import batchOpenMPI
#defining function
def f_org(x) :
return x ** 2
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)
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
"""
example with 'calls_expected' in addtoBatch used
"""
import batchOpenMPI
def f_mult(x) :
return x*2.0
f = batchOpenMPI.batchFunction(f_mult) #creating function wrapper
batchOpenMPI.begin_MPI_loop() # both the workers and the master process run the same code up until here
f.addtoBatch(4,calls_expected=4)
batchOpenMPI.processBatch() #get the workers to calculate all the inputs
res = [f(4),f(4),f(4)]
print(res)
#another test
f.addtoBatch(1)
batchOpenMPI.processBatch() #get the workers to calculate all the inputs
res = f(1), f(1)
batchOpenMPI.end_MPI_loop(print_stats=True) #releases workers
print("*** jobs executed by workers should be 2 ,(5 calls made),jobs uncollected should = 1, jobs_master=1")
#! /usr/bin/env python
import numpy, tuning_setups, batchOpenMPI, plotlib
from optTune import tMOPSO
from matplotlib import pyplot
batchOpenMPI.begin_MPI_loop()
#adjust which CEC problem to tune under, etcetera in tuning_setups.py
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]
"""
example with multiRef
"""
import batchOpenMPI
def f_mult(x) :
return x*2.0
f = batchOpenMPI.batchFunction(f_mult,multiRef=True) #creating function wrapper
batchOpenMPI.begin_MPI_loop(print_launch_messages=False) # both the workers and the master process run the same code up until here
no = range(10) + range(10) # creates [0,1,2,3,4,5,6,7,8,9] x 2
for i in no :# adding all f_inv input and queing them for parallel processing
f.addtoBatch(i)
batchOpenMPI.processBatch() #get the workers to calculate all the inputs
res = [] #used for storing results
for i in no :
res.append(f(i))
print(res)
batchOpenMPI.end_MPI_loop(print_stats=True) #releases workers
print("*** jobs executed by workers should be %i, out of the total of %i" % (len(no)/2,len(no)) )
"simulates an external program that writes its output to file"
f = file('results.txt','w')
f.write(str(x ** 2))
f.close()
def fun_org(x) :
ex_prog(x)
f = file('results.txt','r')
res = float(f.readline().strip())
f.close()
return res
fun = batchOpenMPI.batchFunction(fun_org) #creating function wrapper
batchOpenMPI.WorkingDir_base = 'workspace' #giving each worker a directory to workin.
batchOpenMPI.begin_MPI_loop() #split workers and master
no = range(10) #creating inputs
print("""f(x) = x ** 2
the results will be written to file, and as the result file name will be the same.
each process will be given its own workspace using batchOpenMPI.WorkingDir_base""")
print("\ninputs :" + str(no))
for i in no :# adding all f_inv input and queing them for parallel processing
fun.addtoBatch(i)
batchOpenMPI.processBatch() #get the workers to calculate all the inputs
res = [] #used for storing results
for i in no :
import batchOpenMPI
batchOpenMPI.begin_MPI_loop(print_launch_messages=False)
print('First call should fail:')
batchOpenMPI.showStats()
print('Second call after end_MPI_loop should work')
batchOpenMPI.end_MPI_loop(print_stats=True)
