An Improved GPU Simulator For Spiking Neural P Systems

Spiking Neural P (SNP) systems, variants of Psystems (under Membrane and Natural computing), are computing models that acquire abstraction and inspiration from the way neurons 'compute' or process information. Similar to other P system variants, SNP systems are Turing complete models that by nature compute non-deterministically and in a maximally parallel manner. P systems usually trade (often exponential) space for (polynomial to constant) time. Due to this nature, P system variants are currently limited to parallel simulations, and several variants have already been simulated in parallel devices. In this paper we present an improved SNP system simulator based on graphics processing units (GPUs). Among other reasons, current GPUs are architectured for massively parallel computations, thus making GPUs very suitable for SNP system simulation. The computing model, hardware/software considerations, and simulation algorithm are presented, as well as the comparisons of the CPU only and CPU-GPU based simulators.Ministerio de Ciencia e Innovación TIN2009–13192Junta de Andalucía P08-TIC-0420

Spiking Neural P Systems with Structural Plasticity: Attacking the Subset Sum Problem

Spiking neural P systems with structural plasticity (in short, SNPSP systems) are models of computations inspired by the function and structure of biological neurons. In SNPSP systems, neurons can create or delete synapses using plasticity rules. We report two families of solutions: a non-uniform and a uniform one, to the NP-complete problem Subset Sum using SNPSP systems. Instead of the usual rule-level nondeterminism (choosing which rule to apply) we use synapse-level nondeterminism (choosing which synapses to create or delete). The nondeterminism due to plasticity rules have the following improvements from a previous solution: in our non-uniform solution, plasticity rules allowed for a normal form to be used (i.e. without forgetting rules or rules with delays, system is simple, only synapse-level nondeterminism); in our uniform solution the number of neurons and the computation steps are reduced.Ministerio de Economía y Competitividad TIN2012-3743

A Spiking Neural P System Simulator Based on CUDA

In this paper we present a Spiking Neural P system (SNP system) simulator based on graphics processing units (GPUs). In particular we implement the simulator using NVIDIA CUDA enabled GPUs. The massively parallel architecture of current GPUs is very suitable for the maximally parallel computations of SNP systems. We simulate a wider variety of SNP systems, after presenting a previous work on SNP system matrix representation which led to their simulation in GPUs, and the simulation algorithm included here. Finally, we compare and present the performance speedups of the CPU-GPU based simulator over the CPU only simulator.Ministerio de Ciencia e Innovación TIN2009–13192Junta de Andalucía P08-TIC-0420

Asynchronous Spiking Neural P Systems with Structural Plasticity

Spiking neural P (in short, SNP) systems are computing devices inspired by biological spiking neurons. In this work we consider SNP systems with structural plasticity (in short, SNPSP systems) working in the asynchronous (in short, asyn mode). SNPSP systems represent a class of SNP systems that have dynamic synapses, i.e. neurons can use plasticity rules to create or remove synapses. We prove that for asyn mode, bounded SNPSP systems (where any neuron produces at most one spike each step) are not universal, while unbounded SNPSP systems with weighted synapses (a weight associated with each synapse allows a neuron to produce more than one spike each step) are universal. The latter systems are similar to SNP systems with extended rules in asyn mode (known to be universal) while the former are similar to SNP systems with standard rules only in asyn mode (conjectured not to be universal). Our results thus provide support to the conjecture of the still open problem.Ministerio de Economía y Competitividad TIN2012-3743

On the Simulations of Evolution-Communication P Systems with Energy without Antiport Rules for GPUs

In this report, we present our initial proposal on simulating computations on a restricted variant of Evolution-Communication P system with energy (ECPe system) which will then be implemented in Graphics Processing Units (GPUs). This ECPe sys- tems variant prohibits the use of antiport rules for communication. Several possible levels of parallelizations for simulating ECPe systems computations on GPUs are emphasized. Our work is based on a localized matrix representation for the mentioned variant given in a previous literature. Our proposal employs a methodology for forward computing also discussed in the said literature.Junta de Andalucía P08-TIC04200Ministerio de Ciencia e Innovación TIN2009–1319

Improving GPU Simulations of Spiking Neural P Systems

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.Ministerio de Ciencia e Innovación TIN2009–13192Junta de Andalucía P08-TIC-0420

Spiking Neural P System Simulations on a High Performance GPU Platform

In this paper we present our results in adapting a Spiking Neural P system (SNP system) simulator to a high performance graphics processing unit (GPU) platform. In particular, we extend our simulations to larger and more complex SNP systems using an NVIDIA Tesla C1060 GPU. The C1060 is manufactured for high performance computing and massively parallel computations, matching the maximally parallel nature of SNP systems. Using our GPU accelerated simulations we present speedups of around 200× for some SNP systems, compared to CPU only simulations.Junta de Andalucía P08–TIC-04200Ministerio de Educación y Ciencia TIN2009–1319

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

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

CuSNP: Spiking Neural P Systems Simulators in CUDA

Spiking neural P systems (in short, SN P systems) are models of computation inspired by biological neurons. CuSNP is a project involving sequential (CPU) and parallel (GPU) simulators for SN P systems. In this work, we report the following results: a P-Lingua le parser is included, for ease of use when performing simulations; extension of the matrix representation of SN P systems to include delay; comparison and analysis of our simulators by simulating two types (bitonic and generalized) of parallel sorting networks; extension of supported types of regular expressions in SN P systems. Our GPU simulator is better suited for generalized sorting as compared to bitonic sorting networks, and the GPU simulators run up to 50 faster than our CPU simulator. Finally, we discuss our experiments and provide directions for further work