Over the past decade there has been a growing interest in the development of parallel hardware systems for simulating large-scale networks of spiking neurons. Compared to other highly-parallel systems, GPU-accelerated solutions have the advantage of a relatively low cost and a great versatility, thanks also to the possibility of using the CUDA-C/C++ programming languages. NeuronGPU is a GPU library for large-scale simulations of spiking neural network models, written in the C++ and CUDA-C++ programming languages, based on a novel spike-delivery algorithm. This library includes simple LIF (leaky-integrate-and-fire) neuron models as well as several multisynapse AdEx (adaptive-exponential-integrate-and-fire) neuron models with current or conductance based synapses, user definable models and different devices. The numerical solution of the differential equations of the dynamics of the AdEx models is performed through a parallel implementation, written in CUDA-C++, of the fifth-order Runge-Kutta method with adaptive step-size control. In this work we evaluate the performance of this library on the simulation of a well-known cortical microcircuit model, based on LIF neurons and current-based synapses, and on a balanced networks of excitatory and inhibitory neurons, using AdEx neurons and conductance-based synapses. On these models, we will show that the proposed library achieves state-of-the-art performance in terms of simulation time per second of biological activity. In particular, using a single NVIDIA GeForce RTX 2080 Ti GPU board, the full-scale cortical-microcircuit model, which includes about 77,000 neurons and $3 cdot 10^8$ connections, can be simulated at a speed very close to real time, while the simulation time of a balanced network of 1,000,000 AdEx neurons with 1,000 connections per neuron was about 70 s per second of biological activity.

Fast simulations of highly-connected spiking cortical models using GPUs / Golosio, Bruno; Tiddia, Gianmarco; DE LUCA, Chiara; Pastorelli, Elena; Simula, Francesco; Stanislao Paolucci, Pier. - In: FRONTIERS IN COMPUTATIONAL NEUROSCIENCE. - ISSN 1662-5188. - 15:(2021). [10.3389/fncom.2021.627620]

Fast simulations of highly-connected spiking cortical models using GPUs

Chiara De Luca
Membro del Collaboration Group
;
Elena Pastorelli
Membro del Collaboration Group
;
2021

Abstract

Over the past decade there has been a growing interest in the development of parallel hardware systems for simulating large-scale networks of spiking neurons. Compared to other highly-parallel systems, GPU-accelerated solutions have the advantage of a relatively low cost and a great versatility, thanks also to the possibility of using the CUDA-C/C++ programming languages. NeuronGPU is a GPU library for large-scale simulations of spiking neural network models, written in the C++ and CUDA-C++ programming languages, based on a novel spike-delivery algorithm. This library includes simple LIF (leaky-integrate-and-fire) neuron models as well as several multisynapse AdEx (adaptive-exponential-integrate-and-fire) neuron models with current or conductance based synapses, user definable models and different devices. The numerical solution of the differential equations of the dynamics of the AdEx models is performed through a parallel implementation, written in CUDA-C++, of the fifth-order Runge-Kutta method with adaptive step-size control. In this work we evaluate the performance of this library on the simulation of a well-known cortical microcircuit model, based on LIF neurons and current-based synapses, and on a balanced networks of excitatory and inhibitory neurons, using AdEx neurons and conductance-based synapses. On these models, we will show that the proposed library achieves state-of-the-art performance in terms of simulation time per second of biological activity. In particular, using a single NVIDIA GeForce RTX 2080 Ti GPU board, the full-scale cortical-microcircuit model, which includes about 77,000 neurons and $3 cdot 10^8$ connections, can be simulated at a speed very close to real time, while the simulation time of a balanced network of 1,000,000 AdEx neurons with 1,000 connections per neuron was about 70 s per second of biological activity.
2021
spiking neural network simulator; cortical microcircuits; adaptive exponential integrate-and-fire neuron model; conductance-based synapses; GPU
01 Pubblicazione su rivista::01a Articolo in rivista
Fast simulations of highly-connected spiking cortical models using GPUs / Golosio, Bruno; Tiddia, Gianmarco; DE LUCA, Chiara; Pastorelli, Elena; Simula, Francesco; Stanislao Paolucci, Pier. - In: FRONTIERS IN COMPUTATIONAL NEUROSCIENCE. - ISSN 1662-5188. - 15:(2021). [10.3389/fncom.2021.627620]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1491772
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