if rank > 0:
return
b = avgp[0] / maxp[0]
print 'Load Balance = %g' % b
print '\n ',
for i in header:
print '%12s' % i,
print '\n avg ',
for i in avgp:
print '%12.2f' % i,
print '\n max ',
for i in maxp:
print '%12.2f' % i,
print ''
if __name__ == '__main__':
import common
import util
common.nmitral = 1
common.ncell = 2
import net_mitral_centric as nmc
nmc.build_net_roundrobin(getmodel())
pc.spike_record(-1, spikevec, idvec)
from odorstim import OdorStim
from odors import odors
ods = OdorStim(odors['Apple'])
ods.setup(nmc.mitrals, 10., 20., 100.)
prun(200.)
util.finish()
def run(): parrun.prun(params.tstop) weightsave.weight_file(params.filename + '.weight.dat') util.finish()
header = ['comp','spk','split','gap']
for i in p:
avgp.append(pc.allreduce(i, 1)/nhost)
maxp.append(pc.allreduce(i, 2))
if rank > 0:
return
b = avgp[0]/maxp[0]
print 'Load Balance = %g'% b
print '\n ',
for i in header: print '%12s'%i,
print '\n avg ',
for i in avgp: print '%12.2f'%i,
print '\n max ',
for i in maxp: print '%12.2f'%i,
print ''
if __name__ == '__main__':
import common
import util
common.nmitral = 1
common.ncell = 2
import net_mitral_centric as nmc
nmc.build_net_roundrobin(getmodel())
pc.spike_record(-1, spikevec, idvec)
from odorstim import OdorStim
from odors import odors
ods = OdorStim(odors['Apple'])
ods.setup(nmc.mitrals, 10., 20., 100.)
prun(200.)
util.finish()
if rank == 0:
c = []
for i in r.values():
c += i
del r
#distribute by LPT
parts = lpt(c, npart)
print statistics(parts)
for i, p in enumerate(parts):
s.update({i: p[1]})
else:
del r
#send each partition to the proper rank
local = all2all(s)
del s
if rank == 0:
print "load_bal time %g" % (h.startsw() - elapse)
return local[0]
if __name__ == '__main__':
from util import serialize, finish
if True:
cx = [(10 * rank + i, 10 * rank + i) for i in range(1, 5)]
print cx
cx = load_bal(cx, nhost)
for r in serialize():
print 'rank %d ' % rank, cx
if nhost > 0:
finish()
def run():
parrun.prun(params.tstop)
weightsave.weight_file(params.filename + '.weight.dat')
util.finish()
if rank == 0:
c = []
for i in r.values():
c += i
del r
#distribute by LPT
parts = lpt(c, npart)
print statistics(parts)
for i,p in enumerate(parts):
s.update({i : p[1]})
else:
del r
#send each partition to the proper rank
local = all2all(s)
del s
if rank == 0:
print "load_bal time %g" % (h.startsw()-elapse)
return local[0]
if __name__ == '__main__':
from util import serialize, finish
if True:
cx = [(10*rank+i, 10*rank+i) for i in range(1,5)]
print cx
cx = load_bal(cx, nhost)
for r in serialize():
print 'rank %d '%rank, cx
if nhost > 0:
finish()
