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];
durand.hoc *
groucho_gapbld.hoc *
groucho_gapbld_mix.hoc *
serial_or_par_wrapper.hoc *
synaptic_compmap_construct.hoc *
synaptic_map_construct.hoc *
// create replacement durand that might be replaced later by
// a compiled mechanism (nmodl mod file) for speed
objref seed_vec, output_vec
objref randobj
randobj = new Random()
{randobj.uniform(0, 1)}
if (!serial) {
//	randobj.Isaac64(ranseedbase)

// arguments are Vector seed, double size_of_return_vector, Vector output
// both the seed and the output are unused for now and only seed has the
// potential to be used since the function is called output=durand(...)

obfunc durand() { // sent a vector whose first element is seed
		// the number of random elements to return
		// in the third argument which was a vector
	objref output_vec
	seed_vec = $o1 // unused for now - could feed into randobj
	vec_size = $2
	output_vec = $o3
	output_vec = new Vector()
	for i=1, vec_size {
		output_vec.append( randobj.repick() )
	return output_vec