在 Python 中并行化四个嵌套循环

2022-01-12 00:00:00 python python-multiprocessing multiprocessing joblib

问题描述

我有一个相当简单的嵌套 for 循环，它遍历四个数组:

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

也许这并不是在 4D 网格上执行计算的最有效方式.我知道 joblib 能够并行化两个嵌套的 for 循环，例如 this，但我无法将其推广到四个嵌套循环.有什么想法吗?

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?

解决方案

我通常使用这种形式的代码:

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)

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