Artículo
Improving GPU Simulations of Spiking Neural P Systems
Autor/es | Cabarle, Francis George C.
Adorna, Henry N. Martínez del Amor, Miguel Ángel Pérez Jiménez, Mario de Jesús |
Departamento | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial |
Fecha de publicación | 2012 |
Fecha de depósito | 2018-10-30 |
Publicado en |
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Resumen | In this work we present further extensions and improvements
of a Spiking Neural P system (for short, SNP systems) simulator on graphics
processing units (for short, GPUs). Using previous results on representing SNP
system ... In this work we present further extensions and improvements of a Spiking Neural P system (for short, SNP systems) simulator on graphics processing units (for short, GPUs). Using previous results on representing SNP system computations using linear algebra, we analyze and implement a compu- tation simulation algorithm on the GPU. A two-level parallelism is introduced for the computation simulations. We also present a set of benchmark SNP sys- tems to stress test the simulation and show the increased performance obtained using GPUs over conventional CPUs. For a 16 neuron benchmark SNP system with 65536 nondeterministic rule selection choices, we report a 2.31 speedup of the GPU-based simulations over CPU-based simulations. |
Agencias financiadoras | Ministerio de Ciencia e Innovación (MICIN). España Junta de Andalucía |
Identificador del proyecto | TIN2009-13192
P08-TIC-04200 |
Cita | Cabarle, F.G.C., Adorna, H.N., Martínez del Amor, M.Á. y Pérez Jiménez, M.d.J. (2012). Improving GPU Simulations of Spiking Neural P Systems. Romanian Journal of Information Science and Technology, 15 (1), 5-20. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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01-Cabarle.pdf | 297.9Kb | [PDF] | Ver/ | |