GPU implementation of evolving spiking neural P systems

• Published in: Neurocomputing (Amsterdam) Vol. 503; pp. 140 - 161
• Main Authors: Gungon, Rogelio V., Hernandez, Katreen Kyle M., Cabarle, Francis George C., de la Cruz, Ren Tristan A., Adorna, Henry N., Martínez-del-Amor, Miguel Á., Orellana-Martín, David, Pérez-Hurtado, Ignacio
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 07-09-2022
• Subjects: CUDA; Evolutionary computing; Genetic algorithm; GPU computing; Membrane computing; Spiking neural P systems; CUDA; Spiking neural P systems; Evolutionary computing; Membrane computing; Genetic algorithm; GPU computing
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2022.06.094

•A novel parallel framework for evolving spiking neural P systems.•GPU implementation of the genetic algorithm employed in the parallel framework.•Adapting CuSNP design for the efficient simulation of spiking neural P systems in the parallel framework.•Reporting up to 9x of speedup of the GPU implementation versus the CPU counterpart for the parallel step, and up to 3x for the overall process.
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.