我曾经和一个人一起工作过，他说“如果编译器笨到不能弄清楚你要做什么，并且不能优化它，那么你的编译器就坏了，是时候换一个新的了”。我确信在某些情况下汇编程序会打败你的C代码，但是如果你发现自己经常使用汇编程序来“赢得”编译器，那么你的编译器就完蛋了。

对于编写试图强制查询计划器执行操作的“优化”SQL也是如此。如果您发现自己重新安排查询以让计划器执行您想要的操作，那么您的查询计划器就完蛋了——请更换一个新的计划器。

只要有合适的程序员，汇编程序总是可以比C程序快(至少稍微快一点)。如果不能从汇编器中取出至少一条指令，则很难创建一个C程序。

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。

只有在使用编译器不支持的特殊用途指令集时。

为了最大限度地利用具有多个管道和预测分支的现代CPU的计算能力，您需要以这样一种方式来构造汇编程序:a)人类几乎不可能编写b)甚至更不可能维护。

此外，更好的算法、数据结构和内存管理将为您提供至少一个数量级的性能，而不是在汇编中进行的微观优化。

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.

长波克，只有一个限制时间。当你没有足够的资源来优化每一个代码的变化，并花时间分配寄存器，优化一些溢出和诸如此类的事情时，编译器每次都会赢。对代码进行修改、重新编译和度量。如有必要重复。

此外，你可以在高水平方面做很多事情。此外，检查生成的程序集可能会给人一种代码是垃圾的印象，但实际上它的运行速度比您想象的要快。例子:

Int y = data[i]; //在这里做一些事情。 call_function (y,…);

编译器将读取数据，将其推入堆栈(溢出)，然后从堆栈读取并作为参数传递。听起来屎?它实际上可能是非常有效的延迟补偿，并导致更快的运行时。

//优化版本 call_function(数据[我],…);//毕竟不是那么优化。

优化版本的想法是，我们降低了寄存器压力，避免溢出。但事实上，“垃圾”版本更快!

看看汇编代码，只看指令，然后得出结论:指令越多，速度越慢，这将是一个错误的判断。

这里需要注意的是:许多组装专家认为他们知道很多，但知道的很少。规则也会随着架构的变化而变化。例如，x86代码并不存在总是最快的银弹。如今，最好还是按照经验法则行事:

记忆很慢 缓存速度快 尽量更好地使用缓存 你多久会错过一次?你有延迟补偿策略吗? 对于一个cache miss，你可以执行10-100个ALU/FPU/SSE指令 应用程序架构很重要。 . .但是当问题不在架构上时，它就没有帮助了

此外，过于相信编译器会神奇地将考虑不周到的C/ c++代码转换为“理论上最优”的代码是一厢情愿的想法。如果你关心这个低级别的“性能”，你必须知道你使用的编译器和工具链。

C/ c++中的编译器通常不太擅长重新排序子表达式，因为对于初学者来说，函数有副作用。函数式语言没有受到这个警告的影响，但它不太适合当前的生态系统。有一些编译器选项可以允许宽松的精确规则，允许编译器/链接器/代码生成器改变操作的顺序。

这个话题有点死路一条;对于大多数人来说，这是无关紧要的，而剩下的人，他们已经知道自己在做什么了。

这一切都归结为:“理解你在做什么”，这与知道你在做什么有点不同。