了解汇编程序的原因之一是，有时可以使用汇编程序来编写比用高级语言(特别是C语言)编写的代码性能更好的代码。然而，我也听人说过很多次，尽管这并非完全错误，但实际上可以使用汇编程序来生成性能更好的代码的情况极其罕见，并且需要汇编方面的专业知识和经验。
这个问题甚至没有涉及到这样一个事实，即汇编程序指令将是特定于机器的、不可移植的，或者汇编程序的任何其他方面。当然，除了这一点之外，了解汇编还有很多很好的理由，但这是一个需要示例和数据的具体问题，而不是关于汇编程序与高级语言的扩展论述。
谁能提供一些具体的例子，说明使用现代编译器汇编代码比编写良好的C代码更快，并且您能否用分析证据支持这一说法?我相信这些案例确实存在，但我真的很想知道这些案例到底有多深奥，因为这似乎是一个有争议的问题。
当前回答
我认为汇编程序更快的一般情况是，当一个聪明的汇编程序员看到编译器的输出并说“这是性能的关键路径，我可以写这个更有效”，然后那个人调整汇编程序或从头重写它。
其他回答
紧密循环，就像处理图像时一样，因为一张图像可能需要数百万像素。坐下来研究一下如何最好地利用有限的处理器寄存器会有很大的不同。下面是一个真实的例子:
http://danbystrom.se/2008/12/22/optimizing-away-ii/
处理器通常有一些深奥的指令，这些指令对于编译器来说太专业了，但有时汇编程序员可以很好地利用它们。以XLAT指令为例。如果您需要在循环中进行表查找，并且表限制在256字节，那么这非常棒!
更新:哦，当我们谈论一般循环时，最关键的是:编译器通常不知道常见情况下会有多少次迭代!只有程序员知道一个循环会被迭代很多次，因此用一些额外的工作来准备循环是有益的，或者如果它迭代的次数太少，以至于设置实际花费的时间比预期的迭代要长。
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.
如今，考虑到像英特尔c++这样的编译器对C代码进行了极大的优化，它很难与编译器的输出竞争。
http://cr.yp.to/qhasm.html有很多例子。
我认为汇编程序更快的一般情况是，当一个聪明的汇编程序员看到编译器的输出并说“这是性能的关键路径，我可以写这个更有效”，然后那个人调整汇编程序或从头重写它。
