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];
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splitcell
nrntraub
net
durand.hoc *
groucho.hoc
groucho_gapbld.hoc *
groucho_gapbld_mix.hoc *
network_specification_interface.hoc
serial_or_par_wrapper.hoc *
synaptic_compmap_construct.hoc *
synaptic_map_construct.hoc *
                            
objref map, x

obfunc synaptic_map_construct (){ localobj f, s, tmpmap
	/* $1 thisno,
	$2 num_presynaptic_cells,
	$3 num_postsynaptic_cells,
	// no more $o4 map(i,j), Matrix object = cell # of ith presyn input to jth postsyn cell
	$4 num_presyninputs_perpostsyn_cell, 
	$5 display
	$s6 map file base name
	*/

	// Construct a map of presynaptic cells of one type to postsyn.
	//  cells of some type. 
	// display is an integer flag.  If display = 1, print gjtable

	//        INTEGER thisno, num_presynaptic_cells,
	//     &   num_postsynaptic_cells,
	//     &   num_presyninputs_perpostsyn_cell,
	//     &   map (num_presyninputs_perpostsyn_cell,
	//     &          num_postsynaptic_cells) 
	//        INTEGER i,j,k,l,m,n,o,p
	//       INTEGER display

	//        double precision seed, x(1)
	thisno = $1
	num_presynaptic_cells = $2
	num_postsynaptic_cells = $3
	objref map
	//map = $o4 // dosen't matter
	num_presyninputs_perpostsyn_cell = $4
	display = $5
	// objref seed
	seed = new Vector()
	seed.append(297.e0)
	map = new Matrix( num_presyninputs_perpostsyn_cell+1, num_postsynaptic_cells+1 )

  if (!use_p2c_net_connections) {
	k = 1

	for ii = 1, num_postsynaptic_cells {
	//	print " Constructing map for cell #",ii
		for j = 1, num_presyninputs_perpostsyn_cell {
	        	x = durand (seed, k, x)
			// This defines a presynaptic cell
		
		        LL = int ( x.x(0) * num_presynaptic_cells ) + 1
		        if (LL > num_presynaptic_cells) {
				print " unnexpected boundary issue in synaptic_map_construct()"
				LL = num_presynaptic_cells
			}

		        map.x[j][ii] = LL
	
		}
	}

	// Possibly print out map when done.
	if ((display == 1) && (thisno == 0)) {
		print "SYNAPTIC MAP"
	        for i = 1, num_postsynaptic_cells {
         		printf("%6d %6d %6d \n",map.x(1,i), map.x(2,i), \
			map.x(num_presyninputs_perpostsyn_cell,i))
		}
	}
  }else{
	// read from file created by port2colossus
	s = new String()
	sprint(s.s, "../../p2c/map/%s.dat", $s6)
//printf("%s %d %d\n", s.s, map.nrow-1, map.ncol-1)
	f = new File()
	f.ropen(s.s)
	tmpmap = new Matrix(map.nrow-1, map.ncol-1)
	tmpmap.scanf(f, map.nrow-1, map.ncol-1)
	tmpmap.bcopy(0,0,map.nrow-1, map.ncol-1, 1, 1, map)
  }
	return map
}

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