Minho Kim, Jorg Peters
In “A realtime GPU subdivision kernel” (SIGGRAPH 2005), Shiue et al. showed that, in principle, all major features of subdivision algorithms can be realized in the framework of highly parallel stream processing. Shiue et al. tested the approach by implementing Catmull-Clark subdivision, with semi-smooth creases and global boundaries, in programmable graphics hardware, at near realtime […]
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Matthew James Mawson Breeze
To explore progressively larger biomolecular systems, methods to model explicit solvent cheaply are required. In this work, the use of Graphics Processing Units, found in commodity video cards, for solving the constraints, calculating the non-bonded forces and generating the pair list in the case of the fully constrained three site SPC water model is investigated. […]
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Zhongwen Luo, Hongzhi Liu, Xincai Wu
Artificial neural network (ANN) is widely used in pattern recognition related area. In some case, the computational load is very heavy, in other case, real time process is required. So there is a need to apply a parallel algorithm on it, and usually the computation for ANN is inherently parallel. In this paper, graphic hardware […]
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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

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