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cache_efficient
load balance output
f.sim.h('objref cvode') f.sim.h('cvode = new CVode()') f.sim.h.cvode.cache_efficient(0)
f.sim.runSim() # run parallel Neuron simulation
computation_time = f.pc.step_time() max_comp_time = f.pc.allreduce(computation_time, 2) min_comp_time = f.pc.allreduce(computation_time, 3) avg_comp_time = f.pc.allreduce(computation_time, 1)/f.nhosts load_balance = avg_comp_time/max_comp_time
#print 'node:',f.rank,' comp_time:',computation_time if f.rank==0: print 'max_comp_time:', max_comp_time print 'min_comp_time:', min_comp_time print 'avg_comp_time:', avg_comp_time print 'load_balance:',load_balance print '\nspike exchange time atp(run_time-comp_time): ', f.timing['runTime'] - max_comp_time
The text was updated successfully, but these errors were encountered:
added option for cache_efficient (issue #77); separate common runSim …
f67425c
…commands into preRun() (issue #83)
Added sim.loadBalance() func to calculate and print load balance info…
29df8d0
…rmation (issue #77)
salvadord
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cache_efficient
load balance output
set cache_efficient on
f.sim.h('objref cvode')
f.sim.h('cvode = new CVode()')
f.sim.h.cvode.cache_efficient(0)
f.sim.runSim() # run parallel Neuron simulation
computation_time = f.pc.step_time()
max_comp_time = f.pc.allreduce(computation_time, 2)
min_comp_time = f.pc.allreduce(computation_time, 3)
avg_comp_time = f.pc.allreduce(computation_time, 1)/f.nhosts
load_balance = avg_comp_time/max_comp_time
#print 'node:',f.rank,' comp_time:',computation_time
if f.rank==0:
print 'max_comp_time:', max_comp_time
print 'min_comp_time:', min_comp_time
print 'avg_comp_time:', avg_comp_time
print 'load_balance:',load_balance
print '\nspike exchange time atp(run_time-comp_time): ', f.timing['runTime'] - max_comp_time
The text was updated successfully, but these errors were encountered: