Exploring the Contrast on GPGPU Computing through CUDA and OpenCL

2014 ◽  
Vol 9 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Bala Dhandayuthapani Veerasamy ◽  
G.M. Nasira
Keyword(s):  
Gpgpu Computing

scholarly journals GPGPU Computing and Visualization on GPUs at LANL

2012 ◽  
Author(s):  
Laura M. Monroe
Keyword(s):  
Gpgpu Computing

212 Development of a numerical model for blood flow in microvessels using GPGPU computing

2012 ◽  
Vol 2012.47 (0) ◽  
pp. 230-231
Author(s):  
Naoki Takeishi ◽  
Yohsuke IMAI ◽  
Keita NAKAAKI ◽  
Takuji ISHIKAWA ◽  
Takami YAMAGUCHI
Keyword(s):  
Blood Flow ◽  
Numerical Model ◽  
Gpgpu Computing

A Survey of General Purpose Computation of GPU for Computational Fluid Dynamics

2013 ◽  
Vol 753-755 ◽  
pp. 2731-2735
Author(s):  
Wei Cao ◽  
Zheng Hua Wang ◽  
Chuan Fu Xu
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
High Performance ◽  
Stokes Equations ◽  
Gpu Computing ◽  
Graphics Processing Unit ◽  
General Purpose ◽  
Navier Stokes ◽  
Processing Unit ◽  
Gpgpu Computing

The graphics processing unit (GPU) has evolved from configurable graphics processor to a powerful engine for high performance computer. In this paper, we describe the graphics pipeline of GPU, and introduce the history and evolution of GPU architecture. We also provide a summary of software environments used on GPU, from graphics APIs to non-graphics APIs. At last, we present the GPU computing in computational fluid dynamics applications, including the GPGPU computing for Navier-Stokes equations methods and the GPGPU computing for Lattice Boltzmann method.

scholarly journals MULTIRESOLUTION RENDERING BASED ON GPGPU COMPUTING

2014 ◽  
pp. 298-307
Author(s):  
Julián Lamas-Rodríguez ◽  
Francisco Argüello ◽  
Dora B. Heras
Keyword(s):  
Volume Rendering ◽  
Texture Mapping ◽  
Gpgpu Computing ◽  
Visualization Process ◽  
Resolution Level ◽  
Data Transfers ◽  
Multiresolution Rendering ◽  
Different Levels

The problem of visualizing large volumetric datasets is appealing for computation on the GPU. Nevertheless, the design of GPU volume rendering solutions must deal with the limited available memory in a graphics card. In this work, we present a system for multiresolution volume rendering which preprocesses the dataset dividing it into bricks and generating a compressed version by applying different levels of compression based on wavelets. The compressed volume is then stored in the GPU memory. For the later visualization process by texture mapping each brick of the volume is decompressed and rendered with a different resolution level depending on its distance to the camera. This approach computes most of the tasks in the GPU, thus minimizing the data transfers among CPU and GPU. We obtain competitive results for volumes of size in the range between 64 and 256.

Solving time-invariant differential matrix Riccati equations using GPGPU computing

2014 ◽  
Vol 70 (2) ◽  
pp. 623-636
Author(s):  
Jesús Peinado ◽  
Pedro Alonso ◽  
Javier Ibáñez ◽  
Vicente Hernández ◽  
Murilo Boratto
Keyword(s):  
Riccati Equations ◽  
Gpgpu Computing ◽  
Invariant Differential ◽  
Time Invariant ◽  
Differential Matrix
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