NEURON
Parallel network simulations with NEURON. The NEURON simulation environment has been extended to support parallel network simulations. Each processor integrates the equations for its subnet over an interval equal to the minimum (interprocessor) presynaptic spike generation to postsynaptic spike delivery connection delay. The performance of three published network models with very different spike patterns exhibits superlinear speedup on Beowulf clusters and demonstrates that spike communication overhead is often less than the benefit of an increased fraction of the entire problem fitting into high speed cache. On the EPFL IBM Blue Gene, almost linear speedup was obtained up to 100 processors. Increasing one model from 500 to 40,000 realistic cells exhibited almost linear speedup on 2000 processors, with an integration time of 9.8 seconds and communication time of 1.3 seconds. The potential for speed-ups of several orders of magnitude makes practical the running of large network simulations that could otherwise not be explored.
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References in zbMATH (referenced in 178 articles , 1 standard article )
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Sorted by year (- Bazenkov, Nikolay I.; Boldyshev, Boris A.; Dyakonova, Varvara; Kuznetsov, Oleg P.: Simulating small neural circuits with a discrete computational model (2020)
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- Dai, Wei P.; Li, Songting; Zhou, Douglas: Fast algorithms for simulation of neuronal dynamics based on the bilinear dendritic integration rule (2019)
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- Zhukov, Oleg A.; Kazakova, Tatiana A.; Maksimov, Georgy V.; Brazhe, Alexey R.: Cost of auditory sharpness: model-based estimate of energy use by auditory brainstem “octopus” neurons (2019)
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- Jaramillo, Gabriela; Venkataramani, Shankar C.: Target patterns in a 2D array of oscillators with nonlocal coupling (2018)
- Kufel, Dominik S.; Wojcik, Grzegorz M.: Analytical modelling of temperature effects on an AMPA-type synapse (2018)
- Laing, Carlo R.: The dynamics of networks of identical theta neurons (2018)
- Morel, Danielle; Singh, Chandan; Levy, William B.: Linearization of excitatory synaptic integration at no extra cost (2018)
- Rulkov, Nikolai F.; Neiman, Alexander B.: Control of sampling rate in map-based models of spiking neurons (2018)
- Sadashivaiah, Vijay; Sacré, Pierre; Guan, Yun; Anderson, William S.; Sarma, Sridevi V.: Modeling the interactions between stimulation and physiologically induced APs in a mammalian nerve fiber: dependence on frequency and fiber diameter (2018)