blocks|key|317702|text|代码运行速度变慢并不重要(当然，抛开您的方便性不谈)-分析器仍然会告诉您每个函数花费的正确时间比例。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|317703|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|$]]

It doesn't matter that the code is running slower (your convenience aside, of course) - the profiler will still tell you the correct proportion of time spent in each function.

blocks|key|319574|text|使用CPU的高性能计时机制(例如，x86)开发几个宏，并将运行核心循环的单个线程绑定到特定的CPU+(set+the+affinity)。您需要实现以下宏。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|319575|PROF_INIT+//allocate+any+variables+--+probably+a+const+char
PROF_START("name")+//+start+a+timer
PROF_STOP()+//+end+a+timer+and+calculate+the+difference+--+
++++++++++++//+which+you+write+out+using+a+async+fd|code-block|syntax|javascript|319576|我在我感兴趣的每个函数中都放了这样的东西，我确保宏将计时调用放在调用树的上下文中--这可能是最准确的分析方法。|319577|备注：|style|BOLD|319578|该方法由您的代码驱动，并且不依赖外部工具以任何方式窥探您的代码。当涉及到小段代码时，监听、采样和中断驱动的分析是不准确的。此外，您还希望控制何时何地收集计时数据--比如在代码中的特定构造，如循环、递归调用链的开始或大容量内存分配。|319579|-编辑--|319580|您可能会对link+from+this+answer+to+one+of+my+questions感兴趣。|319581|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/Time_Stamp_Counter|1|http://en.wikipedia.org/wiki/Processor_affinity|2|https://stackoverflow.com/questions/1833253/preserving-the-execution-pipeline/1833390#1833390^0|H|3|0|1F|G|1|0|0|0|0|3|0|0|0|5|18|2|0^^$0|@$1|2|3|4|5|6|7|16|8|@]|9|@$A|17|B|18|1|19]|$A|1A|B|1B|1|1C]]|C|$]]|$1|D|3|E|5|F|7|1D|8|@]|9|@]|C|$G|H]]|$1|I|3|J|5|6|7|1E|8|@]|9|@]|C|$]]|$1|K|3|L|5|6|7|1F|8|@$A|1G|B|1H|M|N]]|9|@]|C|$]]|$1|O|3|P|5|6|7|1I|8|@]|9|@]|C|$]]|$1|Q|3|R|5|6|7|1J|8|@]|9|@]|C|$]]|$1|S|3|T|5|6|7|1K|8|@]|9|@$A|1L|B|1M|1|1N]]|C|$]]|$1|U|3|-4|5|6|7|1O|8|@]|9|@]|C|$]]]|V|$W|$5|X|Y|Z|C|$10|11]]|12|$5|X|Y|Z|C|$10|13]]|14|$5|X|Y|Z|C|$10|15]]]]

Develop a few macros using the high performance timing mechanism of your CPU (e.g., <a href="http://en.wikipedia.org/wiki/Time_Stamp_Counter" rel="nofollow noreferrer">x86</a>) -- the routines that don't rely on system calls, and bind a single thread running your core loop to a specific CPU (<a href="http://en.wikipedia.org/wiki/Processor_affinity" rel="nofollow noreferrer">set the affinity</a>). You would need to implement the following macro's.

<pre><code>PROF_INIT //allocate any variables -- probably a const char
PROF_START("name") // start a timer
PROF_STOP() // end a timer and calculate the difference -- 
 // which you write out using a async fd
</code></pre>

I had something like this that I placed in every function I was interested in, I made sure the macro's placed the timing calls into the context of the call tree -- this is possibly the most accurate way to profile.

Note:

This method is driven by your code -- and does not rely on an external tool to snoop your code in any way. Snooping, Sampling and interrupt driven profiling is inaccurate when it comes to small sections of code. Besides, you want to control where and when the timing data is collected -- like at specific constructs in your code, like loops, the beginning of a recursive call-chain or mass memory allocations.

-- edit --

You might be interested in the <a href="https://stackoverflow.com/questions/1833253/preserving-the-execution-pipeline/1833390#1833390">link from this answer to one of my questions</a>.

blocks|key|317727|text|我假设您想要做的是找出哪些代码行花费了您足够的成本来进行优化。这与定时函数有很大的不同。You+can+do+better+than+.|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|317728|Here's+a+fairly+complete+explanation+of+how+to+do+it.|317729|您可以手动完成此操作，也可以使用可以提供相同信息的分析器之一，例如和。|317730|顺便说一句，只有当被内联的例程很小，并且不调用函数本身，并且它们被调用的行在足够长的时间内是活动的，才有重要的价值。|317731|至于调试和发布版本之间的数量级性能比率，这可能是由于许多因素，可能是也可能不是内联。您可以使用上面提到的stackshot方法来确定在这两种情况下到底发生了什么。我发现，由于其他原因，调试构建可能会很慢，比如递归数据结构验证。|317732|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/questions/1777556/alternatives-to-gprof/1779343#1779343|1|https://stackoverflow.com/questions/375913/what-can-i-use-to-profile-c-code-in-linux/378024#378024|2|https://stackoverflow.com/questions/406760/whats-your-most-controversial-programming-opinion/1562802#1562802^0|18|O|0|0|0|1H|1|0|0|0|1G|9|2|0^^$0|@$1|2|3|4|5|6|7|X|8|@]|9|@$A|Y|B|Z|1|10]]|C|$]]|$1|D|3|E|5|6|7|11|8|@]|9|@$A|12|B|13|1|14]]|C|$]]|$1|F|3|G|5|6|7|15|8|@]|9|@]|C|$]]|$1|H|3|I|5|6|7|16|8|@]|9|@]|C|$]]|$1|J|3|K|5|6|7|17|8|@]|9|@$A|18|B|19|1|1A]]|C|$]]|$1|L|3|-4|5|6|7|1B|8|@]|9|@]|C|$]]]|M|$N|$5|O|P|Q|C|$R|S]]|T|$5|O|P|Q|C|$R|U]]|V|$5|O|P|Q|C|$R|W]]]]

I assume what you want to do is find out which lines of code are costing you enough to be worth optimizing. That is very different from timing functions. <a href="https://stackoverflow.com/questions/1777556/alternatives-to-gprof/1779343#1779343">You can do better than gprof.</a>

<a href="https://stackoverflow.com/questions/375913/what-can-i-use-to-profile-c-code-in-linux/378024#378024">Here's a fairly complete explanation of how to do it.</a>

You can do it by hand, or use one of the profilers that can provide the same information, such as <a href="http://oprofile.sourceforge.net/news/" rel="nofollow noreferrer">oprofile</a>, and <a href="http://www.rotateright.com/" rel="nofollow noreferrer">RotateRight/Zoom</a>.

BTW, inlining is of significant value only if the routines being inlined are small and don't call functions themselves, and if the lines where they are being called are active enough of the time to be significant.

As for the order of magnitude performance ratio between debug and release build, it may be due to a number of things, maybe or maybe not the inlining. You can use the <a href="https://stackoverflow.com/questions/406760/whats-your-most-controversial-programming-opinion/1562802#1562802">stackshot</a> method mentioned above to find out for certain just what's going on in either case. I've found that debug builds can be slow for other reasons, like recursive data structure validation, for example.

blocks|key|322538|text|您可以使用功能更强大的分析器，例如英特尔的VTune，它可以为您提供装配线级别的性能详细信息。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|322539|http://software.intel.com/en-us/intel-vtune/|offset|length|322540|它适用于Windows和Linux，但确实要花钱...|322541|entityMap|0|LINK|mutability|MUTABLE|url^0|0|0|18|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|P|8|@]|9|@$D|Q|E|R|1|S]]|A|$]]|$1|F|3|G|5|6|7|T|8|@]|9|@]|A|$]]|$1|H|3|-4|5|6|7|U|8|@]|9|@]|A|$]]]|I|$J|$5|K|L|M|A|$N|C]]]]

You can use a more powerful profiler, such as Intel's VTune, which can give you assembly line level of performance detail. 

<a href="http://software.intel.com/en-us/intel-vtune/" rel="nofollow noreferrer">http://software.intel.com/en-us/intel-vtune/</a>

It's for Windows and Linux, but does cost money...

blocks|key|319611|text|valgrind会有更大的帮助吗？|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|319612|与KCachegrind图形用户界面相结合，它提供了一种免费且简单的方式来浏览适合内联代码的带注释的代码。这里有一个非常简单的指令：http://web.stanford.edu/class/cs107/guide_callgrind.html|319613|entityMap|0|LINK|mutability|MUTABLE|url|http://valgrind.org/|1|http://kcachegrind.sourceforge.net/html/Home.html|2|http://web.stanford.edu/class/cs107/guide_callgrind.html^0|0|8|0|0|1|B|1|1U|1K|2|0^^$0|@$1|2|3|4|5|6|7|R|8|@]|9|@$A|S|B|T|1|U]]|C|$]]|$1|D|3|E|5|6|7|V|8|@]|9|@$A|W|B|X|1|Y]|$A|Z|B|10|1|11]]|C|$]]|$1|F|3|-4|5|6|7|12|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]|N|$5|I|J|K|C|$L|O]]|P|$5|I|J|K|C|$L|Q]]]]

Would <a href="http://valgrind.org/" rel="nofollow noreferrer">valgrind</a> be any more helpful?

Combined with <a href="http://kcachegrind.sourceforge.net/html/Home.html" rel="nofollow noreferrer">KCachegrind</a> GUI it offers a free and easy way of browsing annotated code suitable for inlined code.
Here you have pretty a straightforward instruction: <a href="http://web.stanford.edu/class/cs107/guide_callgrind.html" rel="nofollow noreferrer">http://web.stanford.edu/class/cs107/guide_callgrind.html</a>

blocks|key|319615|text|您可以使用gcov提供逐行执行计数。这至少应该告诉您哪些内联函数是瓶颈。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|319616|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|$]]

You can use gcov to give you line-by-line execution counts. This should at least tell you which inlined functions are the bottleneck.

I am trying to figure out where my C++ program is spending its time, using gprof. Here's my dilemma: if I compile with the same optimization settings I use for my release build, pretty much everything gets inlined, and gprof tells me, unhelpfully, that 90% of my time is spent in a core routine, where everything was inlined. On the other hand, if I compile with inlining disabled, the program runs an order of magnitude slower.

I want to find out how much time procedures called from my core routine are taking, when my program is compiled with inlining enabled.

I am running 64-bit Ubuntu 9.04 on a quad-core Intel machine. I looked into google-perftools, but that doesn't seem to work well on x86_64. Running on a 32-bit machine is not an option.

Does anyone have suggestions as to how I can more effectively profile my application, when inlining is enabled?

Edit: Here is some clarification of my problem. I apologize if it was not clear initially.

I want to find where the time was being spent in my application. Profiling my optimized build resulted in gprof telling me that ~90% of the time is spent in main, where everything was inlined. I already knew that before profiling!

What I want to find out is how much time the inlined functions are taking, preferably, without disabling optimization or inlining in my build options. The application is something like an order of magnitude slower when profiling with inlining disabled. This difference in execution time is a convenience issue, but also, I am not confident that the performance profile of the program built with inlining disabled will strongly correspond to the performance profile of the program built with inlining enabled.

In short: is there a way to get useful profiling information on a C++ program without disabling optimization or inlining?

Profiling C++ in the presence of aggressive inlining?

我正在尝试使用gprof找出我的C++程序将时间花在哪里。这是我的两难境地:如果我使用与发布版本相同的优化设置进行编译，几乎所有内容都会内联，而gprof告诉我，我90%的时间都花在核心例程中，所有内容都是内联的。另一方面，如果我在禁用内联的情况下编译，程序的运行速度会慢一个数量级。我想知道当我的程序在启用内联的情况下...

问在具有侵略性内联的情况下分析C++？
EN

社区

活动

资源

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

问在具有侵略性内联的情况下分析C++？EN

社区

活动

资源

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

问在具有侵略性内联的情况下分析C++？
EN