Parallelizing four nested loops in Python

Question

I have a fairly straightforward nested for loop that iterates over four arrays:

for a in a_grid:
    for b in b_grid:
        for c in c_grid:
            for d in d_grid:
                do_some_stuff(a,b,c,d)  # perform calculations and write to file

Maybe this isn't the most efficient way to perform calculations over a 4D grid to begin with. I know joblib is capable of parallelizing two nested for loops like this, but I'm having trouble generalizing it to four nested loops. Any ideas?

Answer 1

I usually use code of this form:

#!/usr/bin/env python3
import itertools
import multiprocessing

#Generate values for each parameter
a = range(10)
b = range(10)
c = range(10)
d = range(10)

#Generate a list of tuples where each tuple is a combination of parameters.
#The list will contain all possible combinations of parameters.
paramlist = list(itertools.product(a,b,c,d))

#A function which will process a tuple of parameters
def func(params):
  a = params[0]
  b = params[1]
  c = params[2]
  d = params[3]
  return a*b*c*d

#Generate processes equal to the number of cores
pool = multiprocessing.Pool()

#Distribute the parameter sets evenly across the cores
res  = pool.map(func,paramlist)

Answer 2

If you use a tool that makes it easy to parallelize two nested loops, but not four, you can use itertools.product to reduce four nested for loops into two:

from itertools import product

for a, b in product(a_grid, b_grid):
    for c, d in product(c_grid, d_grid):
        do_some_stuff(a, b, c, d)

Answer 3

The number of jobs is not related to the number of nested loops. In that other answer, it happened to be n_jobs=2 and 2 loops, but the two are completely unrelated.

Think of it this way: You have a bunch of function calls to make; in your case (unrolling the loops):

do_some_stuff(0,0,0,0)
do_some_stuff(0,0,0,1)
do_some_stuff(0,0,0,2)
do_some_stuff(0,0,1,0)
do_some_stuff(0,0,1,1)
do_some_stuff(0,0,1,2)
...

and you want to distribute those function calls across some number of jobs. You could use 2 jobs, or 10, or 100, it doesn't matter. Parallel takes care of distributing the work for you.