Evaluation of parallel paradigms on anisotropic nonlinear diffusion

Authors:
S. Tabik;E. M. Garzón;I. García;J. J. Fernández
Affiliations:
Dept. Computer Architecture and Electronics, University of Almería, Almería, Spain;Dept. Computer Architecture and Electronics, University of Almería, Almería, Spain;Dept. Computer Architecture and Electronics, University of Almería, Almería, Spain;Dept. Computer Architecture and Electronics, University of Almería, Almería, Spain
Venue:
Euro-Par'06 Proceedings of the 12th international conference on Parallel Processing
Year:
2006

Citing 10
Cited 0

Numerical recipes: the art of scientific computing

Numerical recipes: the art of scientific computing
Scale-Space and Edge Detection Using Anisotropic Diffusion

IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear filtering of magnetic resonance tomograms by geometry-driven diffusion

Machine Vision and Applications
A Fundamental Relationship between Bilateral Filtering, Adaptive Smoothing, and the Nonlinear Diffusion Equation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Coherence-Enhancing Diffusion Filtering

International Journal of Computer Vision
Superlinear Performance in Real-Time Parallel Computation

The Journal of Supercomputing
Three-dimensional reconstruction of cellular structures by electron microscope tomography and parallel computing

Journal of Parallel and Distributed Computing
Performance Evaluation of the SGI Altix 3700

ICPP '05 Proceedings of the 2005 International Conference on Parallel Processing
Anisotropic Nonlinear Filtering of Cellular Structures in Cryoelectron Tomography

Computing in Science and Engineering
Efficient and reliable schemes for nonlinear diffusion filtering

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Anisotropic Nonlinear Diffusion (AND) is a powerful noise reduction technique in the field of computer vision. This method is based on a Partial Differential Equation (PDE) tightly coupled with a massive set of eigensystems. Denoising large 3D images in biomedicine and structural cellular biology by AND is extremely expensive from a computational point of view, and the requirements may become so huge that parallel computing turns out to be essential. This work addresses the parallel implementation of AND. The parallelization is carried out by means of three paradigms: (1) Shared address space paradigm, (2) Message passing paradigm, and (3) Hybrid paradigm. The three parallel approaches have been evaluated on two parallel platforms: (1) a DSM (Distributed Shared Memory) platform based on cc-NUMA memory access and (2) a cluster of Symmetric biprocessors. An analysis of the performance of the three strategies has been accomplished to determine which is the most suitable paradigm for each platform.