Cell splitting in neural networks extends strong scaling (Hines et al. 2008)

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Neuron tree topology equations can be split into two subtrees and solved on different processors with no change in accuracy, stability, or computational effort; communication costs involve only sending and receiving two double precision values by each subtree at each time step. Application of the cell splitting method to two published network models exhibits good runtime scaling on twice as many processors as could be effectively used with whole-cell balancing.
1 . Hines ML, Eichner H, Schürmann F (2008) Neuron splitting in compute-bound parallel network simulations enables runtime scaling with twice as many processors. J Comput Neurosci 25:203-10 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Generic;
Cell Type(s):
Gap Junctions:
Simulation Environment: NEURON;
Model Concept(s): Methods;
Implementer(s): Hines, Michael [Michael.Hines at Yale.edu];
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Santhakumar V, Aradi I, Soltesz I (2005) Role of mossy fiber sprouting
and mossy cell loss in hyperexcitability: a network model of the dentate
gyrus incorporating cell types and axonal topography. /J Neurophysiol/ *93*:437-53


Note: a diagram of the model is one of the figures of the paper.
I cannot say at the moment how many states the model has because some
mod files cannot be used with
the variable time step method.

527 cells
 500 Granule, 9 compartments, 7 Mechanisms
 6 Basket, 17 compartments, 7 Mechanisms
 15 Mossy, 17 compartments, 8 Mechanisms
 6 HIPP, 13 compartments, 8 Mechanisms
11293 NetCon into 4875 Exp2Syn

300 ms,  2861 spikes generated, 50136 spikes delivered.