Add to ORKGIn the present work an implementation of the Back and Forth Error Compensation and Correction (BFECC) algorithm specially suited for running on General-Purpose Graphics Processing Units (GPGPUs) through Nvidia’s Compute Unified Device Architecture (CUDA) is analyzed in order to solve transient pure advection equations. The objective is to compare it to a previous explicit version used in a Navier-Stokes solver fully written in CUDA. It turns out that BFECC could be implemented with unconditional stable stability using Semi-Lagrangian time integration allowing larger time steps than Eulerian ones
GPU-accelerated computing is becoming a popular technology due to the emergence of techniques such a...
We optimized the Arbitrary accuracy DErivatives Riemann problem (ADER) - Discontinuous Galerkin (DG)...
This webinar discusses how NVIDIA GPUs and NVIDIA CUDA can enable high-fidelity Computational Fluid ...
In the present work an implementation of the Back and Forth Error Compensation and Correction (BFECC...
Numerical integration of initial boundary problem for advection equation in 3 ℜ is considered. The m...
This dissertation introduces a new implementation of a well-known already existing algorithm, the Ba...
Real-time fluid engineering simulations require significant computational power and high-resolution ...
We implement Total Variation Diminishing Lax Friedrichs (TVDLF, or Rusanov) method to obtain numeric...
summary:We study the use of a GPU for the numerical approximation of the curvature dependent flows o...
A finite element code is developed in which all computational expensive steps are performed on a gra...
Graphics processing unit (GPU) has become a powerful computation platform not only for graphic rende...
A parallel implementation of a method of the semi-Lagrangian type for the advection equation on a hy...
In the past 15 years the field of general purpose computing on graphics processing units, or GPUs, h...
Utility of the computational power of Graphics Processing Units (GPUs) is elaborated for solutions o...
Graphical processing units (GPUs), characterized by significant computing performance, are nowadays ...
GPU-accelerated computing is becoming a popular technology due to the emergence of techniques such a...
We optimized the Arbitrary accuracy DErivatives Riemann problem (ADER) - Discontinuous Galerkin (DG)...
This webinar discusses how NVIDIA GPUs and NVIDIA CUDA can enable high-fidelity Computational Fluid ...
In the present work an implementation of the Back and Forth Error Compensation and Correction (BFECC...
Numerical integration of initial boundary problem for advection equation in 3 ℜ is considered. The m...
This dissertation introduces a new implementation of a well-known already existing algorithm, the Ba...
Real-time fluid engineering simulations require significant computational power and high-resolution ...
We implement Total Variation Diminishing Lax Friedrichs (TVDLF, or Rusanov) method to obtain numeric...
summary:We study the use of a GPU for the numerical approximation of the curvature dependent flows o...
A finite element code is developed in which all computational expensive steps are performed on a gra...
Graphics processing unit (GPU) has become a powerful computation platform not only for graphic rende...
A parallel implementation of a method of the semi-Lagrangian type for the advection equation on a hy...
In the past 15 years the field of general purpose computing on graphics processing units, or GPUs, h...
Utility of the computational power of Graphics Processing Units (GPUs) is elaborated for solutions o...
Graphical processing units (GPUs), characterized by significant computing performance, are nowadays ...
GPU-accelerated computing is becoming a popular technology due to the emergence of techniques such a...
We optimized the Arbitrary accuracy DErivatives Riemann problem (ADER) - Discontinuous Galerkin (DG)...
This webinar discusses how NVIDIA GPUs and NVIDIA CUDA can enable high-fidelity Computational Fluid ...