13588
Asier Lacasta, Carmelo Juez, Javier Murillo, Pilar García-Navarro
The movement of poorly sorted material over steep areas constitutes a hazardous environmental problem. Computational tools help in the understanding and predictions of such landslides. The main drawback is the high computational effort required for obtaining accurate numerical solutions due to the high number of cells involved in the calculus. In order to overcome this […]
View View   Download Download (PDF)   
Asier Lacasta, Mario Morales-Hernández, Javier Murillo, Pilar García-Navarro
This work is related with the implementation of a finite volume method to solve the 2D Shallow Water Equations on Graphic Processing Units (GPU). The strategy is fully oriented to work efficiently with unstructured meshes which are widely used in many fields of Engineering. Due to the design of the GPU cards, structured meshes are […]
View View   Download Download (PDF)   
C. E. Castro, J. Behrens, C. Pelties
We implement the ADER-DG numerical method using the CUDA-C language to run the code in a Graphic Processing Unit (GPU). We focus on solving linear hyperbolic partial differential equations where the method can be expressed as a combination of precomputed matrix multiplications becoming a good candidate to be used on the GPU hardware. Moreover, the […]
View View   Download Download (PDF)   
Hrag Gorune Krikor Ohannessian
We present CUDACLAW, a data-parallel solution framework for 2D and 3D hyperbolic partial differential equation (PDE) systems. CUDACLAW is a finite volume method based on time adaptive point-wise Riemann problem solvers, and can handle linear and nonlinear problems. The framework is tailored for the GPU architecture, optimized to take advantage of the powerful computational potential, […]
View View   Download Download (PDF)   
Scott Rostrup, Hans De Sterck
Increasingly, high-performance computing is looking towards data-parallel computational devices to enhance computational performance. Two technologies that have received significant attention are IBM’s Cell Processor and NVIDIA’s CUDA programming model for graphics processing unit (GPU) computing. In this paper we investigate the acceleration of parallel hyperbolic partial differential equation simulation on structured grids with explicit time […]

* * *

* * *

Follow us on Twitter

HGPU group

1752 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

371 people like HGPU on Facebook

HGPU group © 2010-2016 hgpu.org

All rights belong to the respective authors

Contact us: