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
nrntraub
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alphasyndiffeq.mod
alphasynkin.mod *
alphasynkint.mod *
ampa.mod
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cad.mod *
cal.mod *
cat.mod *
cat_a.mod *
gabaa.mod
iclamp_const.mod *
k2.mod *
ka.mod *
ka_ib.mod *
kahp.mod *
kahp_deeppyr.mod *
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kc.mod *
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km.mod *
naf.mod *
naf_tcr.mod
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ri.mod
traub_nmda.mod
                            
TITLE Calcium high-threshold L type current for RD Traub, J Neurophysiol 89:909-921, 2003

COMMENT

	Implemented by Maciej Lazarewicz 2003 (mlazarew@seas.upenn.edu)
ENDCOMMENT

INDEPENDENT { t FROM 0 TO 1 WITH 1 (ms) }

UNITS { 
	(mV) = (millivolt) 
	(mA) = (milliamp) 
}
 
NEURON { 
	SUFFIX cal
	USEION ca WRITE ica
	RANGE  gbar, ica
	RANGE alpha, beta : added to write rates for comparison with F
}

PARAMETER { 
	gbar = 0.0 	(mho/cm2)
	v  		(mV)  
}
 
ASSIGNED { 
	ica 		(mA/cm2) 
	alpha (/ms) beta	(/ms)
}
 
STATE {
	m
}

BREAKPOINT { 
	SOLVE states METHOD cnexp
	ica = gbar * m * m * ( v - 125 ) 
}
 
INITIAL { 
	settables(v) 
	m = alpha / ( alpha + beta )
	m = 0
}
 
DERIVATIVE states { 
	settables(v) 
	m' = alpha * ( 1 - m ) - beta * m 
}

UNITSOFF 

PROCEDURE settables(v (mV)) { LOCAL tmp
	TABLE alpha, beta FROM -120 TO 40 WITH 641

	alpha = 1.6 / ( 1 + exp( - 0.072 * ( v - 5 ) ) )

	tmp = v + 8.9
	if ( fabs( tmp ) < 1e-6 ) {
		beta  = 0.1 * exp( - tmp / 5 ) 
	}else{
		beta  = 0.02 * tmp / ( exp( tmp / 5 ) - 1 )
	}
}

UNITSON

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