Fast Simulation of a Multi-Area Spiking Network Model of Macaque Cortex on an MPI-GPU Cluster

The reproduction of the brain ’sactivity and its functionality is the main goal of modern neuroscience. To this aim, several models have been proposed to describe the activity of single neurons at different levels of detail. Then, single neurons are linked together to build a network, in order to reproduce complex behaviors. In the literature, different network-building rules and models have been described, targeting realistic distributions and connections of the neurons. In particular, the Granular layEr Simulator (GES) performs the granular layer network reconstruction considering biologically realistic rules to connect the neurons. Moreover, it simulates the network considering the Hodgkin–Huxley model. The work proposed in this paper adopts the network reconstruction model of GES and proposes a simulation module based on Leaky Integrate and Fire (LIF) model. This simulator targets the reproduction of the activity of large scale networks, exploiting the GPU technology to reduce the processing times. Experimental results show that a multi-GPU system reduces the simulation of a network with more than 1.8 million neurons from approximately 54 to 13 h.

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“…Authors of [ 30 ] reproduced the activity of 32 mm

of a macaque cortex on 32 Tesla V100 GPUs. The parallel implementation achieved a speed-up of about 3 compared to the NEST simulator baseline.…”

Section: Resultsmentioning

confidence: 99%

Towards the Simulation of a Realistic Large-Scale Spiking Network on a Desktop Multi-GPU System

et al. 2022

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“…found that NEST simulation on the JURECA system (Thörnig, 2021) at the Jülich Supercomputing Centre was ≈ 15 times faster than simulation with GeNN on a single GPU (NVIDIA TITAN RTX). The recent work by Tiddia et al (2022) found that NEST GPU (parallel simulation on 32 GPUs, NVIDIA V100 GPU with 16 GB HBM2e) outperformed NEST (simulated on JUSUF HPC cluster Von St. Vieth (2021) and JURECA) by at least a factor of two. Kurth et al (2022) reported the real-time factor for the multi-layered model of a single cortical column introduced by Potjans and Diesmann (2014) with about N = 80, 000 neurons and 0, 3 • 10 9 synapses for a simulation with GeNN as RT = 0.7 (NVIDIA Titan RTX) and with NEST as RT = 0, 56 (cluster with 2 dual-processor machines with 128 cores each).…”

Section: Discussionmentioning

confidence: 99%

“…Knight and Nowotny (2021) found that NEST simulation on the JURECA system (Thörnig, 2021) at the Jülich Supercomputing Centre was ≈ 15 times faster than simulation with GeNN on a single GPU (NVIDIA TITAN RTX). The recent work by Tiddia et al (2022) found that NEST GPU (parallel simulation on 32 GPUs, NVIDIA V100 GPU with 16 GB HBM2e) outperformed NEST (simulated on JUSUF HPC cluster Von St. Vieth (2021) and JURECA) by at least a factor of two.…”

Section: Discussionmentioning

confidence: 99%

Efficient parameter calibration and real-time simulation of large scale spiking neural networks with GeNN and NEST

Schmitt

Rostami

Nawrot

2022

Preprint

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Spiking neural networks (SNN) represent the state-of-the-art approach to the biologically realistic modeling of nervous system function. The systematic calibration for multiple free model parameters to achieve robust network function demands high computing power and large memory resources. Special requirements arise from closed-loop model simulation in virtual environments, and from real-time simulation in robotic application. Here, we compare two complementary approaches to efficient large scale and real-time SNN simulation. The widely used NEural Simulation Technology (NEST) parallelizes simulation across multiple CPU cores. The GPU-enhanced Neural Network (GeNN) simulator uses the highlyparallel GPU-based architecture to gain simulation speed. We quantify fixed and variable simulation costs on different hardware configurations. As benchmark model we us a spiking cortical attractor network with a topology of densely connected excitatory and inhibitory neuron clusters. We show that simulation time scales linear with the simulated biological time and, for large networks, approximately linear with the model size as dominated by the number of synaptic connections. Additional fixed costs with GeNN for model compilation are independent of model size while fixed costs with NEST for model building increase linear with model size. We demonstrate how GeNN can be used for simulating networks with up to 3, 5 million neurons (> 3 × 1012 synapses) on a high-end GPU, and up to 250 thousand neurons (25 billion synapses) on a low-cost GPU. Real-time simulation was achieved for networks of more than 90 k neurons. We conclude that GeNN is in advantage for large networks and real-time applications while NEST plays out its strengths in a high degree of flexibility ideal for prototyping, and easy access for non-expert programmers.

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“…found that NEST simulation on the JURECA system (Thörnig, 2021) at the Jülich Supercomputing Center was ≈ 15 times faster than simulation with GeNN on a single GPU (NVIDIA TITAN RTX). The recent work by Tiddia et al (2022) found that NEST GPU (parallel simulation on 32 GPUs, NVIDIA V100 GPU with 16 GB HBM2e) outperformed NEST (simulated on JUSUF HPC cluster, Von St. Vieth, 2021, and JURECA) by at least a factor of two. Kurth et al (2022) reported the real-time factor for the multilayered model of a single cortical column introduced by Potjans and Diesmann (2014) with about N = 80, 000 neurons and 0, 3 • 10 9 synapses for a simulation with GeNN as RT = 0.7 (NVIDIA Titan RTX) and with NEST as RT = 0, 56 (cluster with two dual-processor machines with 128 cores each).…”

Section: Limitations Of the Present Studymentioning

confidence: 99%

Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN and NEST

Schmitt

Rostami²,

Nawrot³

2023

Front. Neuroinform.

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Spiking neural networks (SNNs) represent the state-of-the-art approach to the biologically realistic modeling of nervous system function. The systematic calibration for multiple free model parameters is necessary to achieve robust network function and demands high computing power and large memory resources. Special requirements arise from closed-loop model simulation in virtual environments and from real-time simulation in robotic application. Here, we compare two complementary approaches to efficient large-scale and real-time SNN simulation. The widely used NEural Simulation Tool (NEST) parallelizes simulation across multiple CPU cores. The GPU-enhanced Neural Network (GeNN) simulator uses the highly parallel GPU-based architecture to gain simulation speed. We quantify fixed and variable simulation costs on single machines with different hardware configurations. As a benchmark model, we use a spiking cortical attractor network with a topology of densely connected excitatory and inhibitory neuron clusters with homogeneous or distributed synaptic time constants and in comparison to the random balanced network. We show that simulation time scales linearly with the simulated biological model time and, for large networks, approximately linearly with the model size as dominated by the number of synaptic connections. Additional fixed costs with GeNN are almost independent of model size, while fixed costs with NEST increase linearly with model size. We demonstrate how GeNN can be used for simulating networks with up to 3.5 · 106 neurons (> 3 · 1012synapses) on a high-end GPU, and up to 250, 000 neurons (25 · 109 synapses) on a low-cost GPU. Real-time simulation was achieved for networks with 100, 000 neurons. Network calibration and parameter grid search can be efficiently achieved using batch processing. We discuss the advantages and disadvantages of both approaches for different use cases.

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Fast Simulation of a Multi-Area Spiking Network Model of Macaque Cortex on an MPI-GPU Cluster

Cited by 7 publications

References 54 publications

Towards the Simulation of a Realistic Large-Scale Spiking Network on a Desktop Multi-GPU System

Towards the Simulation of a Realistic Large-Scale Spiking Network on a Desktop Multi-GPU System

Efficient parameter calibration and real-time simulation of large scale spiking neural networks with GeNN and NEST

Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN and NEST

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