Parallel network simulations with NEURON (Migliore et al 2006)

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Accession:64229
The NEURON simulation environment has been extended to support parallel network simulations. The performance of three published network models with very different spike patterns exhibits superlinear speedup on Beowulf clusters.
Reference:
1 . Migliore M, Cannia C, Lytton WW, Markram H, Hines ML (2006) Parallel Network Simulations with NEURON. J Comp Neurosci 21:110-119 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
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];
{load_file("nrngui.hoc")}
create acell_home_
{load_file("netparmpi.hoc")}
objref pnm, pc

ncell = 1000
ncon = 100

ranoffset_ = 500 // adjacent cell streams will be correlated by this offset
connect_random_low_start_ = 1
run_random_low_start_ = 2

pnm = new ParallelNetManager(ncell)
pc = pnm.pc
iterator pcitr() {local i1, i2
	i1 = 0
	for (i2=pc.id; i2 < ncell; i2 += pc.nhost) {
		$&1 = i1
		$&2 = i2
		iterator_statement
		i1 += 1
	}
}

{load_file("perfrun.hoc")}


setuptime = startsw()
{load_file("net.hoc")}
//want_all_spikes()
{cvode_local(1)}
tstop = 200
init_run_random(run_random_low_start_)
mkhist(100)

setuptime = startsw() - setuptime

if (pnm.myid == 0) {print "SetupTime: ", setuptime}
prun()
if (pnm.myid == 0) {print "RunTime: ", runtime}

{pnm.pc.runworker()}

//{pnm.prstat(1)}
getstat()
//{pnm.gatherspikes()}
prhist()
print_spike_stat_info()
{pnm.pc.done()}

perf2file()
//spike2file()
{printf("ncell = %d ncon = %d tstop = %g\n", ncell, ncon, tstop) }
quit()


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