我已经阅读了所有的答案(超过30个)，并没有找到一个简单的原因:如果你读过并练习过Intel®64和IA-32架构优化参考手册，汇编程序比C更快，所以汇编程序可能更慢的原因是编写这种慢汇编程序的人没有阅读优化手册。

In the good old days of Intel 80286, each instruction was executed at a fixed count of CPU cycles. Still, since Pentium Pro, released in 1995, Intel processors became superscalar, utilizing Complex Pipelining: Out-of-Order Execution & Register Renaming. Before that, on Pentium, produced in 1993, there were U and V pipelines. Therefore, Pentium introduced dual pipelines that could execute two simple instructions at one clock cycle if they didn't depend on one another. However, this was nothing compared with the Out-of-Order Execution & Register Renaming that appeared in Pentium Pro. This approach introduced in Pentium Pro is practically the same nowadays on most recent Intel processors.

Let me explain the Out-of-Order Execution in a few words. The fastest code is where instructions do not depend on previous results, e.g., you should always clear whole registers (by movzx) to remove dependency from previous values of the registers you are working with, so they may be renamed internally by the CPU to allow instruction execute in parallel or in a different order. Or, on some processors, false dependency may exist that may also slow things down, like false dependency on Pentium 4 for inc/dec, so you may wish to use add eax, 1 instead or inc eax to remove dependency on the previous state of the flags.

如果时间允许，您可以阅读更多无序执行和注册重命名。因特网上有大量的信息。

There are also many other essential issues like branch prediction, number of load and store units, number of gates that execute micro-ops, memory cache coherence protocols, etc., but the crucial thing to consider is the Out-of-Order Execution. Most people are simply not aware of the Out-of-Order Execution. Therefore, they write their assembly programs like for 80286, expecting their instructions will take a fixed time to execute regardless of the context. At the same time, C compilers are aware of the Out-of-Order Execution and generate the code correctly. That's why the code of such uninformed people is slower, but if you become knowledgeable, your code will be faster.

除了乱序执行之外，还有很多优化技巧和技巧。请阅读上面提到的优化手册:-)

However, assembly language has its own drawbacks when it comes to optimization. According to Peter Cordes (see the comment below), some of the optimizations compilers do would be unmaintainable for large code-bases in hand-written assembly. For example, suppose you write in assembly. In that case, you need to completely change an inline function (an assembly macro) when it inlines into a function that calls it with some arguments being constants. At the same time, a C compiler makes its job a lot simpler—and inlining the same code in different ways into different call sites. There is a limit to what you can do with assembly macros. So to get the same benefit, you'd have to manually optimize the same logic in each place to match the constants and available registers you have.

我不能给出具体的例子，因为那是很多年前的事情了，但是在很多情况下，手工编写的汇编程序可以胜过任何编译器。原因:

您可以偏离调用约定，在寄存器中传递参数。 您可以仔细考虑如何使用寄存器，避免将变量存储在内存中。 对于跳转表之类的东西，可以避免检查索引的边界。

基本上，编译器在优化方面做得很好，这几乎总是“足够好”，但在某些情况下(如图形渲染)，你要为每一个周期付出高昂的代价，你可以走捷径，因为你知道代码，而编译器不能，因为它必须在安全的方面。

事实上，我听说过一些图形渲染代码，其中一个例程，如直线绘制或多边形填充例程，实际上在堆栈上生成了一小块机器代码并在那里执行，以避免关于线条样式、宽度、模式等的连续决策。

也就是说，我想让编译器为我生成好的汇编代码，但又不太聪明，它们通常都是这样做的。事实上，我讨厌Fortran的一个原因是它为了“优化”而打乱代码，通常没有什么重要的目的。

通常，当应用程序出现性能问题时，都是由于浪费的设计造成的。这些天，我永远不会推荐汇编程序的性能，除非整个应用程序已经在它的生命周期内进行了调优，仍然不够快，并且把所有的时间都花在了紧凑的内部循环中。

补充:我见过很多用汇编语言编写的应用程序，与C、Pascal、Fortran等语言相比，汇编语言的主要速度优势是因为程序员在用汇编语言编码时要谨慎得多。他或她每天要写大约100行代码，不管哪种语言，在编译器语言中，这将等于3或400条指令。

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!

CP/M-86版本的PolyPascal (Turbo Pascal的兄弟)的一个可能性是用机器语言例程取代“使用生物将字符输出到屏幕上”的功能，本质上是给定x、y和字符串放在那里。

这使得更新屏幕的速度比以前快得多!

二进制文件中有足够的空间来嵌入机器代码(几百个字节)，也有其他的东西，所以尽可能多地压缩是必要的。

事实证明，由于屏幕是80x25，这两个坐标都可以容纳每个字节，所以都可以容纳两个字节的单词。这允许在更少的字节内完成所需的计算，因为单个添加可以同时操作两个值。

据我所知，没有C编译器可以在一个寄存器中合并多个值，对它们执行SIMD指令，然后再将它们分开(而且我不认为机器指令会更短)。

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.

这个问题有点毫无意义，因为无论如何c都是编译到汇编程序的。 但是，通过优化编译器生成的汇编程序几乎是完全优化的，所以除非你在优化特定的汇编程序方面做了20个博士学位，否则你无法打败编译器。