Artículo
CuSNP: Spiking Neural P Systems Simulators in CUDA
Autor/es | Carandang, Jym Paul
Villaflores, John Matthew B. Cabarle, Francis George C. Adorna, Henry N. Martínez del Amor, Miguel Ángel |
Departamento | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial |
Fecha de publicación | 2017 |
Fecha de depósito | 2021-03-18 |
Publicado en |
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Resumen | Spiking neural P systems (in short, SN P systems) are models
of computation inspired by biological neurons. CuSNP is a project involving
sequential (CPU) and parallel (GPU) simulators for SN P systems. In this
work, we ... Spiking neural P systems (in short, SN P systems) are models of computation inspired by biological neurons. CuSNP is a project involving sequential (CPU) and parallel (GPU) simulators for SN P systems. In this work, we report the following results: a P-Lingua le parser is included, for ease of use when performing simulations; extension of the matrix representation of SN P systems to include delay; comparison and analysis of our simulators by simulating two types (bitonic and generalized) of parallel sorting networks; extension of supported types of regular expressions in SN P systems. Our GPU simulator is better suited for generalized sorting as compared to bitonic sorting networks, and the GPU simulators run up to 50 faster than our CPU simulator. Finally, we discuss our experiments and provide directions for further work. |
Cita | Carandang, J.P., Villaflores, J.M.B., Cabarle, F.G.C., Adorna, H.N. y Martínez del Amor, M.Á. (2017). CuSNP: Spiking Neural P Systems Simulators in CUDA. Romanian Journal of Information Science and Technology (ROMJIST), 20 (1), 57-70. |
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