为什么我的python 3实现比我在C++中编写的快得多？

Question

我知道C++应该比Python 3快得多,因为它是一种编译语言而不是解释语言.

我编写了两个程序,使用蒙特卡罗模拟来计算Pi,一个在Python 3中,另一个在C++中.

Python的结果比C++快大约16倍.如下图所示,重复值为(10,000,000),Python需要8.5秒,而C++需要137.4秒.

我是C++的新手,但我找不到解释这种行为的在线帖子.

根据这篇文章, C++一般应该比Python快10倍 - 100倍,这显然不是我的情况.

请帮助我理解为什么Python在我的情况下比C++快得多.

我的结果:

C++中的蒙特卡罗模拟(Pi估计)

Python 3中的蒙特卡罗模拟(Pi估计)

Python源代码:

from random import random
import time
import sys

class MonteCarloSimulator(object):

    def __init__(self, value):
        self.value = value

        if sys.platform == "win32":
            self.G = ''
            self.R = ''
            self.END = ''
        else:
            self.G = '\033[92m'
            self.R = '\033[1;31m'
            self.END = '\033[0m'

    def unit_circle(self, x, y):
        if (x ** 2 + y ** 2) <= 1:
            return True
        else:
            return False

    def simulate(self):
        print("\nProcessing calculations with a repetition value of " + self.R +
        str(self.value) + self.END + " times.")

        area_of_circle = 0
        area_of_square = 0

        start = time.clock()

        for i in range(1, self.value):
            x = random()
            y = random()

            if self.unit_circle(x, y):
                area_of_circle += 1
            area_of_square += 1

        pi = (area_of_circle * 4) / area_of_square

        runtime = time.clock() - start

        print("\tCalculated Pi = " + self.G + str(pi) + self.END +
        " ({0} seconds, {1} minutes)".format(round(runtime, 10),
        round(runtime / 60, 10)))

        print("Estimated Num of Pi is off by", abs(pi - 3.14159265359))

def main():
    values = [1000, 10000, 100000, 1000000, 10000000, 100000000,1000000000, 10000000000]
    for value in values: MonteCarloSimulator(value).simulate()
if __name__ == "__main__":
    try:
        main()
    except KeyboardInterrupt:
        print("\nQuitting...")
        sys.exit(1)

C++源代码:

#include <iostream>                     // std library
#include <random>                       // random number generator
#include <ctime>                        // calculating runtime
#include <cmath>                        // absolute value function
#include "MonteCarloSimmulation.hpp"    // function prototypes

using namespace std;

const double g_PI {3.141592653589793238463};

int main()
{
    // repitition values
    long values[5] = {1000, 10000, 100000, 1000000, 10000000};//, 100000000, 1000000000, 10000000000};

    // runs the simulation with the different repetition values
    for (auto value : values)
        simulate(value);

    cout << "\nPress return to exit";
    cin.get();

    return 0;
}

/**
 * The actual simulation
 */
void simulate(unsigned long value)
{
    // start time for calculating runtime
    const clock_t startTime = clock();

    // area's variables
    unsigned long area_of_circle = 0;
    unsigned long area_of_square = 0;

    // print the repitiion value
    cout << "\nProcessing calculations with a repetition value of " << value <<
    " times." << endl;

    for (unsigned long i = 0; i != value; i++)
    {
        // gets random values from 0 to 1 for (x) and (y)
        float x = randomFloat();
        float y = randomFloat();

        // checks if (x, y) are in a unit circle, if so increment circle area
        if (unit_circle(x, y))
            area_of_circle++;
        area_of_square++;
    }

    // pi = area of circle * 4 / area of square
    double calculatedPi = static_cast<double>(area_of_circle * 4) / area_of_square;

    float endTime = static_cast<float>(clock() - startTime) / CLOCKS_PER_SEC;

    // prints the value of calculated pi
    cout << "\tCalculated Value of Pi: " << calculatedPi <<
    " (" << endTime << " seconds, " << endTime/60 << " minutes)" << endl;

    // difference between the calc value and pi
    cout << "Estimated Num of Pi is off by " << abs(calculatedPi - g_PI) << '\n';
}

/**
 * returns a random number from 0 to 1
 */
float randomFloat()
{
    random_device rd;
    default_random_engine generator(rd()); // rd() provides a random seed
    uniform_real_distribution<float> distribution(0,1);

    float x = distribution(generator);

    return x;
}

/**
 * checks if the two input parameters are inside a unit circle
 */
bool unit_circle(float x, float y)
{
    if ((x*x + y*y) <= 1)
        return true;
    else
        return false;
}

Answer 1

主要问题是您正在为C++代码中的每个随机数重新安排一个随机数生成器.另外,您没有在启用优化的情况下进行编译(-O3).

我将随机数生成器的初始化移到了函数之外randomFloat(同样,你可以static在函数内部使用变量):

random_device rd;
default_random_engine generator(rd()); // rd() provides a random seed
uniform_real_distribution<float> distribution(0,1);

float randomFloat() {
    float x = distribution(generator);
    return x;
}

-O3现在用C++ 编译,比C++快得多

另一种可能性是python和C++代码使用不同的随机数生成器.Python random模块(此处为C代码)使用MT19937 Mersenne Twister随机数生成器,这是一种快速PRNG,专门针对蒙特卡罗等数值问题进行了优化; default_random_engineC++中的算法是实现定义的.正如Melak47所指出的,你可以强制在C++中使用MT19937 PRNG:

mt19937 generator(rd());

要么

mt19937_64 generator(rd());

PS,Python表现优于C++并不是闻所未闻; C++算法重视通用性,而Python算法通常针对某些用例进行了优化.例如,请参阅有关子字符串匹配的此问题.