# Python: Computation time Issue with mpi4py

I am using in Python mpi4py to process in parallel 20 minimization functions. Each of the 20 worker processes the same algorithm but with different random initial starting values. What I don't understand is the time it takes to process my job. I expect each of the workers to take roughly the same amount of time to minimize the function but it varies from 300 seconds to 2000 seconds. Now I am not sure why this is the case. I am wondering if I am specifying my MPI job properly or this happens to be because it is meant to happen. Here's my code

from mpi4py import MPI
import os
import random
import nlopt

#Set the range for each of the variables (parameters)
X1_ = arange(1.01,1.99,.01)
X2_ = arange(0.01, 0.9, 0.01)

comm = MPI.COMM_WORLD
size = comm.Get_size()
rank = comm.Get_rank()

# Draw random values for each of the parameters
X1 = random.choice(X1_)
X2 = random.choice(X2_)

#Set up the lower and upper bound for each variables:
X1min = 1.05
X1max = 1.99
X2min = .01
X2max = 0.9999999

step=1e-8) #numgrad is a function that computes the gradient but irrelevant with a derivative-free algo
return myfunction([x[0], x[1]], data)

opt.set_lower_bounds([X1min, X2min])
opt.set_upper_bounds([X1max, X2max])
opt.set_min_objective(myfunc)
opt.set_xtol_rel(1e-8)
opt.maxeval = 10000
x = opt.optimize([X1, X2])
minf = opt.last_optimum_value()


Am I missing something in my MPI specification?

• Please reduce your code to a minimal test case that correctly reproduces the problem. That will make it much easier for someone to help you. Throwing a bunch of code at people and expecting them to identify the problem by reading it is unhelpful and can also be considered rude. – Kirill Aug 23 '14 at 0:29
• @Kirill agree! I removed a lot of "pointless" lines. Thanks – Plug4 Aug 23 '14 at 2:09