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 169 articles , 1 standard article )
Showing results 1 to 20 of 169.
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- Kufel, Dominik S.; Wojcik, Grzegorz M.: Analytical modelling of temperature effects on an AMPA-type synapse (2018)
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- 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)
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- Gandolfo, Daniel; Rodriguez, Roger; Tuckwell, Henry C.: Mean field analysis of large-scale interacting populations of stochastic conductance-based spiking neurons using the Klimontovich method (2017)
- Lin, Zhongwei; Tropper, Carl; McDougal, Robert A.; Ishlam Patoary, Mohammand Nazrul; Lytton, William W.; Yao, Yiping; Hines, Michael L.: Multithreaded stochastic PDES for reactions and diffusions in neurons (2017)