为什么此生成器表达式函数比循环版本慢?

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问题描述

我一直在根据生成器表达式比普通循环更有效的理论进行操作.但是随后我遇到了以下示例:写一个给定数字 N 的函数，并且某些因子 ps 返回下所有数字的总和N 是至少一个因子的倍数.

I have been operating under the theory that generator expressions tend to be more efficient than normal loops. But then I ran into the following example: write a function which given a number, N, and some factors, ps, returns the sum of all the numbers under N that are a multiple of at least one factor.

这是一个循环版本和一个较短的生成器表达式版本:

Here is a loop version and a shorter generator expression version:

def loops(N, ps):
    total_sum = 0
    for i in xrange(N):
        for p in ps:
            if i%p == 0:
                total_sum += i
                break
    return total_sum

def genexp(N, ps):
    return sum(i for i in xrange(N)
               if any(i%p == 0 for p in ps))

我希望两者的性能大致相同，也许理解版本会更快一些，但是我没想到的是:

I'd expect the two to perform roughly equal, with maybe the comprehension version a little faster, but what I didn't expect was this:

for func in ('loops', 'genexp'):
    print func, timeit.timeit('%s(100000, [3,5,7])' % func,
                              number=100,
                              setup='from __main__ import %s' % func)


loops 2.82878184319
genexp 10.1663100719

慢4倍甚至还没有接近！为什么?我有什么误会?

4x slower isn't even close! Why? What am I misunderstanding?

推荐答案

首先:生成器表达式是内存有效的，不一定是速度有效的.

First of all: generator expressions are memory efficient, not necessarily speed efficient.

您的紧凑型 genexp()版本较慢，原因有两个:

Your compact genexp() version is slower for two reasons:

生成器表达式是使用新作用域(如新函数)实现的.您正在生成 N 个新范围，每个 any()测试一个.创建新的作用域并再次将其拆解是相对昂贵的，当然，在循环中完成该操作之后，再与不执行此操作的代码进行比较.

Generator expressions are implemented using a new scope (like a new function). You are producing N new scopes, one for for each any() test. Creating a new scope and tearing it down again is relatively expensive, certainly when done in a loop and then compared with code that doesn't do this.

sum()和 any()名称是要查询的其他全局变量.对于 any()，这是每个测试额外的 N 个全局查询.必须在字典中查找全局变量，而在C数组中通过索引查找局部变量(这非常快).

The sum() and any() names are additional globals to be looked up. In the case of any(), that's an additional N global lookups per test. Globals must be looked up in a dictionary, versus locals which are looked up by index in a C-array (which is very fast).

后者只是一个很小的组成部分，大部分成本在于创建和销毁框架(范围)；如果创建一个版本，其中 _any 和 _sum 是该函数的本地变量，则性能会有所改善:

The latter is but a small component, most of the cost lies in creating and destroying frames (scopes); if you create a version where _any and _sum are locals to the function you get but a small improvement in performance:

>>> def genexp_locals(N, ps, _any=any, _sum=sum):
...     return _sum(i for i in xrange(N)
...                if _any(i%p == 0 for p in ps))
...
>>> for func in ('loops', 'genexp', 'genexp_locals'):
...     print func, timeit.timeit('%s(100000, [3,5,7])' % func,
...                               number=100,
...                               setup='from __main__ import %s' % func)
...
loops 2.00835800171
genexp 6.45241594315
genexp_locals 6.23843789101

我没有为 xrange 创建本地以保持相同的外观.从技术上讲，生成器表达式代码对象将 _any 名称作为闭包而不是局部的名称进行查找，其速度不像全局查找那样慢，但也不如局部查找那么快.

I didn't create a local for xrange to keep that aspect the same. Technically speaking, the _any name is looked up as a closure, not a local, by the generator expression code object, which are not as slow as global lookups but not quite as speedy as a local lookup either.

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