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

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]

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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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mosinit_orig.hoc *

#!/bin/bash

dorun() {
	echo "starting dorun $np"
	mpirun -machinefile $HOME/mpi32 -np $1  /home/hines/neuron/mpi*6/i686/bin/nrniv init.hoc >& temp
	sort -k 1n,1n -k 2n,2n out.dat > out.$1
	mv temp stdout.$1
	echo "completed dorun $np"
}

for np in 25 15 12 6 5 3 2 1 ; do
	dorun $np
done