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
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。
其他回答
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.
，问了这个问题，并给出了使用汇编的利弊。
Walter Bright的《optimization Immutable and Purity》可能值得一看，它不是一个概要测试，但向您展示了手写和编译器生成ASM之间的区别。Walter Bright写优化编译器，所以值得一看他的其他博客文章。
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。
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.
