令人惊讶的是，“C/ c++必须比Java快，因为Java是解释性的”的神话仍然活跃。有几年前的文章，也有最近的文章，用概念或测量来解释为什么这种情况并不总是如此。

当前的虚拟机实现(顺便说一下，不仅仅是JVM)可以利用程序执行期间收集的信息来动态地优化代码，使用各种技术:

将频繁的方法呈现给机器代码， 内联小方法， 锁紧调整

以及各种其他调整，这些调整是基于了解代码实际在做什么，以及它运行的环境的实际特征。

The lack of abstraction is what makes C faster. If you write an output statement you know exactly what is happening. If you write an output statement in java it is getting compiled to a class file which then gets run on a virtual machine introducing a layor of abstraction. The lack of object oriented features as a part of the language also increases it's speed do to less code being generated. If you use C as an object oriented language then you are doing all the coding for things such as classes, inharitence, etc. This means rather then make something generalized enough for everyone with the amount of code and the performance penelty that requires you only write what you need to get the job done.

c++的平均速度更快(就像它最初一样，主要是C的超集，尽管有一些不同)。然而，对于特定的基准测试，通常有另一种更快的语言。

https://benchmarksgame-team.pages.debian.net/benchmarksgame/

fannjuch-redux是Scala中最快的

n-body和fasta在Ada中更快。

频谱范数在Fortran中是最快的。

反补、mandelbrot和pidigits在ATS中最快。

regex-dna是JavaScript中最快的。

chameneau -redux最快的是Java 7。

Haskell的螺纹环速度最快。

其余的基准测试在C或c++中是最快的。

实际上，在某些应用程序(数字)中，甚至C也可以被击败，我指的不是汇编语言，而是老的、经常被嘲笑的Fortran。原因是，Fortran保证没有指针别名。

如果你花了一个月的时间用C语言构建的程序只需要0.05秒，而我花了一天的时间用Java写同样的程序，只需要0.10秒，那么C语言真的更快吗?

但是回答你的问题，编写良好的C代码通常会比其他语言编写的代码运行得更快，因为编写良好的C代码的一部分包括在接近机器的级别上进行手动优化。

尽管编译器确实非常聪明，但它们还不能创造性地提出与手工按摩算法竞争的代码(假设“手”属于一个优秀的C程序员)。

编辑:

很多评论都是这样的:“我用C语言编写，我不考虑优化。”

举个具体的例子:

在Delphi中我可以这样写:

function RemoveAllAFromB(a, b: string): string;
var
  before, after :string;
begin
  Result := b;
  if 0 < Pos(a,b) then begin
    before := Copy(b,1,Pos(a,b)-Length(a));
    after := Copy(b,Pos(a,b)+Length(a),Length(b));
    Result := before + after;
    Result := RemoveAllAFromB(a,Result);  //recursive
  end;
end;

用C语言写:

char *s1, *s2, *result; /* original strings and the result string */
int len1, len2; /* lengths of the strings */
for (i = 0; i < len1; i++) {
   for (j = 0; j < len2; j++) {
     if (s1[i] == s2[j]) {
       break;
     }
   }
   if (j == len2) {  /* s1[i] is not found in s2 */
     *result = s1[i]; 
     result++; /* assuming your result array is long enough */
   }
}

但是C版本中有多少优化呢?我们在实现方面做了很多我在Delphi版本中没有考虑到的决定。字符串是如何实现的?在特尔斐我看不出来。在C语言中，我已经决定它将是一个指向ASCII整数数组的指针，我们称之为字符。在C语言中，我们每次测试一个字符的存在性。在Delphi中，我使用Pos。

这只是一个小例子。在一个大型程序中，C程序员必须对每几行代码做出这类低级决策。它加起来就是一个手工制作、手工优化的可执行文件。

在过去，只有两种类型的语言:编译型和解释型。

编译语言利用“编译器”读取语言语法并将其转换为相同的汇编语言代码，这可以直接在CPU上进行。解释型语言使用了几种不同的方案，但从本质上讲，语言语法被转换成一种中间形式，然后在“解释器”(用于执行代码的环境)中运行。

因此，在某种意义上，在代码和机器之间存在另一个“层”——解释器。而且，在计算机中，越多就意味着使用更多的资源。翻译速度较慢，因为他们必须执行更多的操作。

More recently, we've seen more hybrid languages like Java, that employ both a compiler and an interpreter to make them work. It's complicated, but a JVM is faster, more sophisticated and way more optimized than the old interpreters, so it stands a much better change of performing (over time) closer to just straight compiled code. Of course, the newer compilers also have more fancy optimizing tricks so they tend to generate way better code than they used to as well. But most optimizations, most often (although not always) make some type of trade-off such that they are not always faster in all circumstances. Like everything else, nothing comes for free, so the optimizers must get their boast from somewhere (although often times it using compile-time CPU to save runtime CPU).

Getting back to C, it is a simple language, that can be compiled into fairly optimized assembly and then run directly on the target machine. In C, if you increment an integer, it's more than likely that it is only one assembler step in the CPU, in Java however, it could end up being a lot more than that (and could include a bit of garbage collection as well :-) C offers you an abstraction that is way closer to the machine (assembler is the closest), but you end up having to do way more work to get it going and it is not as protected, easy to use or error friendly. Most other languages give you a higher abstraction and take care of more of the underlying details for you, but in exchange for their advanced functionality they require more resources to run. As you generalize some solutions, you have to handle a broader range of computing, which often requires more resources.

保罗。