GPU implementation of evolving spiking neural P systems
Rogelio V. Gungon, Katreen Kyle M. Hernandez, Francis George C. Cabarle, Ren Tristan A. de la Cruz, Henry N. Adorna, Miguel Ángel Martínez del Amor, David Orellana-Martín, Ignacio Pérez–Hurtado
Abstract
Methods for optimizing and evolving spiking neural P systems (in short, SN P systems) have been previously developed with the use of a genetic algorithm framework. So far, these computations, both evolving and simulating, were done only sequentially. Due to the non-deterministic and parallel nature of SN P systems, it is natural to harness parallel processors in implementing its evolution and simulation. In this work, a parallel framework for the evolution of SN P Systems is presented. This is the result of extending our previous work by implementing it on a CUDA-enabled graphics processing unit and adapting CuSNP design in simulations. Using binary addition and binary subtraction with 3 different categories each as initial SN P systems, the GPU-based evolution runs up to 9x faster with respect to its CPU-based evolution counterparts. Overall, when considering the whole process, the GPU framework is up to 3 times faster than the CPU version.