Edgardo Mejia-Roa, Daniel Tabas-Madrid, Javier Setoain, Carlos Garcia, Francisco Tirado, Alberto Pascual-Montano
BACKGROUND: In the last few years, the Non-negative Matrix Factorization (NMF) technique has gained a great interest among the Bioinformatics community, since it is able to extract interpretable parts from high-dimensional datasets. However, the computing time required to process large data matrices may become impractical, even for a parallel application running on a multiprocessors cluster. […]
Volodymyr Kysenko, Karl Rupp, Oleksandr Marchenko, Siegfried Selberherr, Anatoly Anisimov
An implementation of the non-negative matrix factorization algorithm for the purpose of text mining on graphics processing units is presented. Performance gains of more than one order of magnitude are obtained.
View View   Download Download (PDF)   
Yuancheng Luo, Ramani Duraiswami
We parallelize a version of the active-set iterative algorithm derived from the original works of Lawson and Hanson [Solving Least Squares Problems, Prentice-Hall, 1974] on multicore architectures. This algorithm requires the solution of an unconstrained least squares problem in every step of the iteration for a matrix composed of the passive columns of the original […]
Jukka Antikainen, Jiri Havel, Radovan Josth, Adam Herout, Pavel Zemcik, Markku Hauta-Kasari
This article presents an optimized algorithm for Nonnegative Tensor Factorization (NTF), implemented in the CUDA (Compute Uniform Device Architecture) framework, that runs on contemporary graphics processors and exploits their massive parallelism. The NTF implementation is primarily targeted for analysis of high-dimensional spectral images, including dimensionality reduction, feature extraction, and other tasks related to spectral imaging; […]
Petr Gajdos, Jan Platos, Pavel Moravec
In this paper, we describe an alternative method of the recognition of human irises with the usage of Non-Negative Matrix Factorization. The proposed method has been implemented on graphic processor unit (GPU) which makes the method usable in the real world due to short computation time.
Jan Platos, Pavel Kromer, Vaclav Snasel, Ajith Abraham
Attacks on the computer infrastructures are becoming an increasingly serious problem. Whether it is banking, e-commerce businesses, health care, law enforcement, air transportation, or education, we are all becoming increasingly reliant upon the networked computers. The possibilities and opportunities are limitless; unfortunately, so too are the risks and chances of malicious intrusions. Intrusion detection is […]
View View   Download Download (PDF)   
J. Platos, P. Gajdos
This article brings an interesting comparison of two different methods, which were implemented on GPU and help us to detect system intrusions. Generally, both of them can be widely used in the area of information retrieval. The modern trends of parallel computation have a significant influence on performance of implemented methods (Non-negative Matrix Factorization (NMF) […]
Hua Zhou, Kenneth Lange, Marc A. Suchard
This paper discusses the potential of graphics processing units (GPUs) in high-dimensional optimization problems. A single GPU card with hundreds of arithmetic cores can be inserted in a personal computer and dramatically accelerates many statistical algorithms. To exploit these devices fully, optimization algorithms should reduce to multiple parallel tasks, each accessing a limited amount of […]
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: