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];
iterator pcitr() {local i1, i2, si
	// only over the source gids unless $3
	// exists and nonzero. Note that with load balance, the gid
	// exists only after the cell is created.
	si = 0
	if (numarg() == 3) {
		si = $3
	for i1=0, gidvec.size-1 {
		i2 = gidvec.x[i1]
		$&1 = i1
		$&2 = i2
		if (si == 1) {
		}else if (i2 < splitbit) {

proc read_splitcell_info() { localobj s
	s = new String()
	sprint(s.s, "%s.%d", $s1, pc.nhost)
	load_balance_.read_load_balance_info(s.s, pc.id)
	gidvec = new Vector()