一个更好的基准测试(我认为…)，编译器由于优化可以改变代码，因为分配的向量/数组的结果不会在任何地方使用。 结果:

$ g++ test.cpp -o test -O3 -march=native
$ ./test 
UseArray inner completed in 0.652 seconds
UseArray completed in 0.773 seconds
UseVector inner completed in 0.638 seconds
UseVector completed in 0.757 seconds
UseVectorPushBack inner completed in 6.732 seconds
UseVectorPush completed in 6.856 seconds
The whole thing completed in 8.387 seconds

编译器:

gcc version 6.2.0 20161019 (Debian 6.2.0-9)

CPU:

model name  : Intel(R) Core(TM) i7-3630QM CPU @ 2.40GHz

代码是:

#include <cstdlib>
#include <vector>

#include <iostream>
#include <string>

#include <boost/date_time/posix_time/ptime.hpp>
#include <boost/date_time/microsec_time_clock.hpp>

class TestTimer
{
    public:
        TestTimer(const std::string & name) : name(name),
            start(boost::date_time::microsec_clock<boost::posix_time::ptime>::local_time())
        {
        }

        ~TestTimer()
        {
            using namespace std;
            using namespace boost;

            posix_time::ptime now(date_time::microsec_clock<posix_time::ptime>::local_time());
            posix_time::time_duration d = now - start;

            cout << name << " completed in " << d.total_milliseconds() / 1000.0 <<
                " seconds" << endl;
        }

    private:
        std::string name;
        boost::posix_time::ptime start;
};

struct Pixel
{
    Pixel()
    {
    }

    Pixel(unsigned char r, unsigned char g, unsigned char b) : r(r), g(g), b(b)
    {
    }

    unsigned char r, g, b;
};

void UseVector(std::vector<std::vector<Pixel> >& results)
{
    TestTimer t("UseVector inner");

    for(int i = 0; i < 1000; ++i)
    {
        int dimension = 999;

        std::vector<Pixel>& pixels = results.at(i);
        pixels.resize(dimension * dimension);

        for(int i = 0; i < dimension * dimension; ++i)
        {
            pixels[i].r = 255;
            pixels[i].g = 0;
            pixels[i].b = 0;
        }
    }
}

void UseVectorPushBack(std::vector<std::vector<Pixel> >& results)
{
    TestTimer t("UseVectorPushBack inner");

    for(int i = 0; i < 1000; ++i)
    {
        int dimension = 999;

        std::vector<Pixel>& pixels = results.at(i);
            pixels.reserve(dimension * dimension);

        for(int i = 0; i < dimension * dimension; ++i)
            pixels.push_back(Pixel(255, 0, 0));
    }
}

void UseArray(Pixel** results)
{
    TestTimer t("UseArray inner");

    for(int i = 0; i < 1000; ++i)
    {
        int dimension = 999;

        Pixel * pixels = (Pixel *)malloc(sizeof(Pixel) * dimension * dimension);

        results[i] = pixels;

        for(int i = 0 ; i < dimension * dimension; ++i)
        {
            pixels[i].r = 255;
            pixels[i].g = 0;
            pixels[i].b = 0;
        }

        // free(pixels);
    }
}

void UseArray()
{
    TestTimer t("UseArray");
    Pixel** array = (Pixel**)malloc(sizeof(Pixel*)* 1000);
    UseArray(array);
    for(int i=0;i<1000;++i)
        free(array[i]);
    free(array);
}

void UseVector()
{
    TestTimer t("UseVector");
    {
        std::vector<std::vector<Pixel> > vector(1000, std::vector<Pixel>());
        UseVector(vector);
    }
}

void UseVectorPushBack()
{
    TestTimer t("UseVectorPush");
    {
        std::vector<std::vector<Pixel> > vector(1000, std::vector<Pixel>());
        UseVectorPushBack(vector);
    }
}


int main()
{
    TestTimer t1("The whole thing");

    UseArray();
    UseVector();
    UseVectorPushBack();

    return 0;
}

GNU's STL (and others), given vector<T>(n), default constructs a prototypal object T() - the compiler will optimise away the empty constructor - but then a copy of whatever garbage happened to be in the memory addresses now reserved for the object is taken by the STL's __uninitialized_fill_n_aux, which loops populating copies of that object as the default values in the vector. So, "my" STL is not looping constructing, but constructing then loop/copying. It's counter intuitive, but I should have remembered as I commented on a recent stackoverflow question about this very point: the construct/copy can be more efficient for reference counted objects etc..

So:

vector<T> x(n);

or

vector<T> x;
x.resize(n);

是-在许多STL实现中-类似于:

T temp;
for (int i = 0; i < n; ++i)
    x[i] = temp;

The issue being that the current generation of compiler optimisers don't seem to work from the insight that temp is uninitialised garbage, and fail to optimise out the loop and default copy constructor invocations. You could credibly argue that compilers absolutely shouldn't optimise this away, as a programmer writing the above has a reasonable expectation that all the objects will be identical after the loop, even if garbage (usual caveats about 'identical'/operator== vs memcmp/operator= etc apply). The compiler can't be expected to have any extra insight into the larger context of std::vector<> or the later usage of the data that would suggest this optimisation safe.

这可以与更明显的直接实现形成对比:

for (int i = 0; i < n; ++i)
    x[i] = T();

我们可以期待一个编译器优化。

为了更明确地解释vector行为的这一方面，可以考虑:

std::vector<big_reference_counted_object> x(10000);

显然，如果我们创建10000个独立对象，而不是创建10000个引用相同数据的对象，这是一个很大的区别。有一种合理的观点认为，保护普通c++用户不意外地做一些如此昂贵的事情的好处超过了现实世界中难以优化的拷贝构造的非常小的成本。

原始答案(供参考/理解评论): 没有机会。Vector和数组一样快，至少如果你合理地保留空间. ...

当我第一次看您的代码时，这很难说是一个公平的比较;我还以为你不是在比较苹果和苹果。所以我想，让构造函数和析构函数在所有测试中都被调用;然后比较。

const size_t dimension = 1000;

void UseArray() {
    TestTimer t("UseArray");
    for(size_t j = 0; j < dimension; ++j) {
        Pixel* pixels = new Pixel[dimension * dimension];
        for(size_t i = 0 ; i < dimension * dimension; ++i) {
            pixels[i].r = 255;
            pixels[i].g = 0;
            pixels[i].b = (unsigned char) (i % 255);
        }
        delete[] pixels;
    }
}

void UseVector() {
    TestTimer t("UseVector");
    for(size_t j = 0; j < dimension; ++j) {
        std::vector<Pixel> pixels(dimension * dimension);
        for(size_t i = 0; i < dimension * dimension; ++i) {
            pixels[i].r = 255;
            pixels[i].g = 0;
            pixels[i].b = (unsigned char) (i % 255);
        }
    }
}

int main() {
    TestTimer t1("The whole thing");

    UseArray();
    UseVector();

    return 0;
}

我的想法是，在这样的设置下，它们应该是完全相同的。事实证明，我错了。

UseArray completed in 3.06 seconds
UseVector completed in 4.087 seconds
The whole thing completed in 10.14 seconds

那么为什么会出现30%的性能损失呢?STL的所有内容都在头文件中，因此编译器应该能够理解所需的所有内容。

我的想法是，它是在循环如何初始化默认构造函数的所有值。所以我做了一个测试:

class Tester {
public:
    static int count;
    static int count2;
    Tester() { count++; }
    Tester(const Tester&) { count2++; }
};
int Tester::count = 0;
int Tester::count2 = 0;

int main() {
    std::vector<Tester> myvec(300);
    printf("Default Constructed: %i\nCopy Constructed: %i\n", Tester::count, Tester::count2);

    return 0;
}

结果如我所料:

Default Constructed: 1
Copy Constructed: 300

这显然是减速的根源，因为vector使用复制构造函数从默认构造的对象初始化元素。

这意味着，以下伪操作顺序发生在向量的构造过程中:

Pixel pixel;
for (auto i = 0; i < N; ++i) vector[i] = pixel;

由于编译器创建了隐式复制构造函数，扩展为:

Pixel pixel;
for (auto i = 0; i < N; ++i) {
    vector[i].r = pixel.r;
    vector[i].g = pixel.g;
    vector[i].b = pixel.b;
}

所以默认像素保持未初始化，而其余的初始化使用默认像素的未初始化值。

与New[]/Delete[]相比:

int main() {
    Tester* myvec = new Tester[300];

    printf("Default Constructed: %i\nCopy Constructed:%i\n", Tester::count, Tester::count2);

    delete[] myvec;

    return 0;
}

Default Constructed: 300
Copy Constructed: 0

它们都保留了未初始化的值，并且没有对序列进行两次迭代。

有了这些信息，我们如何进行测试呢?让我们试着重写隐式复制构造函数。

Pixel(const Pixel&) {}

结果呢?

UseArray completed in 2.617 seconds
UseVector completed in 2.682 seconds
The whole thing completed in 5.301 seconds

总而言之，如果你经常制作数百个向量:重新考虑你的算法。

在任何情况下，STL实现都不会因为某些未知的原因而变慢，它只是完全按照你的要求去做;希望你能明白。

我做了一些长期以来一直想做的广泛测试。不妨分享一下。

这是我的双启动机i7-3770, 16GB Ram, x86_64, Windows 8.1和Ubuntu 16.04。更多信息和结论，备注如下。测试了MSVS 2017和g++(在Windows和Linux上)。

测试程序

#include <iostream>
#include <chrono>
//#include <algorithm>
#include <array>
#include <locale>
#include <vector>
#include <queue>
#include <deque>

// Note: total size of array must not exceed 0x7fffffff B = 2,147,483,647B
//  which means that largest int array size is 536,870,911
// Also image size cannot be larger than 80,000,000B
constexpr int long g_size = 100000;
int g_A[g_size];


int main()
{
    std::locale loc("");
    std::cout.imbue(loc);
    constexpr int long size = 100000;  // largest array stack size

    // stack allocated c array
    std::chrono::steady_clock::time_point start = std::chrono::steady_clock::now();
    int A[size];
    for (int i = 0; i < size; i++)
        A[i] = i;

    auto duration = std::chrono::duration_cast<std::chrono::microseconds>(std::chrono::steady_clock::now() - start).count();
    std::cout << "c-style stack array duration=" << duration / 1000.0 << "ms\n";
    std::cout << "c-style stack array size=" << sizeof(A) << "B\n\n";

    // global stack c array
    start = std::chrono::steady_clock::now();
    for (int i = 0; i < g_size; i++)
        g_A[i] = i;

    duration = std::chrono::duration_cast<std::chrono::microseconds>(std::chrono::steady_clock::now() - start).count();
    std::cout << "global c-style stack array duration=" << duration / 1000.0 << "ms\n";
    std::cout << "global c-style stack array size=" << sizeof(g_A) << "B\n\n";

    // raw c array heap array
    start = std::chrono::steady_clock::now();
    int* AA = new int[size];    // bad_alloc() if it goes higher than 1,000,000,000
    for (int i = 0; i < size; i++)
        AA[i] = i;

    duration = std::chrono::duration_cast<std::chrono::microseconds>(std::chrono::steady_clock::now() - start).count();
    std::cout << "c-style heap array duration=" << duration / 1000.0 << "ms\n";
    std::cout << "c-style heap array size=" << sizeof(AA) << "B\n\n";
    delete[] AA;

    // std::array<>
    start = std::chrono::steady_clock::now();
    std::array<int, size> AAA;
    for (int i = 0; i < size; i++)
        AAA[i] = i;
    //std::sort(AAA.begin(), AAA.end());

    duration = std::chrono::duration_cast<std::chrono::microseconds>(std::chrono::steady_clock::now() - start).count();
    std::cout << "std::array duration=" << duration / 1000.0 << "ms\n";
    std::cout << "std::array size=" << sizeof(AAA) << "B\n\n";

    // std::vector<>
    start = std::chrono::steady_clock::now();
    std::vector<int> v;
    for (int i = 0; i < size; i++)
        v.push_back(i);
    //std::sort(v.begin(), v.end());

    duration = std::chrono::duration_cast<std::chrono::microseconds>(std::chrono::steady_clock::now() - start).count();
    std::cout << "std::vector duration=" << duration / 1000.0 << "ms\n";
    std::cout << "std::vector size=" << v.size() * sizeof(v.back()) << "B\n\n";

    // std::deque<>
    start = std::chrono::steady_clock::now();
    std::deque<int> dq;
    for (int i = 0; i < size; i++)
        dq.push_back(i);
    //std::sort(dq.begin(), dq.end());

    duration = std::chrono::duration_cast<std::chrono::microseconds>(std::chrono::steady_clock::now() - start).count();
    std::cout << "std::deque duration=" << duration / 1000.0 << "ms\n";
    std::cout << "std::deque size=" << dq.size() * sizeof(dq.back()) << "B\n\n";

    // std::queue<>
    start = std::chrono::steady_clock::now();
    std::queue<int> q;
    for (int i = 0; i < size; i++)
        q.push(i);

    duration = std::chrono::duration_cast<std::chrono::microseconds>(std::chrono::steady_clock::now() - start).count();
    std::cout << "std::queue duration=" << duration / 1000.0 << "ms\n";
    std::cout << "std::queue size=" << q.size() * sizeof(q.front()) << "B\n\n";
}

结果

//////////////////////////////////////////////////////////////////////////////////////////
// with MSVS 2017:
// >> cl /std:c++14 /Wall -O2 array_bench.cpp
//
// c-style stack array duration=0.15ms
// c-style stack array size=400,000B
//
// global c-style stack array duration=0.130ms
// global c-style stack array size=400,000B
//
// c-style heap array duration=0.90ms
// c-style heap array size=4B
//
// std::array duration=0.20ms
// std::array size=400,000B
//
// std::vector duration=0.544ms
// std::vector size=400,000B
//
// std::deque duration=1.375ms
// std::deque size=400,000B
//
// std::queue duration=1.491ms
// std::queue size=400,000B
//
//////////////////////////////////////////////////////////////////////////////////////////
//
// with g++ version:
//      - (tdm64-1) 5.1.0 on Windows
//      - (Ubuntu 5.4.0-6ubuntu1~16.04.10) 5.4.0 20160609 on Ubuntu 16.04
// >> g++ -std=c++14 -Wall -march=native -O2 array_bench.cpp -o array_bench
//
// c-style stack array duration=0ms
// c-style stack array size=400,000B
//
// global c-style stack array duration=0.124ms
// global c-style stack array size=400,000B
//
// c-style heap array duration=0.648ms
// c-style heap array size=8B
//
// std::array duration=1ms
// std::array size=400,000B
//
// std::vector duration=0.402ms
// std::vector size=400,000B
//
// std::deque duration=0.234ms
// std::deque size=400,000B
//
// std::queue duration=0.304ms
// std::queue size=400,000
//
//////////////////////////////////////////////////////////////////////////////////////////

笔记

平均10次组装。 我最初也使用std::sort()执行测试(您可以看到它被注释掉了)，但后来删除了它们，因为没有显著的相对差异。

我的结论和评论

notice how global c-style array takes almost as much time as the heap c-style array Out of all tests I noticed a remarkable stability in std::array's time variations between consecutive runs, while others especially std:: data structs varied wildly in comparison O3 optimization didn't show any noteworthy time differences Removing optimization on Windows cl (no -O2) and on g++ (Win/Linux no -O2, no -march=native) increases times SIGNIFICANTLY. Particularly for std::data structs. Overall higher times on MSVS than g++, but std::array and c-style arrays faster on Windows without optimization g++ produces faster code than microsoft's compiler (apparently it runs faster even on Windows).

判决

当然，这是用于优化构建的代码。既然问题是关于std::vector，那么是的，它是!比普通数组(优化/未优化)慢。但是当您进行基准测试时，您自然希望生成优化的代码。

对我来说，这个节目的明星是std::array。

好问题。我来这里是希望能找到一些简单的方法来加快矢量测试的速度。结果跟我想象的不太一样!

优化有帮助，但这还不够。通过优化，我仍然看到UseArray和UseVector之间的2X性能差异。有趣的是，UseVector明显比没有优化的UseVectorPushBack慢。

# g++ -Wall -Wextra -pedantic -o vector vector.cpp
# ./vector
UseArray completed in 20.68 seconds
UseVector completed in 120.509 seconds
UseVectorPushBack completed in 37.654 seconds
The whole thing completed in 178.845 seconds
# g++ -Wall -Wextra -pedantic -O3 -o vector vector.cpp
# ./vector
UseArray completed in 3.09 seconds
UseVector completed in 6.09 seconds
UseVectorPushBack completed in 9.847 seconds
The whole thing completed in 19.028 seconds

想法1 -使用new[]代替malloc

我尝试在UseArray中将malloc()更改为new[]，以便构造对象。从单个字段分配到分配一个Pixel实例。哦，重命名内循环变量为j。

void UseArray()
{
    TestTimer t("UseArray");

    for(int i = 0; i < 1000; ++i)
    {   
        int dimension = 999;

        // Same speed as malloc().
        Pixel * pixels = new Pixel[dimension * dimension];

        for(int j = 0 ; j < dimension * dimension; ++j)
            pixels[j] = Pixel(255, 0, 0);

        delete[] pixels;
    }
}

令人惊讶的是(对我来说)，这些变化没有任何不同。甚至没有更改为new[]，这将默认构造所有的像素。看起来gcc在使用new[]时可以优化默认构造函数调用，但在使用vector时就不行。

想法#2 -删除重复的操作符[]调用

我还尝试摆脱三重运算符[]查找，并缓存对像素[j]的引用。这实际上降低了UseVector的速度!哦。

for(int j = 0; j < dimension * dimension; ++j)
{
    // Slower than accessing pixels[j] three times.
    Pixel &pixel = pixels[j];
    pixel.r = 255;
    pixel.g = 0;
    pixel.b = 0;
}

# ./vector 
UseArray completed in 3.226 seconds
UseVector completed in 7.54 seconds
UseVectorPushBack completed in 9.859 seconds
The whole thing completed in 20.626 seconds

想法#3 -删除构造函数

如果完全删除构造函数呢?然后，也许gcc可以在创建向量时优化所有对象的结构。如果我们把像素改为:

struct Pixel
{
    unsigned char r, g, b;
};

结果:大约快10%。还是比数组慢。嗯。

# ./vector 
UseArray completed in 3.239 seconds
UseVector completed in 5.567 seconds

想法4 -使用迭代器而不是循环索引

如何使用vector<Pixel>::iterator代替循环索引?

for (std::vector<Pixel>::iterator j = pixels.begin(); j != pixels.end(); ++j)
{
    j->r = 255;
    j->g = 0;
    j->b = 0;
}

结果:

# ./vector 
UseArray completed in 3.264 seconds
UseVector completed in 5.443 seconds

没有什么不同。至少没有变慢。我认为这将具有类似于#2的性能，其中我使用了Pixel&引用。

结论

即使一些聪明的cookie找到了如何使vector循环和数组循环一样快的方法，这也不能说明std::vector的默认行为。编译器足够聪明，可以优化所有c++特性，并使STL容器像原始数组一样快。

底线是，当使用std::vector时，编译器无法优化掉无操作的默认构造函数调用。如果你使用普通的new[]，它就能很好地优化它们。但不是std::vector。即使你可以重写你的代码，以消除构造函数调用，在这里的咒语:“编译器比你聪明。STL和普通c一样快，不用担心。”

根据我的经验，有时候，只是有时候，vector<int>可能比int[]慢很多倍。需要记住的一点是，向量的向量与int[][]非常不同。因为元素在内存中可能不是连续的。这意味着你可以在主向量中调整不同向量的大小，但CPU可能无法像int[][]那样缓存元素。