如何在Python中并行化列表理解计算？(How to parallelize list-comprehension calculations in Python?)

 列表理解和映射计算至少在理论上应该相对容易并行化：列表理解中的每个计算都可以独立于所有其他元素的计算来完成。 例如在表达式中 
 
[ x*x for x in range(1000) ]
 
 每个x * x-计算可以（至少在理论上）并行进行。 
 
 我的问题是：是否有任何Python-Module / Python-Implementation / Python编程 - 技巧来并行化列表理解计算（以便使用所有16​​/32 / ...核心或通过计算机网格分布计算或在云端）？ 

Both list comprehensions and map-calculations should -- at least in theory -- be relatively easy to parallelize: each calculation inside a list-comprehension could be done independent of the calculation of all the other elements. For example in the expression
 
[ x*x for x in range(1000) ]
 
each x*x-Calculation could (at least in theory) be done in parallel.
 
My question is: Is there any Python-Module / Python-Implementation / Python Programming-Trick to parallelize a list-comprehension calculation (in order to use all 16 / 32 / ... cores or distribute the calculation over a Computer-Grid or over a Cloud)?

原文：https://stackoverflow.com/questions/5236364