Eigen vs Numpy时间对比

有人说Eigen太慢了,用Numpy好一点,我们来看一看是不是这样的

Eigen C++ 程序 t_time.cpp

#include 
    
     #include 
     
      using namespace Eigen;void integer_time(){    int n_a_rows = 4000;    int n_a_cols = 3000;    int n_b_rows = n_a_cols;    int n_b_cols = 200;    MatrixXi a(n_a_rows, n_a_cols);    for(int i =0; i
     
    

使用cmake 来编译

cmake_minimum_required(VERSION 2.8)set(CMAKE_CXX_STANDARD 11)set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -O2 -DNDEBUG")find_package(Eigen3 REQUIRED) include_directories(    ${PROJECT_SOURCE_DIR}    ${EIGEN3_INCLUDE_DIRS}    )add_executable(a t_time.cpp)target_link_libraries(a ${Eigen3_LIBS})

或者直接编译:

g++ -std=c++11 -I/usr/include/eigen3 -O2 -DNDEBUG t_time.cpp -o a

输出结果:

1.15959integer time0.60962double time

Numpy 程序 t_time.py

#!/usr/bin/env python# coding=utf-8import numpy as npimport timen_a_rows = 4000n_a_cols = 3000n_b_rows = n_a_colsn_b_cols = 200a = np.arange(n_a_rows * n_a_cols).reshape(n_a_rows, n_a_cols)b = np.arange(n_b_rows * n_b_cols).reshape(n_b_rows, n_b_cols)print(a.dtype)start = time.time()d = np.dot(a, b)end = time.time()print "integer time  : {}".format(end - start)a = np.arange(n_a_rows * n_a_cols).reshape(n_a_rows, n_a_cols)*1.0b = np.arange(n_b_rows * n_b_cols).reshape(n_b_rows, n_b_cols)*1.0print(a.dtype)start = time.time()d = np.dot(a, b)end = time.time()print "double time : {}".format(end - start)

输出结果:

int64integer time  : 2.97904801369float64double time : 0.0668978691101

总结

• int 型的时候c++比较快, double型的时候numpy比较快
• 但两者都差不多
• numpy用起来比较舒服,代码量非常少
• C++ 使用了 -O2 -DNDEBUG 做编译优化, 不然时间都需要10几秒

C++ 没有使用编译优化时:

16.0714integer time18.6559double time