在过去，只有两种类型的语言:编译型和解释型。

编译语言利用“编译器”读取语言语法并将其转换为相同的汇编语言代码，这可以直接在CPU上进行。解释型语言使用了几种不同的方案，但从本质上讲，语言语法被转换成一种中间形式，然后在“解释器”(用于执行代码的环境)中运行。

因此，在某种意义上，在代码和机器之间存在另一个“层”——解释器。而且，在计算机中，越多就意味着使用更多的资源。翻译速度较慢，因为他们必须执行更多的操作。

More recently, we've seen more hybrid languages like Java, that employ both a compiler and an interpreter to make them work. It's complicated, but a JVM is faster, more sophisticated and way more optimized than the old interpreters, so it stands a much better change of performing (over time) closer to just straight compiled code. Of course, the newer compilers also have more fancy optimizing tricks so they tend to generate way better code than they used to as well. But most optimizations, most often (although not always) make some type of trade-off such that they are not always faster in all circumstances. Like everything else, nothing comes for free, so the optimizers must get their boast from somewhere (although often times it using compile-time CPU to save runtime CPU).

Getting back to C, it is a simple language, that can be compiled into fairly optimized assembly and then run directly on the target machine. In C, if you increment an integer, it's more than likely that it is only one assembler step in the CPU, in Java however, it could end up being a lot more than that (and could include a bit of garbage collection as well :-) C offers you an abstraction that is way closer to the machine (assembler is the closest), but you end up having to do way more work to get it going and it is not as protected, easy to use or error friendly. Most other languages give you a higher abstraction and take care of more of the underlying details for you, but in exchange for their advanced functionality they require more resources to run. As you generalize some solutions, you have to handle a broader range of computing, which often requires more resources.

保罗。

这不是语言的问题，而是工具和库的问题。C语言可用的库和编译器比新语言要老得多。你可能认为这会让它们变慢，但事实恰恰相反。

这些库是在处理能力和内存非常重要的时候编写的。它们必须写得非常高效，才能发挥作用。C编译器的开发人员也花了很长时间为不同的处理器进行各种巧妙的优化。C语言的成熟和广泛采用使得它比同时期的其他语言具有显著的优势。它还使C语言在速度上比那些不像C语言那样强调原始性能的新工具更有优势。

我认为没有人提到这样一个事实，即在C编译器上投入的精力比任何其他编译器都要多，也许Java是例外。

由于前面提到的许多原因，C是非常可优化的——几乎比任何其他语言都要多。因此，如果在其他语言编译器上投入同样的精力，C可能仍然会名列前茅。

I think there is at least one candidate language that with effort could be optimized better than C and thus we could see implementations that produce faster binaries. I'm thinking of digital mars D because the creator took care to build a language that could potentially be better optimized than C. There may be other languages that have this possibility. However I cannot imagine that any language will have compilers more than just a few percent faster than the best C compilers. I would love to be wrong.

我认为真正的“唾手可得的果实”将是那些被设计为易于人类优化的语言。一个熟练的程序员可以让任何语言运行得更快——但有时你不得不做一些荒谬的事情或使用不自然的结构来实现这一点。尽管这总是需要付出努力，但一种好的语言应该产生相对快速的代码，而不必纠结于程序究竟是如何编写的。

It's also important (at least to me) that the worst case code tends to be fast. There are numerous "proofs" on the web that Java is as fast or faster than C, but that is based on cherry picking examples. I'm not big fan of C, but I know that ANYTHING I write in C is going to run well. With Java it will "probably" run within 15% of the speed, usually within 25% but in some cases it can be far worse. Any cases where it's just as fast or within a couple of percent are usually due to most of the time being spent in the library code which is heavily optimized C anyway.

这实际上是一个长期存在的谎言。虽然C程序确实经常更快，但情况并非总是如此，特别是当C程序员不太擅长它的时候。

人们往往会忘记的一个明显的漏洞是，当程序必须为某种IO阻塞时，比如任何GUI程序中的用户输入。在这些情况下，使用什么语言并不重要，因为您受到数据传入速度的限制，而不是处理数据的速度。在这种情况下，不管你使用的是C、Java、c#甚至Perl;你不能比数据进入的速度更快。

The other major thing is that using garbage collection and not using proper pointers allows the virtual machine to make a number of optimizations not available in other languages. For instance, the JVM is capable of moving objects around on the heap to defragment it. This makes future allocations much faster since the next index can simply be used rather than looking it up in a table. Modern JVMs also don't have to actually deallocate memory; instead, they just move the live objects around when they GC and the spent memory from the dead objects is recovered essentially for free.

This also brings up an interesting point about C and even more so in C++. There is something of a design philosophy of "If you don't need it, you don't pay for it." The problem is that if you do want it, you end up paying through the nose for it. For instance, the vtable implementation in Java tends to be a lot better than C++ implementations, so virtual function calls are a lot faster. On the other hand, you have no choice but to use virtual functions in Java and they still cost something, but in programs that use a lot of virtual functions, the reduced cost adds up.

我在链接上找到了一个关于为什么有些语言更快，有些更慢的答案，我希望这将更清楚为什么C或c++比其他语言更快，还有一些其他语言也比C更快，但我们不能使用所有的语言。一些解释-

Fortran仍然重要的一个重要原因是它的速度快:用Fortran编写的数字处理例程往往比用大多数其他语言编写的等效例程要快。在这个领域与Fortran竞争的语言是C和c++，因为它们在性能上具有竞争力。

这就提出了一个问题:为什么?是什么让c++和Fortran速度如此之快?为什么它们比其他流行语言(如Java或Python)性能更好?

解释与编译 根据编程语言所鼓励的编程风格和所提供的特性，有许多方法可以对编程语言进行分类和定义。在性能方面，最大的区别是解释语言和编译语言之间的区别。

划分并不难;而是有一个光谱。在一端，我们有传统的编译语言，包括Fortran、C和c++。在这些语言中，有一个独立的编译阶段，将程序的源代码转换为处理器可以使用的可执行形式。

这个编译过程有几个步骤。对源代码进行分析和解析。基本的编码错误，如错字和拼写错误，此时可以检测到。解析后的代码用于生成内存中的表示，该表示也可用于检测错误——这一次是语义错误，例如调用不存在的函数，或者试图对文本字符串执行算术操作。

然后，这个内存中表示形式用于驱动代码生成器，即生成可执行代码的部分。代码优化，以提高所生成代码的性能，在此过程中的不同时间执行:可以在代码表示上执行高级优化，而在代码生成器的输出上使用低级优化。

实际执行代码发生在后面。整个编译过程只是用来创建可以执行的内容。

在另一端，我们有口译员。解释器将包括一个类似于编译器的解析阶段，但这随后用于驱动直接执行，程序立即运行。

最简单的解释器包含与该语言支持的各种特性相对应的可执行代码，因此它将具有用于添加数字、连接字符串以及给定语言所具有的任何其他功能的函数。当它解析代码时，它将查找相应的函数并执行它。在程序中创建的变量将保存在某种将其名称映射到其数据的查找表中。

解释器风格的最极端的例子是类似批处理文件或shell脚本的东西。在这些语言中，可执行代码通常甚至不内置在解释器本身中，而是单独的独立程序。

So why does this make a difference to performance? In general, each layer of indirection reduces performance. For example, the fastest way to add two numbers is to have both of those numbers in registers in the processor, and to use the processor's add instruction. That's what compiled programs can do; they can put variables into registers and take advantage of processor instructions. But in interpreted programs, that same addition might require two lookups in a table of variables to fetch the values to add, then calling a function to perform the addition. That function may very well use the same processor instruction as the compiled program uses to perform the actual addition, but all the extra work before the instruction can actually be used makes things slower.

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这是自动和手动的区别，高级语言是抽象的，因此是自动化的。C/ c++是人工控制和处理的，甚至错误检查代码有时也是人工劳动。

C和c++也是编译语言，这意味着没有任何一种语言可以在任何地方运行，这些语言必须针对您使用的硬件进行微调，从而增加了额外的隐患。尽管现在C/ c++编译器在所有平台上变得越来越普遍，这有点令人不安。您可以在平台之间进行交叉编译。这仍然不是一个到处运行的情况，你基本上是在指示编译器a针对编译器B编译相同的代码，不同的架构。

归根结底，C语言并不意味着容易理解或推理，这也是为什么它们被称为系统语言。他们出现在所有高层次抽象的废话之前。这也是为什么它们不用于前端web编程。他们只是不适合这项任务，他们的意思是解决传统语言工具无法解决的复杂问题。

这就是为什么你会得到一些疯狂的东西(微架构、驱动程序、量子物理、AAA游戏、操作系统)，这些东西C和c++非常适合。速度和数据处理是主要领域。