Carandang, Jym PaulCabarle, Francis George C.Adorna, Henry NatividadHernandez, Nestine Hope S.Martínez del Amor, Miguel Ángel2025-03-252025-03-252019Carandang, J.P., Cabarle, F.G.C., Adorna, H.N., Hernandez, N.H.S. y Martínez del Amor, M.Á. (2019). Handling Non-determinism in Spiking Neural P Systems: Algorithms and Simulations. Fundamenta Informaticae, 164 (2-3), 139-155. https://doi.org/10.3233/FI-2019-1759.0169-29681875-8681https://hdl.handle.net/11441/170814Spiking 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 ac celerating 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 sys tems 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.application/pdf17 p.engAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Membrane ComputingSpiking Neural P systemMatrix RepresentationCUDAGPUSubset Suminfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccesshttps://doi.org/10.3233/FI-2019-1759