Adam Kull
Graphics processors of today are highly efficient, parallel processors, capable of rendering complex scenes consisting of millions of polygons on the screen each and every second. They are highly specialized towards game graphics and similar, polygon based graphics. In the past, however, they have not been very efficient at rendering volumetric data, and especially not […]
View View   Download Download (PDF)   
Wolfgang Fohl, Julian Dessecker
A plugin system for GPGPU real time audio effect calculation on the graphics processing unit of the computer system is presented. The prototype application is the rendering of mono audio material with head-related transfer functions (HRTFs) to create the impression of a sound source located in a certain direction relative to the listener’s head. The […]
View View   Download Download (PDF)   
Gallagher Pryor, Brett Lucey, Sandeep Maddipatla, Chris McClanahan, John Melonakos, Vishwanath Venugopalakrishnan, Krunal Patel, Pavan Yalamanchili, James Malcolm
We describe a software platform for the rapid development of general purpose GPU (GPGPU) computing applications within the MATLAB computing environment, C, and C++: Jacket. Jacket provides thousands of GPU-tuned function syntaxes within MATLAB, C, and C++, including linear algebra, convolutions, reductions, and FFTs as well as signal, image, statistics, and graphics libraries. Additionally, Jacket […]
Ahmed F. Elnokrashy, Ahmed A. Elmalky, Tamer M. Hosny, Marwan Abd Ellah, Alaa Megawer, Abobakr Elsebai, Abou-Bakr M. Youssef, Yasser M. Kadah
Due to the required computational effort of 4D ultrasound imaging, such systems depend on low complexity techniques like nearest neighbor interpolation, which affects volume quality. Moreover, more accurate techniques like normalized convolution, backward trilinear interpolation, and forward spherical and ellipsoidal Gaussian kernel, are avoided in real-time imaging because of the tight reconstruction time. The goal […]
View View   Download Download (PDF)   
Balazs Toth, Milan Magdics
This paper presents a fast parallel Monte Carlo method to solve the radiative transport equation in inhomogeneous participating media. The implementation is based on CUDA and runs on the GPU. In order to meet the requirements of the parallel GPU architecture and to reuse shooting paths, we follow a photon mapping approach where during gathering […]
View View   Download Download (PDF)   

* * *

* * *

Follow us on Twitter

HGPU group

1658 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

335 people like HGPU on Facebook

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: nVidia CUDA Toolkit 6.5.14, AMD APP SDK 3.0
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.3
  • SDK: AMD APP SDK 3.0

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2015 hgpu.org

All rights belong to the respective authors

Contact us: