inline_expansion_test.py

#!/usr/bin/env python

from benchmarker import Benchmarker
from collections import Counter


with Benchmarker(width=20) as bm:
    counters = [Counter(), Counter(), Counter(), Counter()]

    with bm('double loop'):
        for i in xrange(10000000):
            for counter in counters:
                counter['hoge'] += 1

    counter1 = Counter()
    counter2 = Counter()
    counter3 = Counter()
    counter4 = Counter()

    with bm('single loop'):
        for i in xrange(10000000):
            counter1['hoge'] += 1
            counter2['hoge'] += 1
            counter3['hoge'] += 1
            counter4['hoge'] += 1

    counter4 = Counter()
    counter5 = Counter()
    counter6 = Counter()
    counter7 = Counter()

    def count():
        counter4['hoge'] += 1
        counter5['hoge'] += 1
        counter6['hoge'] += 1
        counter7['hoge'] += 1

    with bm('function invoke'):
        for i in xrange(10000000):
            count()

    def count(c):
        c['hoge'] += 1

    with bm('double loop with map'):
        for i in xrange(10000000):
            map(count, counters)

インライン展開みたいなシンプルなテクニックが通用する結果に
map 遅すぎワロタ

## benchmarker:       release 3.0.1 (for python)                                                                                                                                                                                                              
## python platform:   linux2 [GCC 4.8.2]                                                                                                                                                                                                                      
## python version:    2.7.6                                                                                                                                                                                                                                                               
## python executable: /home/mumumu/benchmark/bin/python                                                                                                                                                                                                                                   

##                       user       sys     total      real                                                                                                                                                                                                                               
double loop           13.6900    0.0200   13.7100   13.7069                                                                                                                                                                                                                                                                  
single loop           12.2100    0.0300   12.2400   12.2302                                                                                                                                                                                                                                                                  
function invoke       12.7300    0.0100   12.7400   12.7385                                                                                                                                                                                                                                                                  
double loop with map  17.4600    0.0400   17.5000   17.4840

## Ranking               real
single loop           12.2302 (100.0%) *************************
function invoke       12.7385 ( 96.0%) ************************
double loop           13.7069 ( 89.2%) **********************
double loop with map  17.4840 ( 70.0%) *****************

## Ratio Matrix          real    [01]    [02]    [03]    [04]
[01] single loop      12.2302  100.0%  104.2%  112.1%  143.0%
[02] function invoke  12.7385   96.0%  100.0%  107.6%  137.3%
[03] double loop      13.7069   89.2%   92.9%  100.0%  127.6%
[04] double loop with map  17.4840   70.0%   72.9%   78.4%  100.0%