blocks|key|1052767|text|如果您使用valgrind，我强烈建议您使用KCacheGrind来可视化性能瓶颈。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1052768|entityMap|0|LINK|mutability|MUTABLE|url|http://kcachegrind.sourceforge.net/html/Home.html^0|5|8|M|B|0|0^^$0|@$1|2|3|4|5|6|7|N|8|@$9|O|A|P|B|C]]|D|@$9|Q|A|R|1|S]]|E|$]]|$1|F|3|-4|5|6|7|T|8|@]|D|@]|E|$]]]|G|$H|$5|I|J|K|E|$L|M]]]]

If you use <code>valgrind</code>, I can highly recommend <a href="http://kcachegrind.sourceforge.net/html/Home.html" rel="nofollow noreferrer">KCacheGrind</a> to visualize performance bottlenecks.

blocks|key|902361|text|我希望在分析器中显示以下功能/输出信息。1.)应该能够显示总时钟周期消耗，也为每个功能。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|902362|2.)如果不是，则应告知所用的总时间和每个函数所花费的时间。|902363|3.)它应该能够告诉你一个函数被调用了多少次。|902364|4.)如果知道内存读取、内存写入、缓存未命中、缓存命中，那就更好了。|902365|5.)每个函数的代码存储器|902366|6.)使用的数据内存:全局常量、堆栈、堆使用率。|902367|=AD|902368|entityMap^0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|Q|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|R|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|S|8|@]|9|@]|A|$]]|$1|H|3|I|5|6|7|T|8|@]|9|@]|A|$]]|$1|J|3|K|5|6|7|U|8|@]|9|@]|A|$]]|$1|L|3|M|5|6|7|V|8|@]|9|@]|A|$]]|$1|N|3|-4|5|6|7|W|8|@]|9|@]|A|$]]]|O|$]]

I would like to have following features/output information shown in a profiler.
1.) Should be able to show Total Clock cycles consumed and also for each function.

2.) If not one, should tell the total time consumed and time spent in each function.

3.) All it should be able to tell how many times a function is called. 

4.) It would be nice to know memory reads, memory writes, cache misses, cache hits.

5.) Code memory for each function

6.) Data memory used: Global constants, Stack, Heap usage.

=AD

blocks|key|1046992|text|两个经典的答案(假设你在*nix世界中)是valgrind和gprof。你想要一个能让你(至少)检查你在每个过程或函数中花费了多少时间的东西。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1046993|entityMap|0|LINK|mutability|MUTABLE|url|http://valgrind.org/|1|http://www.cs.utah.edu/dept/old/texinfo/as/gprof_toc.html^0|L|8|0|U|5|1|0^^$0|@$1|2|3|4|5|6|7|N|8|@]|9|@$A|O|B|P|1|Q]|$A|R|B|S|1|T]]|C|$]]|$1|D|3|-4|5|6|7|U|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]|L|$5|G|H|I|C|$J|M]]]]

The two classical answers (assuming you are in *nix world) are <a href="http://valgrind.org/" rel="nofollow noreferrer">valgrind</a> and <a href="http://www.cs.utah.edu/dept/old/texinfo/as/gprof_toc.html" rel="nofollow noreferrer">gprof</a>. You want something that will let you (at least) check how much time you are spending inside each procedure or function.

blocks|key|1052773|text|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1052774|稳定性-能够长时间分析您的进程，而不会崩溃或内存不足。令人惊讶的是，有这么多商业分析器没有做到这一点。|unordered-list-item|1052775|1052776|entityMap^0|0|0|0^^$0|@$1|2|3|-4|4|5|6|G|7|@]|8|@]|9|$]]|$1|A|3|B|4|C|6|H|7|@]|8|@]|9|$]]|$1|D|3|-4|4|5|6|I|7|@]|8|@]|9|$]]|$1|E|3|-4|4|5|6|J|7|@]|8|@]|9|$]]]|F|$]]

<ul>
<li>Stability - be able to profile your process for long durations without crashing or running out of memory. its surprising how many commercial profilers fail that.</li>
</ul>

blocks|key|3218107|text|goldenmean说的没错，我想补充的是，行执行计数有时也很方便。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3218108|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

goldenmean has it right, I would add that line execution counts are sometimes handy as well.

blocks|key|1052820|text|您所需要的只是一个具有“暂停”按钮的调试器或IDE。它不仅是最简单和最便宜的工具，而且在我的经验中，也是最好的。This+is+a+complete+explanation+why.+Note+the+2nd-to-last+comment.|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1052821|编辑是因为我想到了一个更好的答案：|1052822|顺便说一句，我在70年代学习了人工智能，一个非常流行的想法是自动编程，很多人都试图实现它。(我试过了。)这个想法是试图自动化拥有领域的知识结构以及所需的功能需求的过程，以生成(和调试)一个能够满足这些需求的程序。它将是关于编程领域的自动推理的一种力量。有一些诱人的演示，但在实际意义上，这一领域并没有走得太远。尽管如此，它确实为编程语言贡献了很多想法，比如契约和逻辑验证技术。|1052823|为了构建一个理想的分析器，为了优化程序的目的，它将每隔一纳秒获取程序状态的样本。可以是即时的，也可以是以后的(理想，记得吗？)它将仔细检查每个样本，看看在知道程序执行的原因后，这一特定的纳秒工作是否真的是必要的，或者是否可以以某种方式消除。|1052824|这将是数十亿个样本和大量的推理，但当然会有大量的重复，因为任何浪费成本，例如，10%25的时间，将在10%25的样本上显而易见。这种浪费可以在不到10亿个样本上识别出来。事实上，100个或更少的样本可以发现它，只要他们是在时间上随机选择的，或者至少在用户关心的时间间隔内。这是假设的目的是找到损耗，以便我们可以摆脱它，而不是更精确地测量它。|1052825|为什么将所有的推理能力应用于每个样本是有帮助的？好吧，如果程序很小，并且它只寻找O(n%5E2)代码之类的东西，那么它应该不会太难。但是假设程序的状态由20-30层深的过程堆栈组成，一些递归函数调用可能出现不止一次，一些函数可能是对外部处理器进行IO的调用，程序的操作可能是由表中的一些数据驱动的。然后，要确定特定样本是否浪费，需要潜在地检查所有或至少部分状态信息，并使用推理能力来查看在满足功能需求时是否真的有必要。|1052826|分析器正在寻找的是出于可疑的原因而花费的纳秒。要了解它被花费的原因，需要检查堆栈上的每个函数调用站点，以及它周围的代码，或者至少其中一些站点。花费纳秒的必要性要求每个语句在堆栈上执行的必要性的逻辑与。只需要一个这样的函数调用站点就会有一个可疑的理由，整个样本就会有一个可疑的理由。因此，如果整个目的是为了找出由于可疑的原因而花费的纳秒，那么样本越复杂越好，对每个样本施加的推理能力越强越好。(这就是为什么较大的程序有更大的加速空间-它们有更深的堆栈，因此调用更多，因此更有可能出现不合理的调用。)|1052827|好吧，那是将来的事了。然而，由于我们不需要大量的样本(10或20个是非常有用的)，而且我们已经有了高度智能的自动编程器(由披萨和苏打提供支持)，所以我们现在可以这样做。|1052828|将其与我们现在称为分析器的工具进行比较。它们中最好的是获取堆栈样本，但是它们的输出是什么呢？测量结果。“热路径”。鼠巢图。令人眼花缭乱。因此，即使是人工智能的程序员也很容易忽略效率低下的地方，除了那些输出暴露出来的地方。在你修复了你找到的那些之后，你找不到的那些就是那些让一切变得不同的。|1052829|从研究人工智能中学到的一件事是，如果原则上人类不能做某件事，不要指望能给计算机编程。|1052830|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/questions/375913/what-can-i-use-to-profile-c-code-in-linux/378024#378024^0|1K|1T|0|0|0|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|13|8|@]|9|@$A|14|B|15|1|16]]|C|$]]|$1|D|3|E|5|6|7|17|8|@]|9|@]|C|$]]|$1|F|3|G|5|6|7|18|8|@]|9|@]|C|$]]|$1|H|3|I|5|6|7|19|8|@]|9|@]|C|$]]|$1|J|3|K|5|6|7|1A|8|@]|9|@]|C|$]]|$1|L|3|M|5|6|7|1B|8|@]|9|@]|C|$]]|$1|N|3|O|5|6|7|1C|8|@]|9|@]|C|$]]|$1|P|3|Q|5|6|7|1D|8|@]|9|@]|C|$]]|$1|R|3|S|5|6|7|1E|8|@]|9|@]|C|$]]|$1|T|3|U|5|6|7|1F|8|@]|9|@]|C|$]]|$1|V|3|-4|5|6|7|1G|8|@]|9|@]|C|$]]]|W|$X|$5|Y|Z|10|C|$11|12]]]]

All you need is a debugger or IDE that has a "pause" button. It is not only the simplest and cheapest tool, but in my experience, the best. <a href="https://stackoverflow.com/questions/375913/what-can-i-use-to-profile-c-code-in-linux/378024#378024">This is a complete explanation why. Note the 2nd-to-last comment.</a>

EDIT because I thought of a better answer:

As an aside, I studied A.I. in the 70s, and an idea very much in the air was automatic programming, and a number of people tried to accomplish it.
(I took my crack at it.)
The idea is to try to automate the process of having a knowledge structure of a domain, plus desired functional requirements, to generate (and debug) a program that would accomplish those requirements.
It would be a tour-de-force in automated reasoning about the domain of programming.
There were some tantalizing demonstrations, but in a practical sense the field didn't go very far.
Nevertheless, it did contribute a lot of ideas to programming languages, like contracts and logical verification techniques.

To build an ideal profiler, for the purpose of optimizing programs, it would get a sample of the program's state every nanosecond.
Either on-the-fly or later (ideal, remember?) it would carefully examine each sample, to see if, knowing the reasons for which the program is executing, that particular nanosecond of work was actually necessary or could be somehow eliminated.

That would be billions of samples and a lot of reasoning, but course there would be tremendous duplication, because any wastage costing, say, 10% of time, would be evident on 10% of samples.
That wastage could be recognized on a lot fewer than a billion samples.
If fact, 100 samples or even less could spot it, provided they were randomly chosen in time, or at least in the time interval the user cares about.
This is assuming the purpose is to find the wastage so we can get rid of it, as opposed to measuring it with much precision.

Why would it be helpful to apply all that reasoning power to each sample?
Well, if the programs were little, and it were only looking for things like O(n^2) code, it shouldn't be too hard.
But suppose the state of the program consisted of a procedure stack 20-30 levels deep, possibly with some recursive function calls appearing more than once, possibly with some of the functions being calls to external processors to do IO, possibly with the program's action being driven by some data in a table.
Then, to decide if the particular sample is wasteful requires potentially examining all or at least some of that state information, and using reasoning power to see if it is truly necessary in accomplishing the functional requirements.

What the profiler is looking for is nanoseconds being spent for dubious reasons.
To see the reason it is being spent requires examining every function call site on the stack, and the code surrounding it, or at least some of those sites.
The necessity of the nanosecond being spent requires the logical AND of the necessity of every statement being executed on the stack.
It only takes one such function call site to have a dubious justification for the entire sample to have a dubious justification.
So, if the entire purpose is to find nanoseconds being spent for dubious reasons, the more complicated the samples are, the better,
and the more reasoning power brought to bear on each sample, the better.
(That's why bigger programs have more room for speedup - they have deeper stacks, hence more calls, hence more likelihood of poorly justified calls.)

OK, that's in the future.
However, since we don't need a huge number of samples (10 or 20 is very useful), and since we already have highly intelligent automatic programmers (powered by pizza and soda),
we can do this now.

Compare that to the tools we call profilers today.
The very best of them take stack samples, but what's their output?
Measurements. "Hot paths". Rat's nest graphs. Eye-candy.
From those, even an artificially intelligent programmer would easily miss large inefficiencies, except for the ones that are exposed by those outputs.
After you fix the ones you do find, the ones you don't find are the ones that make all the difference.

One of the things one learns studying A.I. is, don't expect to be able to program a computer to do something if a human, in principle, can't also do it.

blocks|key|1047086|text|我更喜欢采样剖析器而不是插装的剖析器。分析器应该能够将样本数据映射回源代码，最好是在GUI中。我所知道的两个最好的例子是：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1047087|Shark+developer.apple.com|offset|length|1047088|Linux:缩放www.rotateright.com|unordered-list-item|1047089|
+|1047090|Mac|1047091|1047092|entityMap|0|LINK|mutability|MUTABLE|url|http://developer.apple.com/|1|http://www.rotateright.com/^0|0|6|J|0|0|8|J|1|0|0|0|0^^$0|@$1|2|3|4|5|6|7|X|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|Y|8|@]|9|@$D|Z|E|10|1|11]]|A|$]]|$1|F|3|G|5|H|7|12|8|@]|9|@$D|13|E|14|1|15]]|A|$]]|$1|I|3|J|5|6|7|16|8|@]|9|@]|A|$]]|$1|K|3|L|5|H|7|17|8|@]|9|@]|A|$]]|$1|M|3|-4|5|6|7|18|8|@]|9|@]|A|$]]|$1|N|3|-4|5|6|7|19|8|@]|9|@]|A|$]]]|O|$P|$5|Q|R|S|A|$T|U]]|V|$5|Q|R|S|A|$T|W]]]]

My preference is for sampling profilers rather than instrumented profilers. The profiler should be able to map sample data back to the source code, ideally in a GUI. The two best examples of this that I am aware of are:

<ul>
<li>Mac OS X: Shark <a href="http://developer.apple.com" rel="nofollow noreferrer">developer.apple.com</a></li>
<li>Linux: Zoom <a href="http://www.rotateright.com" rel="nofollow noreferrer">www.rotateright.com</a></li>
</ul>

I'm trying to pick a perforamnce analyzer to use. I'm a beginner developer and not sure what to look for in a performance analyzer. What are the most important features?

What are your favorite features of a C/C++ performance profiler / analyzer?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我在试着挑选一个穿透分析仪来使用。我是一个初级开发人员，不确定要在性能分析器中寻找什么。最重要的功能是什么？

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