Implementation on CUDA of the Smoothing Problem with Tissue-Like P Systems

Implementation on CUDA of the Smoothing Problem with Tissue-Like P Systems

Francisco Peña-Cantillana (University of Sevilla, Spain), Daniel Díaz-Pernil (University of Sevilla, Spain), Hepzibah A. Christinal (University of Sevilla, Spain & Karunya University, India) and Miguel A. Gutiérrez-Naranjo (University of Sevilla, Spain)
Copyright: © 2014 |Pages: 10
DOI: 10.4018/978-1-4666-4253-9.ch012


Smoothing is often used in Digital Imagery for improving the quality of an image by reducing its level of noise. This paper presents a parallel implementation of an algorithm for smoothing 2D images in the framework of Membrane Computing. The chosen formal framework has been tissue-like P systems. The algorithm has been implemented by using a novel device architecture called CUDA (Compute Unified Device Architecture) which allows the parallel NVIDIA Graphics Processors Units (GPUs) to solve many complex computational problems. Some examples are presented and compared; research lines for the future are also discussed.
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2. Preliminaries

In this section we provide some basics on the used P system model, tissue-like P systems, and on the foundation of Digital Imagery.

Tissue-like P systems (Martín-Vide, Păun, Pazos, & Rodríguez-Patón, 2003) have two biological inspirations: intercellular communication and cooperation between neurons. The common mathematical model of these two mechanisms is a network of processors dealing with symbols and communicating these symbols along channels specified in advance.

Formally, a tissue-like P system with input of degree q≥1 is a tuple∏=(Γ, Σ, E, w1,…,wq, R, iΠ, oΠ)where

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