[ Python 常見問題 ] Is there a simple process-based parallel map for python?

Source From Here 
Question 
I'm looking for a simple process-based parallel map for python. With native support map function, the performance: 

view plaincopy to clipboardprint?

1. In [1]: data = range(10000000)  
2.   
3. In [2]: time alist = list(map(lambda e:(e*5+1)/2, data))  
4. CPU times: user 1.48 s, sys: 47.6 ms, total: 1.53 s  
5. Wall time: 1.53 s  
6.   
7. In [3]: time olist = [(e*5+1)/2 for e in data]  
8. CPU times: user 862 ms, sys: 54 ms, total: 916 ms  
9. Wall time: 917 ms  

How-To 
I seems like what you need is the map method in multiprocessing.Pool(): 

map(func, iterable[, chunksize])

A parallel equivalent of the map() built-in function (it supports only one iterable argument though). It blocks till the result is ready. This method chops the iterable into a number of chunks which it submits to the process pool as separate tasks. The (approximate) size of these chunks can be specified by setting chunksize to a positive integer

Below is the sample code to show the usage: 
- test.py 

view plaincopy to clipboardprint?

1. #!/usr/bin/env python3  
2. import multiprocessing  
3. from datetime import datetime  
4.   
5. def f(e):  
6.     return (e*5+1)/2  
7.   
8. data = range(10000000)  
9. pool = multiprocessing.Pool()  
10. st = datetime.now()  
11. print("Start at {}".format(st))  
12. mlist = pool.map(f, data)  
13. diff = datetime.now() - st  
14. print("Done with {} ms".format(diff.microseconds/1000))  

Execution result: 

$ ./test.py
Start at 2018-12-25 22:11:46.570080
Done with 245.617 ms

Supplement 
* Python 文章收集 - multiprocessing 模塊介紹