首先，我不同意这个问题的部分公认答案(并且得到了好评)，我说:

为什么jit代码比适当优化的c++(或其他没有运行时开销的语言)运行得慢，实际上有很多原因。 程序包括:

根据定义，在运行时用于jit代码的计算周期在程序执行中不可用。 JITter中的任何热路径都将与你的代码竞争指令和CPU中的数据缓存。我们知道缓存在性能方面占主导地位，而像c++这样的原生语言在设计上并没有这种类型的争用。 运行时优化器的时间预算必然比编译时优化器的时间预算更有限(正如另一个评论者指出的那样)。

底线:最终，您几乎肯定能够在c++中创建比在c#中更快的实现。

现在，说了这么多，速度到底有多快是无法量化的，因为有太多的变量:任务、问题领域、硬件、实现质量和许多其他因素。您将在您的场景上运行测试，以确定性能上的差异，然后决定是否值得额外的努力和复杂性。

这是一个很长很复杂的话题，但为了完整起见，我觉得值得一提的是，c#的运行时优化器非常出色，能够在运行时执行某些c++编译时(静态)优化器无法实现的动态优化。即便如此，优势仍然主要体现在本机应用程序方面，但动态优化器是上面给出的“几乎肯定”限定符的原因。

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在相对性能方面，我也被我在其他一些答案中看到的数字和讨论所困扰，所以我想我应该插话，同时为我上面所做的陈述提供一些支持。

这些基准测试的很大一部分问题是，你不能像写c#一样写c++代码，并期望得到具有代表性的结果(例如。在c++中执行成千上万的内存分配将会给你可怕的数字。)

相反，我编写了稍微更习惯的c++代码，并与@Wiory提供的c#代码进行了比较。我对c++代码所做的两个主要更改是:

使用向量::储备() 将2d数组平摊到1d以获得更好的缓存位置(连续块)

c#(。净4.6.1)

private static void TestArray()
{
    const int rows = 5000;
    const int columns = 9000;
    DateTime t1 = System.DateTime.Now;
    double[][] arr = new double[rows][];
    for (int i = 0; i < rows; i++)
        arr[i] = new double[columns];
    DateTime t2 = System.DateTime.Now;

    Console.WriteLine(t2 - t1);

    t1 = System.DateTime.Now;
    for (int i = 0; i < rows; i++)
        for (int j = 0; j < columns; j++)
            arr[i][j] = i;
    t2 = System.DateTime.Now;

    Console.WriteLine(t2 - t1);
}

运行时间(发布):初始:124ms，填充:165ms

C++14 （Clang v3.8/C2）

#include <iostream>
#include <vector>

auto TestSuite::ColMajorArray()
{
    constexpr size_t ROWS = 5000;
    constexpr size_t COLS = 9000;

    auto initStart = std::chrono::steady_clock::now();

    auto arr = std::vector<double>();
    arr.reserve(ROWS * COLS);

    auto initFinish = std::chrono::steady_clock::now();
    auto initTime = std::chrono::duration_cast<std::chrono::microseconds>(initFinish - initStart);

    auto fillStart = std::chrono::steady_clock::now();

    for(auto i = 0, r = 0; r < ROWS; ++r)
    {
        for (auto c = 0; c < COLS; ++c)
        {
            arr[i++] = static_cast<double>(r * c);
        }
    }

    auto fillFinish = std::chrono::steady_clock::now();
    auto fillTime = std::chrono::duration_cast<std::chrono::milliseconds>(fillFinish - fillStart);

    return std::make_pair(initTime, fillTime);
}

运行时间(发布):初始:398µs(是的，这是微秒)，填充:152ms

总运行时间:c#: 289毫秒，c++ 152毫秒(大约快90%)

观察

Changing the C# implementation to the same 1d array implementation yielded Init: 40ms, Fill: 171ms, Total: 211ms (C++ was still almost 40% faster). It is much harder to design and write "fast" code in C++ than it is to write "regular" code in either language. It's (perhaps) astonishingly easy to get poor performance in C++; we saw that with unreserved vectors performance. And there are lots of pitfalls like this. C#'s performance is rather amazing when you consider all that is going on at runtime. And that performance is comparatively easy to access. More anecdotal data comparing the performance of C++ and C#: https://benchmarksgame.alioth.debian.org/u64q/compare.php?lang=gpp&lang2=csharpcore

归根结底，c++为您提供了对性能的更多控制。你想用指针吗?一个参考吗?栈内存?堆吗?动态多态还是用静态多态(通过模板/CRTP)消除虚表的运行时开销?在c++中你必须…呃，自己做出所有这些选择(甚至更多)，理想情况下，这样你的解决方案才能最好地解决你正在处理的问题。

问问自己是否真的想要或需要该控件，因为即使对于上面的简单示例，您也可以看到尽管性能有了显著的改进，但它需要更深入的投资才能访问。

毕竟，答案总要在某个地方，不是吗?：）

嗯,没有。

正如一些回复所指出的那样，这个问题在某种程度上没有得到充分的说明，只会引起问题的回应，而不是答案。只从一个方面来说:

这个问题将语言和语言实现合并在一起——这个C程序比c#程序慢2194倍，快1.17倍——我们不得不问你:哪种语言实现?

然后是哪些项目?哪个机器?哪些操作系统?哪个数据集?

需要大量内存访问的应用程序。图像处理通常更适合在非托管环境(c++)而不是托管环境(c#)中编写。使用指针算法优化的内循环在c++中更容易控制。在c#中，你可能需要使用不安全的代码来获得相同的性能。

对于“令人尴尬的并行”问题，当在c++上使用Intel TBB和OpenMP时，我观察到与用c#和TPL处理的类似(纯数学)问题相比，性能大约提高了10倍。SIMD是c#无法竞争的一个领域，但我也有一个印象，TPL有相当大的开销。

也就是说，我只在性能关键的任务中使用c++，我知道我将能够多线程并快速得到结果。对于其他任何事情，c#(偶尔f#)都很好。

C/ c++在有大型数组或数组(任何大小)上的大量循环/迭代的程序中可以表现得更好。这就是为什么在C/ c++中图形化通常要快得多，因为几乎所有的图形化操作都基于繁重的数组操作。net在数组索引操作中是出了名的慢，这是由于所有的安全检查，这对于多维数组尤其如此(是的，矩形c#数组甚至比锯齿形c#数组还要慢)。

The bonuses of C/C++ are most pronounced if you stick directly with pointers and avoid Boost, std::vector and other high-level containers, as well as inline every small function possible. Use old-school arrays whenever possible. Yes, you will need more lines of code to accomplish the same thing you did in Java or C# as you avoid high-level containers. If you need a dynamically sized array, you will just need to remember to pair your new T[] with a corresponding delete[] statement (or use std::unique_ptr)—the price for the extra speed is that you must code more carefully. But in exchange, you get to rid yourself of the overhead of managed memory / garbage collector, which can easily be 20% or more of the execution time of heavily object-oriented programs in both Java and .NET, as well as those massive managed memory array indexing costs. C++ apps can also benefit from some nifty compiler switches in certain specific cases.

I am an expert programmer in C, C++, Java, and C#. I recently had the rare occasion to implement the exact same algorithmic program in the latter 3 languages. The program had a lot of math and multi-dimensional array operations. I heavily optimized this in all 3 languages. The results were typical of what I normally see in less rigorous comparisons: Java was about 1.3x faster than C# (most JVMs are more optimized than the CLR), and the C++ raw pointer version came in about 2.1x faster than C#. Note that the C# program only used safe code—it is my opinion that you might as well code it in C++ before using the unsafe keyword.

Lest anyone think I have something against C#, I will close by saying that C# is probably my favorite language. It is the most logical, intuitive and rapid development language I've encountered so far. I do all my prototyping in C#. The C# language has many small, subtle advantages over Java (yes, I know Microsoft had the chance to fix many of Java's shortcomings by entering the game late and arguably copying Java). Toast to Java's Calendar class anyone? If Microsoft ever spends real effort to optimize the CLR and the .NET JITter, C# could seriously take over. I'm honestly surprised they haven't already—they did so many things right in the C# language, why not follow it up with heavy-hitting compiler optimizations? Maybe if we all beg.

In theory, for long running server-type application, a JIT-compiled language can become much faster than a natively compiled counterpart. Since the JIT compiled language is generally first compiled to a fairly low-level intermediate language, you can do a lot of the high-level optimizations right at compile time anyway. The big advantage comes in that the JIT can continue to recompile sections of code on the fly as it gets more and more data on how the application is being used. It can arrange the most common code-paths to allow branch prediction to succeed as often as possible. It can re-arrange separate code blocks that are often called together to keep them both in the cache. It can spend more effort optimizing inner loops.

我怀疑。net或任何jre都能做到这一点，但早在我上大学的时候就有人在研究这一点，所以认为这类东西很快就会在现实世界中找到自己的方式也不是不合理的。