound and a single lattice update takes 304 Byte on the GPU and 456 Byte on the CPU in DP, we initially established an upper performance limit based on sustainablememory bandwidth
Development of a Flow Solver with Complex Kinetics on the Graphic Processing Units
In the current paper, we described the implementation of a numerical solver for simulating chemically reacting flow on the GPU. The fluid dynamics is modeled using high-order shock-capturing schemes, and the chemical kinetics is solved using an implicit solver.
Caedium 3.0: Hybrid CFD Modeling on GPUs and in the Cloud
The latest release of Symscape’s Caedium (v3.0) now has support for CFD simulations using NVIDIA CUDA GPU devices on Windows and Linux. Caedium is an integrated simulation environment that targets Computational Fluid Dynamics. The GPU support is provided by Symscape’s ofgpu linear solver library for OpenFOAM.
