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.

以下是我个人经历中的几个例子:

Access to instructions that are not accessible from C. For instance, many architectures (like x86-64, IA-64, DEC Alpha, and 64-bit MIPS or PowerPC) support a 64 bit by 64 bit multiplication producing a 128 bit result. GCC recently added an extension providing access to such instructions, but before that assembly was required. And access to this instruction can make a huge difference on 64-bit CPUs when implementing something like RSA - sometimes as much as a factor of 4 improvement in performance. Access to CPU-specific flags. The one that has bitten me a lot is the carry flag; when doing a multiple-precision addition, if you don't have access to the CPU carry bit one must instead compare the result to see if it overflowed, which takes 3-5 more instructions per limb; and worse, which are quite serial in terms of data accesses, which kills performance on modern superscalar processors. When processing thousands of such integers in a row, being able to use addc is a huge win (there are superscalar issues with contention on the carry bit as well, but modern CPUs deal pretty well with it). SIMD. Even autovectorizing compilers can only do relatively simple cases, so if you want good SIMD performance it's unfortunately often necessary to write the code directly. Of course you can use intrinsics instead of assembly but once you're at the intrinsics level you're basically writing assembly anyway, just using the compiler as a register allocator and (nominally) instruction scheduler. (I tend to use intrinsics for SIMD simply because the compiler can generate the function prologues and whatnot for me so I can use the same code on Linux, OS X, and Windows without having to deal with ABI issues like function calling conventions, but other than that the SSE intrinsics really aren't very nice - the Altivec ones seem better though I don't have much experience with them). As examples of things a (current day) vectorizing compiler can't figure out, read about bitslicing AES or SIMD error correction - one could imagine a compiler that could analyze algorithms and generate such code, but it feels to me like such a smart compiler is at least 30 years away from existing (at best).

On the other hand, multicore machines and distributed systems have shifted many of the biggest performance wins in the other direction - get an extra 20% speedup writing your inner loops in assembly, or 300% by running them across multiple cores, or 10000% by running them across a cluster of machines. And of course high level optimizations (things like futures, memoization, etc) are often much easier to do in a higher level language like ML or Scala than C or asm, and often can provide a much bigger performance win. So, as always, there are tradeoffs to be made.

在Amiga上，CPU和图形/音频芯片会为了访问特定区域的RAM(具体来说是前2MB的RAM)而争斗。因此，当你只有2MB RAM(或更少)时，显示复杂的图形加上播放声音会杀死CPU的性能。

在汇编程序中，你可以巧妙地交错你的代码，使CPU只在图形/音频芯片内部繁忙时(即当总线空闲时)才尝试访问RAM。因此，通过重新排序指令，巧妙地使用CPU缓存，总线定时，你可以实现一些使用任何高级语言都不可能实现的效果，因为你必须为每个命令定时，甚至在这里或那里插入nop，以使不同的芯片不受彼此的雷达影响。

这也是为什么CPU的NOP (No Operation -什么都不做)指令实际上可以让你的整个应用程序运行得更快的另一个原因。

当然，这种技术取决于特定的硬件设置。这就是为什么许多Amiga游戏无法适应更快的cpu的主要原因:指令的计时错误。

第一点不是答案。 即使你从来没有用它编程，我发现至少知道一个汇编指令集是有用的。这是程序员永无止境的追求的一部分，他们想知道得更多，从而变得更好。当你进入一个没有源代码的框架时，它也很有用，至少对正在发生的事情有一个粗略的了解。它还可以帮助您理解JavaByteCode和. net IL，因为它们都类似于汇编程序。

To answer the question when you have a small amount of code or a large amount of time. Most useful for use in embedded chips, where low chip complexity and poor competition in compilers targeting these chips can tip the balance in favour of humans. Also for restricted devices you are often trading off code size/memory size/performance in a way that would be hard to instruct a compiler to do. e.g. I know this user action is not called often so I will have small code size and poor performance, but this other function that look similar is used every second so I will have a larger code size and faster performance. That is the sort of trade off a skilled assembly programmer can use.

我还想补充一点，这里有很多中间地带，您可以用C编译代码并检查生成的程序集，然后更改C代码或调整并作为程序集进行维护。

我的朋友从事微控制器的工作，目前是用于控制小型电动机的芯片。他在低级c和汇编的组合中工作。他曾经告诉我，有一天他在工作中把主循环从48条指令减少到43条。他还面临着各种选择，比如代码已经增长到填满256k芯片，业务需要一个新功能，你呢

删除现有功能 减少部分或全部现有特性的大小，可能会以性能为代价。 提倡改用成本更高、功耗更高、外形更大的更大芯片。

我想补充一点，作为一个商业开发人员，我有很多的投资组合或语言、平台、应用程序类型，我从来没有觉得有必要深入编写程序集。我一直都很感激我所学到的知识。有时会被调试进去。

我知道我已经回答了“为什么我要学习汇编器”这个问题，但我觉得这是一个更重要的问题，而不是什么时候更快。

所以让我们再试一次 你应该考虑组装

致力于底层操作系统功能 在编译器上工作。 工作在一个极其有限的芯片，嵌入式系统等

记住比较你的程序集和生成的编译器，看看哪个更快/更小/更好。

大卫。

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。

The question is a bit misleading. The answer is there in your post itself. It is always possible to write assembly solution for a particular problem which executes faster than any generated by a compiler. The thing is you need to be an expert in assembly to overcome the limitations of a compiler. An experienced assembly programmer can write programs in any HLL which performs faster than one written by an inexperienced. The truth is you can always write assembly programs executing faster than one generated by a compiler.