Affiliations: [a] Algorithms and Complexity, Department of Computer Science, University of the Philippines Diliman, Philippines. jympaul26@gmail.com, {fccabarle, hnadorna, nshernandez}@up.edu.ph | [b] Research Group on Natural Computing, Department of Computer Science and Artificial Intelligence, Universidad de Sevilla, Spain. mdelamor@us.es
Correspondence:
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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
