matlab – Parfor for Python

matlab – Parfor for Python

The one built-in to python would be multiprocessing docs are here. I always use multiprocessing.Pool with as many workers as processors. Then whenever I need to do a for-loop like structure I use Pool.imap

As long as the body of your function does not depend on any previous iteration then you should have near linear speed-up. This also requires that your inputs and outputs are pickle-able but this is pretty easy to ensure for standard types.

Some code for your updated function just to show how easy it is:

from multiprocessing import Pool
from itertools import product

output = np.zeros((N,N))
pool = Pool() #defaults to number of available CPUs
chunksize = 20 #this may take some guessing ... take a look at the docs to decide
for ind, res in enumerate(pool.imap(Fun, product(xrange(N), xrange(N))), chunksize):
    output.flat[ind] = res

There are many Python frameworks for parallel computing. The one I happen to like most is IPython, but I dont know too much about any of the others. In IPython, one analogue to parfor would be or some of the other constructs in the documentation on quick and easy parallelism.

matlab – Parfor for Python

Jupyter Notebook

To see an example consider you want to write the equivalence of this Matlab code on in Python

matlabpool open 4
parfor n=0:9
   for i=1:10000
       for j=1:10000

The way one may write this in python particularly in jupyter notebook. You have to create a function in the working directory (I called it which has the following

def func(n):
    for i in range(10000):
        for j in range(10000):

Then I go to my Jupyter notebook and write the following code

import multiprocessing  
import FunForParFor

if __name__ == __main__:
    pool = multiprocessing.Pool(processes=4), range(10))

This has worked for me! I just wanted to share it here to give you a particular example.

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