问题描述
我试图仅执行一个初始化过程来测量python字典,cythonized python字典和cythonized cpp std :: unordered_map之间的性能.如果将cythonized cpp代码编译好,我认为它应该比纯python版本更快.我使用4种不同的场景/符号选项进行了测试:
I was trying to measure the performance between python dictionaries, cythonized python dictionaries and cythonized cpp std::unordered_map doing only a init procedure. If the cythonized cpp code is compiled I thought it should be faster than the pure python version. I did a test using 4 different scenario/notation options:
- 使用std :: unordered_map和 Cython图书符号的Cython CPP代码(定义一对并使用插入方法)
- 使用std :: unordered_map和python表示法(map [key] =值)的Cython CPP代码
- 使用python字典(map [key] =值)的Cython代码(键入的代码)
- 纯python代码
- Cython CPP code using std::unordered_map and Cython book notation (defining a pair and using insert method)
- Cython CPP code using std::unordered_map and python notation (map[key] = value)
- Cython code (typed code) using python dictionaries (map[key] = value)
- Pure python code
我期待看到cython代码如何胜过纯python代码,但是在这种情况下并没有改善.可能是什么原因?我正在使用Cython-0.22,python-3.4和g ++-4.8.
I was expecting see how cython code outperforms pure python code, but in this case there is not improvement. Which could be the reason? I'm using Cython-0.22, python-3.4 and g++-4.8.
我使用timeit得到了这个执行时间(秒):
I got this exec time (seconds) using timeit:
- Cython CPP的书写方式-> 15.696417249999968
- Cython CPP python表示法-> 16.481350984999835
- Cython python表示法-> 18.585355018999962
- 纯python-> 18.162724677999904
代码在这里,您可以使用它:
Code is here and you can use it:
cython -a map_example.pyx
python3 setup_map.py build_ext --inplace
python3 use_map_example.py
map_example.pyx
map_example.pyx
from libcpp.unordered_map cimport unordered_map
from libcpp.pair cimport pair
cpdef int example_cpp_book_notation(int limit):
cdef unordered_map[int, int] mapa
cdef pair[int, int] entry
cdef int i
for i in range(limit):
entry.first = i
entry.second = i
mapa.insert(entry)
return 0
cpdef int example_cpp_python_notation(int limit):
cdef unordered_map[int, int] mapa
cdef pair[int, int] entry
cdef int i
for i in range(limit):
mapa[i] = i
return 0
cpdef int example_ctyped_notation(int limit):
mapa = {}
cdef int i
for i in range(limit):
mapa[i] = i
return 0
setup_map.py
setup_map.py
from distutils.core import setup
from distutils.extension import Extension
from Cython.Build import cythonize
from Cython.Distutils import build_ext
import os
os.environ["CC"] = "g++"
os.environ["CXX"] = "g++"
modules = [Extension("map_example",
["map_example.pyx"],
language = "c++",
extra_compile_args=["-std=c++11"],
extra_link_args=["-std=c++11"])]
setup(name="map_example",
cmdclass={"build_ext": build_ext},
ext_modules=modules)
use_map_example.py
use_map_example.py
import map_example
C_MAXV = 100000000
C_NUMBER = 10
def cython_cpp_book_notation():
x = 1
while(x<C_MAXV):
map_example.example_cpp_book_notation(x)
x *= 10
def cython_cpp_python_notation():
x = 1
while(x<C_MAXV):
map_example.example_cpp_python_notation(x)
x *= 10
def cython_ctyped_notation():
x = 1
while(x<C_MAXV):
map_example.example_ctyped_notation(x)
x *= 10
def pure_python():
x = 1
while(x<C_MAXV):
map_a = {}
for i in range(x):
map_a[i] = i
x *= 10
return 0
if __name__ == '__main__':
import timeit
print("Cython CPP book notation")
print(timeit.timeit("cython_cpp_book_notation()", setup="from __main__ import cython_cpp_book_notation", number=C_NUMBER))
print("Cython CPP python notation")
print(timeit.timeit("cython_cpp_python_notation()", setup="from __main__ import cython_cpp_python_notation", number=C_NUMBER))
print("Cython python notation")
print(timeit.timeit("cython_ctyped_notation()", setup="from __main__ import cython_ctyped_notation", number=C_NUMBER))
print("Pure python")
print(timeit.timeit("pure_python()", setup="from __main__ import pure_python", number=C_NUMBER))
推荐答案
我从您的代码中获得的时间(在更正了python * 10缩进:)之后)
My timings from your code (after correcting that python *10 indent :) ) are
Cython CPP book notation
21.617647969018435
Cython CPP python notation
21.229907534987433
Cython python notation
24.44413448998239
Pure python
23.609809526009485
基本上每个人都在同一个球场上,CPP版本的优势不大.
Basically everyone is in the same ballpark, with a modest edge for the CPP versions.
我的机器没什么特别的,通常的Ubuntu 14.10、0.202 Cython,3.42 Python.
Nothing special about my machine, the usual Ubuntu 14.10, 0.202 Cython, 3.42 Python.
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