Using ipython for parallel computing

from xieweizhong.tk
Using ipython for parallel computing
Wed 25 December 2013
By Xiewei Zhong

In Python.

Problem

In my work, I meet one problem: the time consuming during computing.
My Thought

At first, the GPU programming was occured in my mind. But, aftering trying, I found that the algorithm should be rewrite by myself using CUDA-c, and the helpful functions in scipy module can not be used. Therefore, I get rid of the mind, and begain to looking for the method: CPU-parallel programming.
IPython

Before, I only know that the ipython is a wonderful python programming environment. Now, the other powerful function of ipython make me shocked: Using IPython for parallel computing.

Below, the detail solutions are illustrated.

from scipy.optimize import minimize
from IPython.parallel import Client # The most important thing
import numpy as np

def func(x):

"My function for minimize"

def myminimize(x):

"""My function to wrap the minimize funtion"""
...
...  # To determine the x0
res = minimize(func, x0, args=(x,), method='Nelder-Mead',
               options={'xtol': 1e-5, 'disp': False})
return res.x

if name == 'main':

x = list()
...  # initialize content of x
rc = Client()
dview = rc[:]
# set the global informations
dview['g_1'] = g_1
dview['np'] = np
dview['minimize'] = minimize
...
result = dview.map_sync(myminimize, x)

In the termial, typeipcluster start -n 16 at first, and then python yourcode.py