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

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Accession:97917
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.
Reference:
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]
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):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Methods;
Implementer(s): Hines, Michael [Michael.Hines at Yale.edu];
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splitcell
pardentategyrus
readme.html *
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bg.sh
DG500_M7.hoc *
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init.hoc
initorig.hoc *
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out.std
parRI10sp.hoc
RI10sp.hoc
test1.sh *
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load_file("nrngui.hoc")

objref box
box = new VBox()
box.intercept(1)
xpanel("run choices")
	xlabel("The below will typically take about 5 minutes to setup")
	xlabel("the network and then another 20 minutes to run it")
	xbutton("Run 10% sprouting network", "load_file(\"RI10sp.hoc\")")
	xlabel(" ")
	xlabel("The below quickly sets up a fast trivial test network")
	xlabel("for checking runability or running modelview to explore cells")
	xbutton("Test network",  "load_file(\"testnet.hoc\")")
xpanel()
box.intercept(0)
box.map()

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