return x + 1
def h_org(x) :
return x - 0.2
# creating batch functions
f = batchOpenMPI.batchFunction(f_org)
g = batchOpenMPI.batchFunction(g_org)
h = batchOpenMPI.batchFunction(h_org)
batchOpenMPI.begin_MPI_loop()
# building processing que
bi = g.addtoBatch(3,2)
print(f.addtoBatch(bi))
print(h.addtoBatch(bi))
batchOpenMPI.processBatch() #get the workers to calculate all the inputs
# now actuall code
print (
"""
f(x) = x ** 2
g(x) = x + 1
h(x) = x - 0.2
f(g(3)) = %f
h(g(3)) = %f
""" ) % (f(g(3)), h(g(3)))
batchOpenMPI.end_MPI_loop(print_stats=True) #releases workers
print('** nothing should be solved on the masters')
runAlg = tuning_setups.batchOpenMPI_wrapper(tuning_setups.PSO_batch,
tuning_setups.prob_ID)
tuningOpt = tMOPSO(
optAlg=runAlg,
CPV_lb=tuning_setups.PSO_CPV_lb,
CPV_ub=tuning_setups.PSO_CPV_ub,
CPV_validity_checks=tuning_setups.PSO_CPV_validity_checks,
OFE_budgets=tuning_setups.PSO_OFE_budgets,
sampleSizes=tuning_setups.PSO_sampleSizes, #resampling size of 25
resampling_interruption_confidence=tuning_setups.PSO_alpha,
gammaBudget=tuning_setups.PSO_gammaBudget, #tuning budget
addtoBatch=runAlg.addtoBatch,
processBatch=batchOpenMPI.processBatch)
batchOpenMPI.end_MPI_loop(print_stats=True) #release workers, and print stats
print(tuningOpt)
OFE_budgets = [d.fv[0] for d in tuningOpt.PFA.designs]
Fmin_values = [d.fv[1] for d in tuningOpt.PFA.designs]
log_OFE_budgets = [d.xv[0] for d in tuningOpt.PFA.designs]
N_values = [int(d.xv[1]) for d in tuningOpt.PFA.designs]
w_values = [d.xv[2] for d in tuningOpt.PFA.designs]
c_p_values = [d.xv[3] for d in tuningOpt.PFA.designs]
c_g_values = [d.xv[4] for d in tuningOpt.PFA.designs]
plotlib.plot_PSO_results(OFE_budgets, Fmin_values, N_values, w_values,
c_p_values, c_g_values)
def g_org(x) :
return x + 1
def h_org(x) :
return x - 0.2
# creating batch functions
f = batchOpenMPI.batchFunction(f_org)
g = batchOpenMPI.batchFunction(g_org,True)
h = batchOpenMPI.batchFunction(h_org)
batchOpenMPI.begin_MPI_loop()
# building processing que
print(f.addtoBatch(g.addtoBatch(3)))
print(h.addtoBatch(g.addtoBatch(3)))
batchOpenMPI.processBatch() #get the workers to calculate all the inputs
# now actuall code
print (
"""
f(x) = x ** 2
g(x) = x + 1
h(x) = x - 0.2
f(g(3)) = %f
h(g(3)) = %f
""" ) % (f(g(3)), h(g(3)))
batchOpenMPI.end_MPI_loop(print_stats=True)
print('** nothing should be solved on the masters')
"""
example of how2use batchOpenMPI,
simple example
"""
import batchOpenMPI
def f_inv_org(x) :
"function returns the inverse of a number"
return 1.0/x
f_inv = batchOpenMPI.batchFunction(f_inv_org, permissible_exceptions = [ZeroDivisionError]) #creating function wrapper
batchOpenMPI.begin_MPI_loop() # both the workers and the master process run the same code up until here
print('this example script prints in the inverse of the integers from 0 to 9')
no = range(10) # creates [0,1,2,3,4,5,6,7,8,9]
for i in no :# adding all f_inv input and queing them for parallel processing
f_inv.addtoBatch(i)
batchOpenMPI.processBatch() #get the workers to calculate all the inputs
res = [] #used for storing results
for i in no :
try :
res.append(f_inv(i))
except ZeroDivisionError :
print("couldn't inverse " + str(i) + " due to 0 division error")
print(res)
batchOpenMPI.end_MPI_loop(print_stats=True) #release workers, and print stats