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

问题描述

至少在理论上，列表推导和映射计算都应该相对容易并行化:列表推导内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

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) ]

每个x * x计算都可以(至少在理论上)并行完成.

each x*x-Calculation could (at least in theory) be done in parallel.

我的问题是:是否有任何Python模块/Python实现/Python编程技巧来并行化列表理解计算(以便使用所有16​​/32/...内核或在计算机上分发计算-网格还是通过云)?

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)?

推荐答案

正如Ken所说，它不能，但是使用2.6的

As Ken said, it can't, but with 2.6's multiprocessing module, it's pretty easy to parallelize computations.

import multiprocessing

try:
    cpus = multiprocessing.cpu_count()
except NotImplementedError:
    cpus = 2   # arbitrary default


def square(n):
    return n * n

pool = multiprocessing.Pool(processes=cpus)
print(pool.map(square, range(1000)))

文档中也有一些示例，展示了如何操作使用管理器，也应该允许分布式计算.

There are also examples in the documentation that show how to do this using Managers, which should allow for distributed computations as well.

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