Performance Analysis on Several GPU Architectures of an Algorithm for Noise Removal

M. G. Sanchez, V. Vidal, J. Bataller, G. Verdu
Department of Systems and Computing, Instituto Tecnologico de Cd. Guzman, Cd. Guzman, Jalisco, Mexico
The 2012 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA’12), 2012

   title={Performance Analysis on Several GPU Architectures of an Algorithm for Noise Removal},

   author={S{‘a}nchez, MG and Vidal, V. and Bataller, J. and Verd{‘u}, G.},



Download Download (PDF)   View View   Source Source   
In this paper, we present an efficient implementation of parallel algorithms to remove noise in digital images using different Graphics Processing Units (GPUs). The algorithm, based on the concept of peer group, uses a fuzzy metric for finding wrong pixels and the Arithmetic Mean Filter (AMF) to correct it. There are many factors to study in order to get an optimal implementation of an algorithm on a GPU. Our algorithm has been implemented with two different approaches to access the data: Shared and Texture memory. Also, the number of threads and its arrangement have been studied. The test has been conducted on two different cards: Tesla-Fermi and GeForce.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

You must be logged in to post a comment.

* * *

* * *

* * *

Free GPU computing nodes at

Registered users can now run their OpenCL application at 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 11.4
  • SDK: AMD APP SDK 2.8
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 5.0.35, AMD APP SDK 2.8

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 will be treated according to our Privacy Policy

HGPU group © 2010-2014

All rights belong to the respective authors

Contact us: