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Issue title: Bio-Inspired Computing: Theories and Applications (BIC-TA 2017)
Guest editors: Linqiang Pan, Mario J. Pérez-Jiménez and Gexiang Zhang
Article type: Research Article
Authors: Carandang, Jym Paula | Cabarle, Francis George C.a; * | Adorna, Henry Natividada | Hernandez, Nestine Hope S.a | Martínez-del-Amor, Miguel Ángelb
Affiliations: [a] Algorithms and Complexity, Department of Computer Science, University of the Philippines Diliman, Philippines. [email protected], {fccabarle, hnadorna, nshernandez}@up.edu.ph | [b] Research Group on Natural Computing, Department of Computer Science and Artificial Intelligence, Universidad de Sevilla, Spain. [email protected]
Correspondence: [*] Address for correspondence: Algorithms and Complexity, Department of Computer Science, University of the Philippines Diliman, Diliman 1101 Quezon City, Philippines.
Abstract: Spiking Neural P system is a computing model inspired on how the neurons in a living being are interconnected and exchange information. As a model in embrane computing, it is a non-deterministic and massively-parallel system. The latter makes GPU a good candidate for accelerating the simulation of these models. A matrix representation for systems with and without delay have been previously designed, and algorithms for simulating them with deterministic systems was also developed. So far, non-determinism has been problematic for the design of parallel simulators. In this work, an algorithm for simulating non-deterministic spiking neural P system with delays is presented. In order to study how the simulations get accelerated on a GPU, this algorithm was implemented in CUDA and used to simulate non-uniform and uniform solutions to the Subset Sum problem as a case study. The analysis is completed with a comparison of time and space resources in the GPU of such simulations.
Keywords: Membrane Computing, Spiking Neural P systems, Matrix Representation, CUDA, GPU, Subset Sum
DOI: 10.3233/FI-2019-1759
Journal: Fundamenta Informaticae, vol. 164, no. 2-3, pp. 139-155, 2019
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