10387
Asgeir Bjorgan
Hyperspectral imaging with a high spatial and spectral resolution can be used to analyze materials using spectroscopic methods. This can be applied on skin as a general purpose real-time diagnostic tool. Light transport models, like the diffusion model, can describe the light propagation in tissue before the light is captured by the hyperspectral camera. The […]
Dariusz Konieczny, Karol Radziszewski
In this paper we are testing the efficiency of parallelization with use of graphic cards. There are many applications where such systems occurs in common, so we choose the domain of artificial neural networks. Actually sold graphic cards gives us strong potential in speeding up calculations and card vendors provide us with even more, giving […]
View View   Download Download (PDF)   
Revanth N R, P. J. Narayanan
Graphics models are getting increasingly bulkier with detailed geometry, textures, normal maps, etc. There is a lot of interest to model and navigate through detailed models of large monuments. Many monuments of interest have both rich detail and large spatial extent. Rendering them for navigation on a single workstation is practically impossible, even given the […]
View View   Download Download (PDF)   
Dave Cunningham, Rajesh Bordawekar, Vijay Saraswat
GPU architectures have emerged as a viable way of considerably improving performance for appropriate applications. Program fragments (kernels) appropriate for GPU execution can be implemented in CUDA or OpenCL and glued into an application via an API. While there is plenty of evidence of performance improvements using this approach, there are many issues with productivity. […]
View View   Download Download (PDF)   
Vincent Garcia, Eric Debreuve, Frank Nielsen, Michel Barlaud
The k-nearest neighbor (kNN) search problem is widely used in domains and applications such as classification, statistics, and biology. In this paper, we propose two fast GPU-based implementations of the brute-force kNN search algorithm using the CUDA and CUBLAS APIs. We show that our CUDA and CUBLAS implementations are up to, respectively, 64X and 189X […]

* * *

* * *

Like us on Facebook

HGPU group

194 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1331 peoples are following HGPU @twitter

* * *

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: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • 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: AMD APP SDK 2.9
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.2
  • SDK: nVidia CUDA Toolkit 6.0.1, AMD APP SDK 2.9

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 © 2010-2014 hgpu.org

All rights belong to the respective authors

Contact us: