M. Kachelriess, M. Knaup, O. Bockenbach
Tomographic image reconstruction, such as the reconstruction of CT projection values, of tomosynthesis data, PET or SPECT events, is computational very demanding. The most time-consuming step is the backprojection which is often limited by the memory bandwidth. Recently, a novel general purpose architecture optimized for distributed computing became available: the Cell Broadband Engine (CBE). Its […]
View View   Download Download (PDF)   
Fang Xu, K. Mueller
The task of reconstructing an object from its projections via tomographic methods is a time-consuming process due to the vast complexity of the data. For this reason, manufacturers of equipment for medical computed tomography (CT) rely mostly on special application specified integrated circuits (ASICs) to obtain the fast reconstruction times required in clinical settings. Although […]
View View   Download Download (PDF)   
Tai-Pang Wu, Kam-Lun Tang, Chi-Keung Tang, Tien-Tsin Wong
We address the problem of robust normal reconstruction by dense photometric stereo, in the presence of complex geometry, shadows, highlight, transparencies, variable attenuation in light intensities, and inaccurate estimation in light directions. The input is a dense set of noisy photometric images, conveniently captured by using a very simple set-up consisting of a digital video […]
View View   Download Download (PDF)   
C.E. Scheidegger, J.L.D. Comba, R.D. da Cunha
Modern programmable graphics hardware offers sufficient computing power to suggest the implementation of traditional algorithms on the graphics processor. This paper describes a complete implementation of a standard technique to solve the incompressible Navier-Stokes fluid equations running entirely on the GPU: the SMAC (simplified marker and cell) method. This method is widely used in engineering […]
Marko Durkovic,Michael Zwick,Florian Obermeier,Klaus Diepold
Since graphics cards have become programmable the recent years, numerous computationally intensive algorithms have been implemented on the now called general purpose graphics processing units (GPGPUs). While the results show that GPGPUs regularly outperform CPU based implementations, the question arose how optical flow algorithms can be ported to graphics hardware. To answer the question, the […]
View View   Download Download (PDF)   
Carsten Stoll, Stefan Gumhold, Hans-Peter Seidel
Line primitives are a very powerful visual attribute used for scientific visualization and in particular for 3D vector-field visualization. We extend the basic line primitives with additional visual attributes including color, line width, texture and orientation. To implement the visual attributes we represent the stylized line primitives as generalized cylinders. One important contribution of our […]
View View   Download Download (PDF)   
Carlos Eduardo Scheidegger, Joao L.D. Comba, Rudnei D. Da Cunha
The explosive growth in integration technology and the parallel nature of rasterization-based graphics APIs (Application Programming Interface) changed the panorama of consumer-level graphics: today, GPUs (Graphics Processing Units) are cheap, fast and ubiquitous. We show how to harness the computational power of GPUs and solve the incompressible Navier-Stokes fluid equations significantly faster (more than one […]
Jiajun Bu, Mingli Song, Qi Wu, Chun Chen, Cheng Jin
In this paper, a novel system is proposed to recognize facial expression based on face sketch, which is produced by programmable graphics hardware-GPU(Graphics Processing Unit). Firstly, an expression subspace is set up from a corpus of images consisting of seven basic expressions. Secondly, by applying a GPU based edge detection algorithm, the real-time facial expression […]
View View   Download Download (PDF)   
Alexander Bogomjakov, Craig Gotsman
Height fields and depth maps which we collectively refer to as z-fields, usually carry a lot of redundant information and are often used in real-time applications. This is the reason why efficient methods for their simplification are necessary. On the other hand, the computation power and programmability of commodity graphics hardware has significantly grown. We […]
View View   Download Download (PDF)   
Harlen Costa Batagelo, Shin-Ting Wu
We present a simple yet effective snapping technique for constraining the motion of the cursor of an input device to the surface of 3D models whose geometry is arbitrarily deformed by a programmable hardware fragment and vertex processor. The technique works in image space and thus snaps the cursor to the geometry actually rendered instead […]
View View   Download Download (PDF)   
Wesley De Neve, Dieter Van Rijsselbergen, Charles Hollemeersch, Jan De Cock, Stijn Notebaert, Rik Van de Walle
Although pixel shaders were designed for the creation of programmable rendering effects, they can also be used as generic processing units for vector data. In this paper, attention is paid to an implementation of the YCoCg-R to RGB color space transform, as defined in the H.264/AVC Fidelity Range Extensions, by making use of pixel shaders. […]
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: