Simulating Spiking Neural P Systems Without Delays Using GPUs

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Author(s): F. Cabarle (University of the Philippines Diliman, Philippines), H. Adorna (University of the Philippines Diliman, Philippines)and M. A. Martínez-del-Amor (University of Seville, Spain)
Copyright: 2014
Pages: 13
Source title: Natural Computing for Simulation and Knowledge Discovery
Source Author(s)/Editor(s): Leandro Nunes de Castro (Mackenzie University, Brazil)
DOI: 10.4018/978-1-4666-4253-9.ch006

Keywords: Biologically-Inspired Computing / Biotechnology and Bioinformatics / Engineering Science Reference / Medicine & Healthcare

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Abstract

In this paper, the authors discuss the simulation of a P system variant known as Spiking Neural P systems (SNP systems), using Graphics Processing Units (GPUs). GPUs are well suited for highly parallel computations because of their intentional and massively parallel architecture. General purpose GPU computing has seen the use of GPUs for computationally intensive applications, not just in graphics and video processing. P systems, including SNP systems, are maximally parallel computing models taking inspiration from the functioning and dynamics of a living cell. In particular, SNP systems take inspiration from a type of cell known as a neuron. The nature of SNP systems allowed for their representation as matrices, which is an elegant step toward their simulation on GPUs. In this paper, the simulation algorithms, design considerations, and implementation are presented. Finally, simulation results, observations, and analyses using a simple but non-trivial SNP system as an example are discussed, including recommendations for future work.

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Simulating Spiking Neural P Systems Without Delays Using GPUs

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