<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Francis Cabarle</style></author><author><style face="normal" font="default" size="100%">Henry Adorna</style></author><author><style face="normal" font="default" size="100%">Miguel A. Martínez-del-Amor</style></author><author><style face="normal" font="default" size="100%">Mario J. Pérez-Jiménez</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Improving GPU Simulations of Spiking Neural P Systems</style></title><secondary-title><style face="normal" font="default" size="100%">Romanian Journal of Information Science and Technology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">CUDA</style></keyword><keyword><style  face="normal" font="default" size="100%">GPU Computing</style></keyword><keyword><style  face="normal" font="default" size="100%">Membrane computing</style></keyword><keyword><style  face="normal" font="default" size="100%">spiking neural network</style></keyword><keyword><style  face="normal" font="default" size="100%">spiking neural P systems</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2012</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.imt.ro/romjist/Volum15/Number15_1/cuprins15_1.htm</style></url></web-urls></urls><edition><style face="normal" font="default" size="100%">Bio-Inspired Computing – Theory and Applications (BIC-TA) - Selected papers</style></edition><publisher><style face="normal" font="default" size="100%">EDITURA ACADEMIEI ROMÂNE</style></publisher><pub-location><style face="normal" font="default" size="100%">Bucureşti, România</style></pub-location><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">5-20</style></pages><abstract><style face="normal" font="default" size="100%">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.
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