Peter Mileff, Judit Dudra
The graphics processing unit (GPU) has become part of our everyday life through desktop computers and portable devices (tablets, mobile phones, etc.). Because of the dedicated hardware visualization has been significantly accelerated and today’s software uses only the GPU for rasterization. Besides the graphical devices, the central processing unit (CPU) has also made remarkable progress. […]
View View   Download Download (PDF)   
Jan Vanek, Jan Trmal, Josef V. Psutka, Josef Psutka
Gaussian mixture models (GMMs) are often used in various data processing and classification tasks to model a continuous probability density in a multi-dimensional space. In cases, where the dimension of the feature space is relatively high (e.g. in the automatic speech recognition (ASR)), GMM with a higher number of Gaussians with diagonal covariances (DC) instead […]
View View   Download Download (PDF)   
Peter Mileff, Judit Dudra
The market of computer graphics is dominated by GPU based technologies. However today’s fast central processing units (CPU) based on modern architectural design offer new opportunities in the field of classical software rendering. Because the technological development of the GPU architecture almost reached the limits in the field of the programming model, the CPU-based solutions […]
View View   Download Download (PDF)   
Kyle Spafford, Jeremy S. Meredith, Seyong Lee, Dong Li, Philip C. Roth, Jeffrey S. Vetter
With the rise of general purpose computing on graphics processing units (GPGPU), the influence from consumer markets can now be seen across the spectrum of computer architectures. In fact, many of the high-ranking Top500 HPC systems now include these accelerators. Traditionally, GPUs have connected to the CPU via the PCIe bus, which has proved to […]
View View   Download Download (PDF)   
Marries van de Hoef, Bas Zalmstra
We developed a very fast reprojection technique to generate stereoscopic images from a 2D image with depth information. The technique is gather-based and therefore very fast on current graphics hardware. The depth information is sampled at a specific offset which provides the depth to reproject from the left or right camera to the center camera. […]

* * *

* * *

Follow us on Twitter

HGPU group

1660 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

334 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: