使用字符串vs字符数组时性能有多少差异？ [英] How much performance difference when using string vs char array?

问题描述

我有以下代码：

char fname[255] = {0}
snprintf(fname, 255, "%s_test_no.%d.txt", baseLocation, i);

vs

std::string fname = baseLocation + "_test_no." + std::to_string(i) + ".txt";

哪个表现更好？第二个涉及临时创建吗？有什么更好的方法吗？

Which one performs better? Does the second one involve temporary creation? Is there any better way to do this?

推荐答案

让我们运行数字：

代码（我使用了 PAPI 计时器）

The code (I used PAPI Timers)

#include <iostream>
#include <string>
#include <stdio.h>
#include "papi.h"
#include <vector>
#include <cmath>
#define TRIALS 10000000

class Clock
{
  public:
    typedef long_long time;
    time start;
    Clock() : start(now()){}
    void restart(){ start = now(); }
    time usec() const{ return now() - start; }
    time now() const{ return PAPI_get_real_usec(); }
};


int main()
{
  int eventSet = PAPI_NULL;
  PAPI_library_init(PAPI_VER_CURRENT);
  if(PAPI_create_eventset(&eventSet)!=PAPI_OK) 
  {
    std::cerr << "Failed to initialize PAPI event" << std::endl;
    return 1;
  }

  Clock clock;
  std::vector<long_long> usecs;

  const char* baseLocation = "baseLocation";
  //std::string baseLocation = "baseLocation";
  char fname[255] = {};
  for (int i=0;i<TRIALS;++i)
  {
    clock.restart();
    snprintf(fname, 255, "%s_test_no.%d.txt", baseLocation, i);
    //std::string fname = baseLocation + "_test_no." + std::to_string(i) + ".txt";
    usecs.push_back(clock.usec());
  }

  long_long sum = 0;
  for(auto vecIter = usecs.begin(); vecIter != usecs.end(); ++vecIter)
  {
    sum+= *vecIter;
  }

  double average = static_cast<double>(sum)/static_cast<double>(TRIALS);
  std::cout << "Average: " << average << " microseconds" << std::endl;

  //compute variance
  double variance = 0;
  for(auto vecIter = usecs.begin(); vecIter != usecs.end(); ++vecIter)
  {
    variance += (*vecIter - average) * (*vecIter - average);
  }

  variance /= static_cast<double>(TRIALS);
  std::cout << "Variance: " << variance << " microseconds" << std::endl;
  std::cout << "Std. deviation: " << sqrt(variance) << " microseconds" << std::endl;
  double CI = 1.96 * sqrt(variance)/sqrt(static_cast<double>(TRIALS));
  std::cout << "95% CI: " << average-CI << " usecs to " << average+CI << " usecs" << std::endl;  
}

播放注释以获取一种或另一种方式。
用编译行在我的机器上对这两种方法进行1000万次迭代：

Play with the comments to get one way or the other. 10 million iterations of both methods on my machine with the compile line:

g ++ main.cpp -lpapi -DUSE_PAPI -std = c ++ 0x -O3

g++ main.cpp -lpapi -DUSE_PAPI -std=c++0x -O3

使用char数组：

Average: 0.240861 microseconds
Variance: 0.196387microseconds
Std. deviation: 0.443156 microseconds
95% CI: 0.240586 usecs to 0.241136 usecs

使用字符串方法：

Average: 0.365933 microseconds
Variance: 0.323581 microseconds
Std. deviation: 0.568842 microseconds
95% CI: 0.365581 usecs to 0.366286 usecs

在装有MY代码和MY编译器设置的机器上，我看到移到字符串时速度降低了50％。使用以下公式，字符数组比字符串的速度提高了34％：

So at least on MY machine with MY code and MY compiler settings, I saw about a 50% slowdown when moving to strings. that character arrays incur a 34% speedup over strings using the following formula:

（（字符串的时间）-（char数组的时间））/（字符串的时间）

((time for string) - (time for char array) ) / (time for string)

哪个给出了方法之间的时间差，以单独字符串的时间百分比表示。我原来的百分比是正确的；我改用字符数组方法作为参考点，当移至字符串时，它显示速度降低了52％，但我发现它具有误导性。

Which gives the difference in time between the approaches as a percentage on time for string alone. My original percentage was correct; I used the character array approach as a reference point instead, which shows a 52% slowdown when moving to string, but I found it misleading.

关于我如何做错的所有注释：）

I'll take any and all comments for how I did this wrong :)

Average: 0.338876 microseconds
Variance: 0.853823 microseconds
Std. deviation: 0.924026 microseconds
95% CI: 0.338303 usecs to 0.339449 usecs

字符数组

character array

Average: 0.239083 microseconds
Variance: 0.193538 microseconds
Std. deviation: 0.439929 microseconds
95% CI: 0.238811 usecs to 0.239356 usecs

因此字符数组这种方法的速度仍然要快得多，但速度要慢得多。在这些测试中，速度要快29％。

So the character array approach remains significantly faster although less so. In these tests, it was about 29% faster.

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