Fully Implicit Parallel Simulation of Single Neurons (Hines et al. 2008)

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Accession:97985
A 3-d reconstructed neuron model can be simulated in parallel on a dozen or so processors and experience almost linear speedup. Network models can be simulated when there are more processors than cells.
Reference:
1 . Hines ML, Markram H, Schuermann F (2008) Fully Implicit Parallel Simulation of Single Neurons J Comp Neurosci 25:439-448 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type:
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):
objref tdat_   
tdat_ = new Vector(7)

proc statrun() {
        tdat_.x[0] = pnm.pc.wait_time
        stdinit()
        pnm.psolve($1)
        tdat_.x[0] = pnm.pc.wait_time - tdat_.x[0]
        tdat_.x[1] = pnm.pc.step_time
        tdat_.x[2] = pnm.pc.send_time
	tdat_.x[3] = pc.vtransfer_time(0) // for gaps
	tdat_.x[4] = pc.vtransfer_time(1) // for splitcell communication
	tdat_.x[5] = pc.vtransfer_time(2) // for reduced tree
	tdat_.x[6] = cplx
}

objref tavg_stat, tmin_stat, tmax_stat, idmin_stat, idmax_stat
proc poststat() {
	pnm.pc.post("poststat", pnm.myid, tdat_)
}
proc getstat() {local i, j, id localobj tdat
	tdat = tdat_.c	tavg_stat = tdat_.c  tmin_stat = tdat_.c  tmax_stat = tdat_.c
	idmin_stat = tdat_.c.fill(0)  idmax_stat = tdat_.c.fill(0)
	if (pnm.nwork > 1) {
		pnm.pc.context("poststat()\n")
		for i=0, pnm.nwork-2 {
			pnm.pc.take("poststat", &id, tdat)
			tavg_stat.add(tdat)
			for j = 0, tdat_.size-1 {
				if (tdat.x[j] > tmax_stat.x[j]) {
					idmax_stat.x[j] = id
					tmax_stat.x[j] = tdat.x[j]
				}
				if (tdat.x[j] < tmin_stat.x[j]) {
					idmin_stat.x[j] = id
					tmin_stat.x[j] = tdat.x[j]
				}
			}
		}
	}
	tavg_stat.div(pnm.nhost)
}

objref spstat_
proc postspstat() {local i
	spstat_ = new Vector()
	cvode.spike_stat(spstat_)
	i = spstat_.size
	spstat_.resize(spstat_.size + 4)
	spstat_.x[i] = pc.spike_statistics(&spstat_.x[i+1], &spstat_.x[i+2],\
		&spstat_.x[i+3])
	pnm.pc.post("postspstat", pnm.myid, spstat_)
}

proc perf2file() { local i  localobj perf
	perf = new File()
	perf.aopen("perf.dat")
	perf.printf("%d %d 0 %g     %g %g     ",pnm.nhost, model, maxfactor, setuptime, runtime)
	for i=0, tdat_.size-1 { perf.printf(" %g", tavg_stat.x[i]) }
	perf.printf("     ")
	for i=0, tdat_.size-1 { perf.printf(" %d %g ", idmin_stat.x[i], tmin_stat.x[i]) }
	perf.printf("     ")
	for i=0, tdat_.size-1 { perf.printf(" %d %g ", idmax_stat.x[i], tmax_stat.x[i]) }
	perf.printf("\n")

	perf.close
}

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