A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations

Albers, Jasper; Pronold, Jari; Kurth, Anno; Vennemo, Stine Brekke; Mood, Kaveh Haghighi; Patronis, Alexander; Terhorst, D.; Jordan, Jakob; Kunkel, Susanne; Tetzlaff, Tom; Diesmann, Markus; Senk, Johanna

doi:10.3389/fninf.2022.837549

Cited by 10 publications

(7 citation statements)

References 78 publications

(132 reference statements)

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“…Figure 7 shows the performance benchmarks of the multi-area model on CPUs conducted on JURECA-DC using the benchmarking framework beNNch (Albers et al, 2022 ). For both network states, the optimal configuration of the hybrid parallelization is achieved with 8 MPI processes per node and 16 threads per task, thus making use of every physical core of the machine while avoiding hyperthreading.…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, this implementation only allowed MPI buffers to grow, but not to shrink. Albers et al ( 2022 ) identified that this puts unnecessary strain on the MPI communication. They therefore introduced spike compression which only sends one spike to each target MPI process, which has the necessary knowledge on the target threads saved in an additional data structure.…”

Section: Discussionmentioning

confidence: 99%

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

Tiddia

Golosio

Albers

et al. 2022

Front. Neuroinform.

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Spiking neural network models are increasingly establishing themselves as an effective tool for simulating the dynamics of neuronal populations and for understanding the relationship between these dynamics and brain function. Furthermore, the continuous development of parallel computing technologies and the growing availability of computational resources are leading to an era of large-scale simulations capable of describing regions of the brain of ever larger dimensions at increasing detail. Recently, the possibility to use MPI-based parallel codes on GPU-equipped clusters to run such complex simulations has emerged, opening up novel paths to further speed-ups. NEST GPU is a GPU library written in CUDA-C/C++ for large-scale simulations of spiking neural networks, which was recently extended with a novel algorithm for remote spike communication through MPI on a GPU cluster. In this work we evaluate its performance on the simulation of a multi-area model of macaque vision-related cortex, made up of about 4 million neurons and 24 billion synapses and representing 32 mm2 surface area of the macaque cortex. The outcome of the simulations is compared against that obtained using the well-known CPU-based spiking neural network simulator NEST on a high-performance computing cluster. The results show not only an optimal match with the NEST statistical measures of the neural activity in terms of three informative distributions, but also remarkable achievements in terms of simulation time per second of biological activity. Indeed, NEST GPU was able to simulate a second of biological time of the full-scale macaque cortex model in its metastable state 3.1× faster than NEST using 32 compute nodes equipped with an NVIDIA V100 GPU each. Using the same configuration, the ground state of the full-scale macaque cortex model was simulated 2.4× faster than NEST.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Tiddia

Golosio

Albers

et al. 2022

Front. Neuroinform.

Self Cite

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“…To fully exploit their capabilities in a benchmark comparison one needs to optimize for each simulator and use case. Second, the community has not agreed on standardized benchmark models (Albers et al, 2022; Ostrau et al, 2022; Kulkarni et al, 2021; Steffen et al, 2021). The RBN is widely used in computational neuroscience.…”

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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“…To fully exploit their capabilities in a benchmark comparison, one needs to optimize for each simulator and use case. Second, the community has not agreed on standardized benchmark models (Kulkarni et al, 2021;Steffen et al, 2021;Albers et al, 2022;Ostrau et al, 2022). The RBN is widely used in computational neuroscience (cf.…”

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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A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations

Cited by 10 publications

References 78 publications

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

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

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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