Mohammad Zubair Ahmad
The Internet ecosystem comprising of thousands of Autonomous Systems (ASes) now include Internet eXchange Points (IXPs) as another critical component in the infrastructure. Peering plays a significant part in driving the economic growth of ASes and is contributing to a variety of structural changes in the Internet. IXPs are a primary component of this peering […]
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Chatura A. Ettigi
The goal of this project is to demonstrate Parallel Programming on a GPU using the latest Intel technology called Intel Array Building Blocks (Intel ArBB). The main aim is to describe the programming model of Intel ArBB and show effectiveness of the new technology, Intel ArBB on a GPU environment using examples. Parallel Programming is […]
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Frederic Champagnat, Yves Le Sant
Application of geometric transformation to images requires an interpolation step. When applied to image rotation, the presently most efficient GPU implementation for the cubic spline image interpolation still cost about 10 times as much as linear interpolation. This implementation involves two steps: a prefilter step performs a two-pass forward-backward recursive filter, then a cubic polynomial […]
Jihun Oh, Diego Martin, Oskar Skrinjar
Clinical diagnosis and quantification of liver disease have been improved through the development of techniques using contrast-enhanced liver MRI sequences. To qualitatively or quantitatively analyze such image sequences, one first needs to correct for rigid and non-rigid motion of the liver. For motion correction of the liver, we have employed bi-directional local correlation coefficient Demons, […]
Bodam Nam, Sung-il Kang, Hyunki Hong
This paper presents a novel Graphics Processing Unit (GPU)-based system for pedestrian detection with stereo vision in real images on mobile robot. The process of obtaining a dense disparity map on a GPU for real-time applications and the edge property of the scene to extract a region of interest (ROI) is designed. After extracting the […]
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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.

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