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]
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):
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];
/
splitcell
nrntraub
cells
hoc
mod
net
README
balcomp.hoc *
bgrunme
bgsmall.sh
bgsplit.sh
cell_templates.hoc *
clear.hoc *
finit.hoc *
fortmap.hoc *
gidcell.hoc
gidcell.ses *
init.hoc
manage_setup.hoc
metisbal.sh
mosinit_orig.hoc *
onecell.hoc
onecell.ses *
prcellstate.hoc *
prepare.sh
printcon.hoc *
spkplt.hoc *
vclampg.hoc *
vcompclamp.hoc *
vcompsim.hoc *
                            
setuptime = startsw()

one_tenth_ncell = 1
use_gap = 0
use_ectopic = 0
use_inject = 0
default_delay=.05
awake = 1
use_load_balance = 0

{load_file("nrngui.hoc")}
{load_file("fortmap.hoc")}
{load_file("hoc/parlib.hoc")}

focus = 1

iterator pcitr() {local i1, i2
	$&1 = 0
	$&2 = focus
	iterator_statement
}

proc gid_distribute() {
print "gid_distribute ", focus
	pc.set_gid2node(focus, pc.id)
}

{load_file("finit.hoc")}
ranseedbase = 1
serial = 0 // override serial set in parlib.hoc
pmesg = 1 && (pc.id == 0)
small_model = 0 // 0 for full model, set to 1 for 40 cells each type
use_traubexact = 1
{load_file("hoc/traubcon.hoc")}

// turn off/on all tables
proc activate_tables() {
	usetable_ar = $1
	usetable_cal = $1
	usetable_cat_a = $1
	usetable_cat = $1
	usetable_k2 = $1
	usetable_ka = $1
	usetable_ka_ib = $1
	usetable_kc = $1
	usetable_kc_fast = $1
	usetable_kdr = $1
	usetable_kdr_fs = $1
	usetable_km = $1
//	usetable_naf2 = $1
//	usetable_naf = $1
//	usetable_naf_tcr = $1
	usetable_nap = $1
//	usetable_napf = $1
//	usetable_napf_spinstell = $1
//	usetable_napf_tcr = $1
}

// til the shift bug in the mod files are fixed (table depends on range variable)
if (1) {
usetable_naf2 = 0
usetable_naf = 0
usetable_naf_tcr = 0
usetable_napf = 0
usetable_napf_spinstell = 0
usetable_napf_tcr = 0
}

gfac_AMPA = 1
gfac_NMDA = 0
gfac_GABAA = 1

{load_file("cell_templates.hoc")}
use_p2c_net_connections = 1
{load_file("net/network_specification_interface.hoc")}
if (!serial) {load_file("hoc/parlib2.hoc")}
{load_file("net/serial_or_par_wrapper.hoc")}

objref fihprog_

objref pattern_, tvec_, idvec_
objref cell, typeflag
load_file("clear.hoc")
typeflag = new Vector(14)
pattern_ = new PatternStim()

proc pattern() {
	clipboard_retrieve("out.spk")
	tvec_ = hoc_obj_[1].c
	idvec_ = hoc_obj_[0].c
	pattern_.play(tvec_, idvec_)
}

proc fakeout() {local i, gid, th
	if ($1 != 0) { th = 1e9 } else { th = 0 }
	pattern_.fake_output = $1
	for pcitr (&i, &gid) {
		pnm.pc.threshold(gid, th)
	}
}		

proc reload() {local i, st, dtsav  localobj ncl
	dtsav=dt
	st = startsw()
	clear()
	focus = $1
	load_file(1, "net/groucho.hoc")
	define_shape()
	want_all_spikes()
	mkhist(50)

	if (pc.id == 0) fihprog_ = new FInitializeHandler("progress()")
	if (use_traubexact) {
		load_file("hoc/traubcon_net.hoc")
		reset_connection_coefficients()
	}
	st = startsw() - st
	cell = cells.object(0)
	fakeout(1)
	typeflag.fill(0) typeflag.x[cell.type] = 1
	dt=dtsav
	ncl = pnm.nclist
	for i=0, ncl.count-1 if (ncl.object(i).delay == .05) ncl.object(i).delay=0
	if (pc.id == 0) {print "reload time: ", st }
}
reload(focus)

proc progress() {
//	print "t=",t
	cvode.event(t+1, "progress()")
}


cvode_active(1)

setuptime = startsw() - setuptime
if (pc.id == 0) {print "SetupTime: ", setuptime}

steps_per_ms = 50
dt = .01
secondorder = 2
if (serial) {
	tstop = 10
}else{
	tstop = 10
}

load_file("gidcell.ses")

tf = 100
objref gm, synmat[3]
proc rdat() {local numcomp  localobj s, f
	s = new String()
	classname(cell, s.s)
	sprint(s.s, "../p2c/data/GROUCHO110.%s", s.s)
	print s.s
//	gm = new Matrix(999,8)
	gm = new Matrix(10*tf-1,2)
	f = new File()
	f.ropen(s.s)
	gm.scanf(f, gm.nrow, gm.ncol)
	gm.getcol(1).line(Graph[0], gm.getcol(0), 2, 1)
    if (0) {
	gm.getcol(5).line(Graph[1], gm.getcol(0), 2, 1)
	gm.getcol(6).line(Graph[1], gm.getcol(0), 3, 1)
	gm.getcol(7).line(Graph[1], gm.getcol(0), 4, 1)
    }
    if (1) {
	f = new File()
	numcomp=0 forsec cell.all numcomp += 1
	synmat[2] = new Matrix(10*tf-1, numcomp+1)
	classname(cell, s.s)
	sprint(s.s, "../p2c/data/gaba_%s.dat", s.s)
	f.ropen(s.s)
	synmat[2].scanf(f, synmat[2].nrow, synmat[2].ncol)

	synmat[0] = new Matrix(10*tf-1, numcomp+1)
	classname(cell, s.s)
	sprint(s.s, "../p2c/data/ampa_%s.dat", s.s)
	f.ropen(s.s)
	synmat[0].scanf(f, synmat[0].nrow, synmat[0].ncol)

	synmat[1] = new Matrix(10*tf-1, numcomp+1)
	classname(cell, s.s)
	sprint(s.s, "../p2c/data/nmda_%s.dat", s.s)
	f.ropen(s.s)
	synmat[1].scanf(f, synmat[0].nrow, synmat[0].ncol)
    }
}
rdat()
pattern()

proc another() {
	reload($1)
	Graph[0].erase
	rdat()
}

//clipboard_retrieve("../p2c/data/ampa.dat")
//hoc_obj_.line(Graph[1], hoc_obj_[1], 2, 1) 
//clipboard_retrieve("../p2c/data/nmda.dat")
//hoc_obj_.line(Graph[1], hoc_obj_[1], 2, 1) 

objref gg
gg = Graph[1]
which = 1
func am() {local g
	g = 0
	forsec cell.all if (ismembrane("ampa1_ion")) {
		g += iampa1*area(0.5)/100
	}
	return g
}
func nm() {local g
	g = 0
	forsec cell.all if (ismembrane("nmda1_ion")) {
		g += inmda1*area(0.5)/100
	}
	return g
}
func ga() {local g
	g = 0
	forsec cell.all if (ismembrane("gaba1_ion")) {
		g += igaba1*area(0.5)/100
	}
	return g
}

func f() {local g
//   cell.comp[which] if (ismembrane("ampa1_ion")) {
//    g = iampa1*area(0.5)/100
   cell.comp[which] if (ismembrane("gaba1_ion")) {
    g = igaba1*area(0.5)/100
   }
   return g
}

proc pw() {
   gg.erase()
   which = $1
   gm = $o2
   gm.getcol(which).line(gg, gm.getcol(0), 2, 1)
}

/* some useful idioms
objref a
a = cell.synlist
for i=0, a.count-1 if (a.o(i).comp == 3) print i, a.o(i), a.o(i).srcgid

objref b
b = pnm.nclist
for i=0,b.count-1 if (b.o(i).syn.comp == 3) print i, b.o(i), b.o(i).syn

for i=0,b.count-1 b.o(i).threshold = 1000
*/

proc mk_another_panel() {local i  localobj s1, s2, base
	s1 = new String()
	s2 = new String()
	base = new Vector(14)
	base.x[0] = 1
	for i=0, 12 {
		sprint(s1.s, "hoc_ac_ = num_%s", typename[i].s)
		execute(s1.s)
		base.x[i+1] = base.x[i] + hoc_ac_
	}
	xpanel("Make a different cell")
	for i=0, 13 {
		sprint(s2.s, "another(%d)", base.x[i])
		sprint(s1.s, "%s %d", typename[i].s, base.x[i])
		xcheckbox(s1.s, &typeflag.x[i], s2.s)
	}
	xpanel(0)
}
mk_another_panel()

seewhich = 0
seetype = 0 // ampa,nmda,gaba
objref syntrajeclist[3], tsyn
proc synsim() { local i, j  localobj vv, gn, gf, r, rr
	for i=0,2 { syntrajeclist[i] = new List() }
	gn = syntrajeclist[seetype]
	gf = synmat[seetype]
	i = 1
	tsyn = new Vector()
	cell.comp[1] {tsyn.record(&t)}
	for i=1, gf.ncol-1 cell.comp[i] {
		insert ampa1_ion
		insert gaba1_ion
		insert nmda1_ion
		vv = new Vector()
		vv.record(&iampa1(.5))
		syntrajeclist[0].append(vv)
		vv = new Vector()
		vv.record(&inmda1(.5))
		syntrajeclist[1].append(vv)
		vv = new Vector()
		vv.record(&igaba1(.5))
		syntrajeclist[2].append(vv)
	}
	stdinit()
	continuerun(100)
	rr = new Vector()
	for i=1, gf.ncol-1 cell.comp[i] for j=0, 2{
		syntrajeclist[j].object(i-1).mul(area(0.5)/100)
	}
	for i=0, gn.count-1 {
		r = gn.object(i).c.interpolate(gf.getcol(0), tsyn)
		rr.append(r.sub(gf.getcol(i+1)).sumsq)
	}
	i = rr.max_ind
	print i, rr.x[i]
	see(i+1, seetype)
}

proc see() {localobj gn, gf, s
   s = new String()
   gg.erase_all()
   seewhich = $1
   seetype = $2
   if (seetype > 2) {seetype = 2}
   if (seetype < 0) {seetype = 0}
   gf = synmat[seetype]
   gn = syntrajeclist[seetype]
   if (seewhich > gn.count) {seewhich = gn.count-1}
   if (seewhich < 1) { seewhich = 1 }
   if (seetype == 0) { s.s = "AMPA" }
   if (seetype == 1) { s.s = "NMDA" }
   if (seetype == 2) { s.s = "GABAA" }
   cell.comp[seewhich] { sprint(s.s,"%s at %s",s.s, secname()) }
   gg.label(.5,.8,s.s,2,1,0,0,1)
   gf.getcol(seewhich).line(gg, gf.getcol(0), 2, 1)
   gn.object(seewhich-1).line(gg, tsyn) 
}


proc mksee() {
	xpanel("compare synapse conductance")
	xvalue("AMPA=0 NMDA=1 GABAA=2", "seetype", 1, "see(seewhich, seetype)")
	xvalue("which", "seewhich", 1, "see(seewhich, seetype)")
	xpanel()
}

load_file("vclampg.hoc")


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