几乎任何时候编译器看到浮点代码，如果你使用的是旧的糟糕的编译器，手写的版本会更快。(2019年更新:对于现代编译器来说，这并不普遍。特别是在编译x87以外的东西时;编译器更容易使用SSE2或AVX进行标量数学运算，或任何具有平面FP寄存器集的非x86，不像x87的寄存器堆栈。)

主要原因是编译器不能执行任何健壮的优化。关于这个主题的讨论，请参阅来自MSDN的这篇文章。下面是一个例子，其中汇编版本的速度是C版本的两倍(用VS2K5编译):

#include "stdafx.h"
#include <windows.h>

float KahanSum(const float *data, int n)
{
   float sum = 0.0f, C = 0.0f, Y, T;

   for (int i = 0 ; i < n ; ++i) {
      Y = *data++ - C;
      T = sum + Y;
      C = T - sum - Y;
      sum = T;
   }

   return sum;
}

float AsmSum(const float *data, int n)
{
  float result = 0.0f;

  _asm
  {
    mov esi,data
    mov ecx,n
    fldz
    fldz
l1:
    fsubr [esi]
    add esi,4
    fld st(0)
    fadd st(0),st(2)
    fld st(0)
    fsub st(0),st(3)
    fsub st(0),st(2)
    fstp st(2)
    fstp st(2)
    loop l1
    fstp result
    fstp result
  }

  return result;
}

int main (int, char **)
{
  int count = 1000000;

  float *source = new float [count];

  for (int i = 0 ; i < count ; ++i) {
    source [i] = static_cast <float> (rand ()) / static_cast <float> (RAND_MAX);
  }

  LARGE_INTEGER start, mid, end;

  float sum1 = 0.0f, sum2 = 0.0f;

  QueryPerformanceCounter (&start);

  sum1 = KahanSum (source, count);

  QueryPerformanceCounter (&mid);

  sum2 = AsmSum (source, count);

  QueryPerformanceCounter (&end);

  cout << "  C code: " << sum1 << " in " << (mid.QuadPart - start.QuadPart) << endl;
  cout << "asm code: " << sum2 << " in " << (end.QuadPart - mid.QuadPart) << endl;

  return 0;
}

和一些数字从我的PC运行默认版本*:

  C code: 500137 in 103884668
asm code: 500137 in 52129147

出于兴趣，我用dec/jnz交换了循环，它对计时没有影响——有时更快，有时更慢。我想内存有限的方面使其他优化相形见绌。(编者注:更可能的情况是，FP延迟瓶颈足以隐藏循环的额外成本。对奇数/偶数元素并行进行两个Kahan求和，并在最后添加它们，可能会加快2倍的速度。)

哎呀，我正在运行一个稍微不同的代码版本，它输出的数字是错误的(即C更快!)修正并更新了结果。

我很惊讶居然没人这么说。如果用汇编编写strlen()函数，速度会快得多!在C中，你能做的最好的事情就是

int c;
for(c = 0; str[c] != '\0'; c++) {}

在组装过程中，你可以大大加快速度:

mov esi, offset string
mov edi, esi
xor ecx, ecx

lp:
mov ax, byte ptr [esi]
cmp al, cl
je  end_1
cmp ah, cl
je end_2
mov bx, byte ptr [esi + 2]
cmp bl, cl
je end_3
cmp bh, cl
je end_4
add esi, 4
jmp lp

end_4:
inc esi

end_3:
inc esi

end_2:
inc esi

end_1:
inc esi

mov ecx, esi
sub ecx, edi

长度单位是ecx。这一次比较4个字符，所以速度快4倍。并且考虑使用eax和ebx的高阶词，它将比之前的C例程快8倍!

在我的工作中，有三个原因让我了解和使用组装。按重要性排序:

Debugging - I often get library code that has bugs or incomplete documentation. I figure out what it's doing by stepping in at the assembly level. I have to do this about once a week. I also use it as a tool to debug problems in which my eyes don't spot the idiomatic error in C/C++/C#. Looking at the assembly gets past that. Optimizing - the compiler does fairly well in optimizing, but I play in a different ballpark than most. I write image processing code that usually starts with code that looks like this: for (int y=0; y < imageHeight; y++) { for (int x=0; x < imageWidth; x++) { // do something } } the "do something part" typically happens on the order of several million times (ie, between 3 and 30). By scraping cycles in that "do something" phase, the performance gains are hugely magnified. I don't usually start there - I usually start by writing the code to work first, then do my best to refactor the C to be naturally better (better algorithm, less load in the loop etc). I usually need to read assembly to see what's going on and rarely need to write it. I do this maybe every two or three months. doing something the language won't let me. These include - getting the processor architecture and specific processor features, accessing flags not in the CPU (man, I really wish C gave you access to the carry flag), etc. I do this maybe once a year or two years.

不需要给出任何具体的示例或分析器证据，当您比编译器知道的更多时，您可以编写比编译器更好的汇编程序。

In the general case, a modern C compiler knows much more about how to optimize the code in question: it knows how the processor pipeline works, it can try to reorder instructions quicker than a human can, and so on - it's basically the same as a computer being as good as or better than the best human player for boardgames, etc. simply because it can make searches within the problem space faster than most humans. Although you theoretically can perform as well as the computer in a specific case, you certainly can't do it at the same speed, making it infeasible for more than a few cases (i.e. the compiler will most certainly outperform you if you try to write more than a few routines in assembler).

另一方面，有些情况下编译器没有那么多的信息——我想说主要是在使用不同形式的外部硬件时，编译器不知道这些信息。主要的例子可能是设备驱动程序，其中汇编程序结合人类对相关硬件的熟悉知识可以产生比C编译器更好的结果。

其他人提到了特殊用途指令，这就是我在上面一段中所说的——编译器可能对这些指令了解有限或根本不了解，这使得人类可以编写更快的代码。

，问了这个问题，并给出了使用汇编的利弊。

答案很简单……一个非常了解汇编的人(也就是他身边有参考资料，并利用每一个小处理器缓存和管道特性等)保证能够产生比任何编译器更快的代码。

然而，如今在典型的应用程序中，这种差异并不重要。