Improving Simulations of Spiking Neural P Systems in NVIDIA CUDA GPUs: CuSNP

Description

Spiking neural P systems (in short, SN P systems) are parallel models of computations inspired by the spiking ( ring) of biological neurons. In SN P systems, neurons function as spike processors and are placed on nodes of a directed graph. Synapses, the connections between neurons, are represented by arcs or directed endges in the graph. Not only do SN P systems have parallel semantics (i.e. neurons operate in parallel), but their structure as directed graphs allow them to be represented as vectors or matrices. Such representations allow the use of linear algebra operations for simulating the evolution of the system con gurations, i.e. computations. In this work, we continue the implementations of SN P systems with delays, i.e. a delay is associated with the sending of a spike from a neuron to its neighbouring neurons. Our implementation is based on a modi ed representation of SN P systems as vectors and matrices for SN P systems without delays. We us massively parallel processors known as graphics processing units (in short, GPUs) from NVIDIA. For experimental validation, we use SN P systems implementing generalized sorting networks. We report a speedup, i.e. the ratio between the running time of the sequential over the parallel simulator, of up to approximately 51 times for a 512-size input to the sorting network.

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