def multisplit_distrib(model):
enter = h.startsw()
cxlist = determine_multisplit_complexity(model)
#start over
destroy_model(model)
# but we still have model.mconnections and model.rank_gconnections
# from the round-robin distribution perspective.
# we will need to tell the ranks where to distribute that information
cxlist = load_bal(cxlist, nhost) #cxlist is the list of (cx,(gid,piece)) we want on this process
# the new distribution of mitrals and granules
model.gids = set([item[1][0] for item in cxlist])
model.mitral_gids = set([gid for gid in model.gids if gid < params.gid_granule_begin])
model.granule_gids = model.gids - model.mitral_gids
# for splitting need gid:[pieceindices]
gid2pieces = {}
for item in cxlist:
gid = item[1][0]
piece = item[1][1]
if not gid2pieces.has_key(gid):
gid2pieces.update({gid:[]})
gid2pieces[gid].append(piece)
# get the correct mconnections and gconnections
# Round-robin ranks that presently have the connection info for the gids
rr = {}
for gid in model.gids:
r = gid%nhost
if not rr.has_key(r):
rr.update({r:[]})
rr[r].append(gid);
rr = all2all(rr)
# rr is now the ranks for where to send the synapse information
# all the gids in rr were 'owned' by the round-robin distribution
# may wish to revisit so that only synapse info for relevant pieces is
# scattered.
mc = model.mconnections
gc = model.rank_gconnections
# construct a ggid2connection dict
ggid2connection = {}
for r in gc:
for ci in gc[r]:
ggid = ci[3]
if not ggid2connection.has_key(ggid):
ggid2connection.update({ggid:[]})
ggid2connection[ggid].append(ci)
for r in rr:
gids = rr[r]
mgci = []
rr[r] = mgci
for gid in gids:
if mc.has_key(gid):
mgci.append(mc[gid])
else:
mgci.append(ggid2connection[gid])
mgci = all2all(rr)
# mgci contains all the connection info needed by the balanced distribution
# create mitrals and granules, split and register and create synapses
nmc.dc.mk_mitrals(model) # whole cells
nmc.build_granules(model)
for gid in gid2pieces:
if gid < params.Nmitral:
split.splitmitral(gid, model.mitrals[gid], gid2pieces[gid])
pc.multisplit()
nmc.register_mitrals(model)
nmc.register_granules(model)
# build_synapses() ... use mgci to build explicitly
model.mgrss = {}
for r in mgci:
for cil in mgci[r]:
for ci in cil:
if not model.mgrss.has_key(mgrs.mgrs_gid(ci[0], ci[3], ci[6])):
rsyn = mgrs.mk_mgrs(*ci[0:7])
if rsyn:
model.mgrss.update({rsyn.md_gid : rsyn})
nmultiple = int(pc.allreduce(mgrs.multiple_cnt(), 1))
if rank == 0:
print 'nmultiple = ', nmultiple
detectors = h.List("AmpaNmda")
util.elapsed('%d ampanmda for reciprocalsynapses constructed'%int(pc.allreduce(detectors.count(),1)))
detectors = h.List("FastInhib")
util.elapsed('%d fi for reciprocalsynapses constructed'%int(pc.allreduce(detectors.count(),1)))
detectors = h.List("ThreshDetect")
util.elapsed('%d ThreshDetect for reciprocalsynapses constructed'%int(pc.allreduce(detectors.count(),1)))
if rank == 0: print 'multisplit_distrib time ', h.startsw() - enter
def multisplit_distrib(model):
enter = h.startsw()
cxlist = determine_multisplit_complexity(model)
#start over
destroy_model(model)
# fake cell to solve problem with gap junctions
# init_fake_cell()
# but we still have model.mconnections and model.rank_gconnections
# from the round-robin distribution perspective.
# we will need to tell the ranks where to distribute that information
cxlist = load_bal(
cxlist,
nhost) #cxlist is the list of (cx,(gid,piece)) we want on this process
# the new distribution of mitrals and granules
model.gids = set([item[1][0] for item in cxlist])
model.mitral_gids = set(
[gid for gid in model.gids if ismitral(gid) or ismtufted(gid)])
model.granule_gids = set([gid for gid in model.gids if isgranule(gid)])
model.blanes_gids = set([gid for gid in model.gids if isblanes(gid)])
# for splitting need gid:[pieceindices]
gid2pieces = {}
for item in cxlist:
gid = item[1][0]
piece = item[1][1]
if not gid2pieces.has_key(gid):
gid2pieces[gid] = []
gid2pieces[gid].append(piece)
# get the correct mconnections and gconnections
# Round-robin ranks that presently have the connection info for the gids
rr = {}
for gid in model.gids:
r = gid % nhost
if not rr.has_key(r):
rr[r] = []
rr[r].append(gid)
rr = all2all(rr)
# rr is now the ranks for where to send the synapse information
# all the gids in rr were 'owned' by the round-robin distribution
# may wish to revisit so that only synapse info for relevant pieces is
# scattered.
mc = model.mconnections
gc = model.rank_gconnections
# construct a ggid2connection dict
ggid2connection = {}
for r in gc:
for ci in gc[r]:
ggid = ci[3]
if not ggid2connection.has_key(ggid):
ggid2connection[ggid] = []
ggid2connection[ggid].append(ci)
for r in rr:
gids = rr[r]
mgci = []
rr[r] = mgci
for gid in gids:
if mc.has_key(gid):
mgci.append(mc[gid])
elif gidfunc.isgranule(gid):
mgci.append(ggid2connection[gid])
mgci = all2all(rr)
# mgci contains all the connection info needed by the balanced distribution
# create mitrals and granules, split and register and create synapses
nmc.dc.mk_mitrals(model) # whole cells
nmc.build_granules(model)
nmc.build_blanes(model)
for gid in gid2pieces:
if ismitral(gid) or ismtufted(gid):
split.splitmitral(gid, model.mitrals[gid], gid2pieces[gid])
pc.multisplit()
nmc.register_mitrals(model)
nmc.register_granules(model)
nmc.register_blanes(model)
# build_synapses() ... use mgci to build explicitly
model.mgrss = {}
for r in mgci:
for cil in mgci[r]:
for ci in cil:
if not model.mgrss.has_key(mgrs.mgrs_gid(ci[0], ci[3], ci[6])):
rsyn = mgrs.mk_mgrs(*ci[0:7])
if rsyn:
model.mgrss[rsyn.md_gid] = rsyn
nmultiple = int(pc.allreduce(mgrs.multiple_cnt(), 1))
# it is faster if generated again
blanes.mk_gl2b_connections()
# excitatory
for mgid, blanes_gid, w in model.mt2blanes_connections:
syn = blanes.mt2blanes(mgid, blanes_gid, w)
model.mt2blanes[syn.gid] = syn
blanes.mk_b2g_connections()
# inhibitory synapses from blanes to gc
for ggid, blanes_gid, w in model.blanes2gc_connections:
syn = blanes.blanes2granule(blanes_gid, ggid, w)
model.blanes2gc[syn.gid] = syn
if rank == 0:
print 'nmultiple = ', nmultiple
detectors = h.List("AmpaNmda")
util.elapsed('%d ampanmda for reciprocalsynapses constructed' %
int(pc.allreduce(detectors.count(), 1)))
detectors = h.List("FastInhib")
util.elapsed('%d fi for reciprocalsynapses constructed' %
int(pc.allreduce(detectors.count(), 1)))
detectors = h.List("ThreshDetect")
util.elapsed('%d ThreshDetect for reciprocalsynapses constructed' %
int(pc.allreduce(detectors.count(), 1)))
util.elapsed('%d mt to bc' % int(pc.allreduce(len(model.mt2blanes), 1)))
util.elapsed('%d bc to gc' % int(pc.allreduce(len(model.blanes2gc), 1)))
if rank == 0: print 'multisplit_distrib time ', h.startsw() - enter
def whole_cell_distrib(model):
enter = h.startsw()
cx = determine_complexity(model)
# start over
destroy_model(model)
# but we still have model.mconnections and model.rank_gconnections
# from the round-robin distribution perspective.
# we will need to tell the ranks where to distribute that information
cx = load_bal(cx, nhost) # cx is the list of (cx,gid) we want on this process
# the new distribution of mitrals and granules
model.gids = set([item[1] for item in cx])
model.mitral_gids = set([gid for gid in model.gids if gid < params.gid_granule_begin])
model.granule_gids = model.gids - model.mitral_gids
# get the correct mconnections and gconnections
# Round-robin ranks that presently have the connection info for the gids
rr = {}
for gid in model.gids:
r = gid % nhost
if not rr.has_key(r):
rr.update({r: []})
rr[r].append(gid)
rr = all2all(rr)
# rr is now the ranks for where to send the synapse information
# all the gids in rr were 'owned' by the round-robin distribution
mc = model.mconnections
gc = model.rank_gconnections
# construct a ggid2connection dict
ggid2connection = {}
for r in gc:
for ci in gc[r]:
ggid = ci[3]
if not ggid2connection.has_key(ggid):
ggid2connection.update({ggid: []})
ggid2connection[ggid].append(ci)
for r in rr:
gids = rr[r]
mgci = []
rr[r] = mgci
for gid in gids:
if mc.has_key(gid):
mgci.append(mc[gid])
else:
mgci.append(ggid2connection[gid])
mgci = all2all(rr)
# mgci contains all the connection info needed by the balanced distribution
# create mitrals and granules and register and create synapses
nmc.dc.mk_mitrals(model)
nmc.register_mitrals(model)
nmc.build_granules(model)
nmc.register_granules(model)
# build_synapses() ... use mgci to build explicitly
model.mgrss = {}
for r in mgci:
for cil in mgci[r]:
for ci in cil:
if not model.mgrss.has_key(mgrs.mgrs_gid(ci[0], ci[3], ci[6])):
rsyn = mgrs.mk_mgrs(*ci[0:7])
if rsyn:
model.mgrss.update({rsyn.md_gid: rsyn})
nmultiple = int(pc.allreduce(mgrs.multiple_cnt(), 1))
if rank == 0:
print "nmultiple = ", nmultiple
detectors = h.List("ThreshDetect")
util.elapsed("%d ThreshDetect for reciprocalsynapses constructed" % int(pc.allreduce(detectors.count(), 1)))
if rank == 0:
print "whole_cell_distrib time ", h.startsw() - enter
def whole_cell_distrib(model):
enter = h.startsw()
cx = determine_complexity(model)
#start over
destroy_model(model)
# but we still have model.mconnections and model.rank_gconnections
# from the round-robin distribution perspective.
# we will need to tell the ranks where to distribute that information
cx = load_bal(cx,
nhost) #cx is the list of (cx,gid) we want on this process
# the new distribution of mitrals and granules
model.gids = set([item[1] for item in cx])
model.mitral_gids = set(
[gid for gid in model.gids if gid < params.gid_granule_begin])
model.granule_gids = model.gids - model.mitral_gids
# get the correct mconnections and gconnections
# Round-robin ranks that presently have the connection info for the gids
rr = {}
for gid in model.gids:
r = gid % nhost
if not rr.has_key(r):
rr[r] = []
rr[r].append(gid)
rr = all2all(rr)
# rr is now the ranks for where to send the synapse information
# all the gids in rr were 'owned' by the round-robin distribution
mc = model.mconnections
gc = model.rank_gconnections
# construct a ggid2connection dict
ggid2connection = {}
for r in gc:
for ci in gc[r]:
ggid = ci[3]
if not ggid2connection.has_key(ggid):
ggid2connection[ggid] = []
ggid2connection[ggid].append(ci)
for r in rr:
gids = rr[r]
mgci = []
rr[r] = mgci
for gid in gids:
if mc.has_key(gid):
mgci.append(mc[gid])
else:
mgci.append(ggid2connection[gid])
mgci = all2all(rr)
# mgci contains all the connection info needed by the balanced distribution
# create mitrals and granules and register and create synapses
nmc.dc.mk_mitrals(model)
nmc.register_mitrals(model)
nmc.build_granules(model)
nmc.register_granules(model)
# build_synapses() ... use mgci to build explicitly
model.mgrss = {}
for r in mgci:
for cil in mgci[r]:
for ci in cil:
if not model.mgrss.has_key(mgrs.mgrs_gid(ci[0], ci[3], ci[6])):
rsyn = mgrs.mk_mgrs(*ci[0:7])
if rsyn:
model.mgrss[rsyn.md_gid] = rsyn
nmultiple = int(pc.allreduce(mgrs.multiple_cnt(), 1))
if rank == 0:
print 'nmultiple = ', nmultiple
detectors = h.List("ThreshDetect")
util.elapsed('%d ThreshDetect for reciprocalsynapses constructed' %
int(pc.allreduce(detectors.count(), 1)))
if rank == 0: print 'whole_cell_distrib time ', h.startsw() - enter
def multisplit_distrib(model):
enter = h.startsw()
cxlist = determine_multisplit_complexity(model)
#start over
destroy_model(model)
# but we still have model.mconnections and model.rank_gconnections
# from the round-robin distribution perspective.
# we will need to tell the ranks where to distribute that information
cxlist = load_bal(
cxlist,
nhost) #cxlist is the list of (cx,(gid,piece)) we want on this process
# the new distribution of mitrals and granules
model.gids = set([item[1][0] for item in cxlist])
model.mitral_gids = set(
[gid for gid in model.gids if gid < params.gid_granule_begin])
model.granule_gids = model.gids - model.mitral_gids
# for splitting need gid:[pieceindices]
gid2pieces = {}
for item in cxlist:
gid = item[1][0]
piece = item[1][1]
if not gid2pieces.has_key(gid):
gid2pieces.update({gid: []})
gid2pieces[gid].append(piece)
# get the correct mconnections and gconnections
# Round-robin ranks that presently have the connection info for the gids
rr = {}
for gid in model.gids:
r = gid % nhost
if not rr.has_key(r):
rr.update({r: []})
rr[r].append(gid)
rr = all2all(rr)
# rr is now the ranks for where to send the synapse information
# all the gids in rr were 'owned' by the round-robin distribution
# may wish to revisit so that only synapse info for relevant pieces is
# scattered.
mc = model.mconnections
gc = model.rank_gconnections
# construct a ggid2connection dict
ggid2connection = {}
for r in gc:
for ci in gc[r]:
ggid = ci[3]
if not ggid2connection.has_key(ggid):
ggid2connection.update({ggid: []})
ggid2connection[ggid].append(ci)
for r in rr:
gids = rr[r]
mgci = []
rr[r] = mgci
for gid in gids:
if mc.has_key(gid):
mgci.append(mc[gid])
else:
mgci.append(ggid2connection[gid])
mgci = all2all(rr)
# mgci contains all the connection info needed by the balanced distribution
# create mitrals and granules, split and register and create synapses
nmc.dc.mk_mitrals(model) # whole cells
nmc.build_granules(model)
for gid in gid2pieces:
if gid < params.Nmitral:
split.splitmitral(gid, model.mitrals[gid], gid2pieces[gid])
pc.multisplit()
nmc.register_mitrals(model)
nmc.register_granules(model)
# build_synapses() ... use mgci to build explicitly
model.mgrss = {}
for r in mgci:
for cil in mgci[r]:
for ci in cil:
if not model.mgrss.has_key(mgrs.mgrs_gid(ci[0], ci[3], ci[6])):
rsyn = mgrs.mk_mgrs(*ci[0:7])
if rsyn:
model.mgrss.update({rsyn.md_gid: rsyn})
nmultiple = int(pc.allreduce(mgrs.multiple_cnt(), 1))
if rank == 0:
print 'nmultiple = ', nmultiple
detectors = h.List("AmpaNmda")
util.elapsed('%d ampanmda for reciprocalsynapses constructed' %
int(pc.allreduce(detectors.count(), 1)))
detectors = h.List("FastInhib")
util.elapsed('%d fi for reciprocalsynapses constructed' %
int(pc.allreduce(detectors.count(), 1)))
detectors = h.List("ThreshDetect")
util.elapsed('%d ThreshDetect for reciprocalsynapses constructed' %
int(pc.allreduce(detectors.count(), 1)))
if rank == 0: print 'multisplit_distrib time ', h.startsw() - enter