Python 内置 sum 函数 vs. for 循环性能 [英] Python built-in sum function vs. for loop performance

问题描述

我注意到 Python 的内置 sum 函数在对 1 000 000 个整数列表求和时比 for 循环快大约 3 倍:

I noticed that Python's built-in sum function is roughly 3x faster than a for loop when summing a list of 1 000 000 integers:

import timeit

def sum1():
    s = 0
    for i in range(1000000):
        s += i
    return s

def sum2():
    return sum(range(1000000))

print 'For Loop Sum:', timeit.timeit(sum1, number=10)
print 'Built-in Sum:', timeit.timeit(sum2, number=10)

# Prints:
# For Loop Sum: 0.751425027847
# Built-in Sum: 0.266746997833

这是为什么呢?sum是如何实现的?

Why is that? How is sum implemented?

推荐答案

速度差异实际上大于 3 倍，但是您通过首先创建一个包含 100 万个整数的巨大内存列表来减慢任一版本的速度.将其与计时赛分开:

The speed difference is actually greater than 3 times, but you slow down either version by first creating a huge in-memory list of 1 million integers. Separate that out of the time trials:

>>> import timeit
>>> def sum1(lst):
...     s = 0
...     for i in lst:
...         s += i
...     return s
... 
>>> def sum2(lst):
...     return sum(lst)
... 
>>> values = range(1000000)
>>> timeit.timeit('f(lst)', 'from __main__ import sum1 as f, values as lst', number=100)
3.457869052886963
>>> timeit.timeit('f(lst)', 'from __main__ import sum2 as f, values as lst', number=100)
0.6696369647979736

现在速度差已经上升到5倍以上了.

The speed difference has risen to over 5 times now.

for 循环作为解释的 Python 字节码执行.sum() 完全在 C 代码中循环.解释字节码和 C 代码之间的速度差异很大.

A for loop is executed as interpreted Python bytecode. sum() loops entirely in C code. The speed difference between interpreted bytecode and C code is large.

此外，如果 C 代码可以将总和保留在 C 类型中，则确保不会创建新的 Python 对象；这适用于 int 和 float 结果.

In addition, the C code makes sure not to create new Python objects if it can keep the sum in C types instead; this works for int and float results.

反汇编的 Python 版本是这样做的:

The Python version, disassembled, does this:

>>> import dis
>>> def sum1():
...     s = 0
...     for i in range(1000000):
...         s += i
...     return s
... 
>>> dis.dis(sum1)
  2           0 LOAD_CONST               1 (0)
              3 STORE_FAST               0 (s)

  3           6 SETUP_LOOP              30 (to 39)
              9 LOAD_GLOBAL              0 (range)
             12 LOAD_CONST               2 (1000000)
             15 CALL_FUNCTION            1
             18 GET_ITER            
        >>   19 FOR_ITER                16 (to 38)
             22 STORE_FAST               1 (i)

  4          25 LOAD_FAST                0 (s)
             28 LOAD_FAST                1 (i)
             31 INPLACE_ADD         
             32 STORE_FAST               0 (s)
             35 JUMP_ABSOLUTE           19
        >>   38 POP_BLOCK           

  5     >>   39 LOAD_FAST                0 (s)
             42 RETURN_VALUE        

除了解释器循环比 C 慢之外，INPLACE_ADD 将创建一个新的整数对象(超过 255，CPython 将小的 int 对象缓存为单例).

Apart from the interpreter loop being slower than C, the INPLACE_ADD will create a new integer object (past 255, CPython caches small int objects as singletons).

您可以在Python mercurial 代码存储库，但它在评论中明确指出:

You can see the C implementation in the Python mercurial code repository, but it explicitly states in the comments:

/* Fast addition by keeping temporary sums in C instead of new Python objects.
   Assumes all inputs are the same type.  If the assumption fails, default
   to the more general routine.
*/

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