It seems there are no free* .NET performance profilers that can profile on a line-by-line basis. Therefore, I am looking into using Stopwatch for profiling.
*free as in freedom, i.e. license includes commercial applications.
EDIT: In response to those who told me "buy a profiler", I would like to, but if I could spend that much money I would spend it on something else. I tried to convince my boss that a profiler is worth it, but haven't had much luck. This question is mostly based on curiosity. I would never consider Stopwatch as a replacement for a real profiler.
I have a little test app (written in C#) that measures performance differences when using a Stopwatch on a per-line basis. The test code is this:
int n = 100;
BigInteger f = 1;
for (int i = n; i > 1; i--)
{
f *= i;
}
Here is the full code: http://pastebin.com/AvbQmT32
I have a Stopwatch for each line of code. This is my 'profiler'. I also have one Stopwatch for the whole program. This is my 'profiler profiler'.
I have the program configured as Release mode, Any CPU (on an x64 machine), and optimizations disabled.
When I run the program with the profiler disabled, I get something like this:
Line | Ticks
------------------------------|----------
|
Total time: | 359
When I run it with the profiler enabled, I get something like this:
Line | Ticks
------------------------------|----------
|
int n = 100; | 3
BigInteger f = 1; | 12
for (int i = n; i > 1; i--) | 325
{ |
f *= i; | 539
} |
|
Total time: | 1710
Stopwatch overhead: | 831
Ideally, the time spent on code should be equal in both cases, but it appears that the Stopwatches have overhead that appears within their own elapsed time.
Now, it doesn't often make sense to need to profile every line of a program, as it usually works better with a divide-and-conquer approach. You can usually start by profiling chunks of code, and narrow down on any performance problems.
Also, in most applications, the average line of code will be a lot slower than the ones in the test program. This means that there will be less Stopwatch overhead.
However, there is still overhead when using Stopwatches, especially if you use a lot.
So down to the question:
What is the most effective way to use Stopwatches for profiling? How can I minimize overhead? Is it even worth it to wrap a Stopwatch around a single statement?
I appreciate your feedback.
First thing's first: your results are not at all surprising. If you used a commercial profiler, you would see something similar: your program will take a lot longer to run when it is being profiled than when it is not. The more granular you set the profiler, the longer you can expect it to take. When you consider that statements like "i > 1" and "i--" will likely be executed as single processor instructions, it becomes obvious why profiling the execution time of a particular line can take a lot longer than executing the line itself.
The fact that profiling is increasing the overall running time of your program should not be a concern; as several others have mentioned, what matters is not the absolute amount of time the program runs, but comparing the running time of the individual parts against each other to find the bottleneck. But there is another concern. Stopwatch will use the high frequency timer from the underlying OS if one is available; but even that may not be high enough. The high frequency timer on my Windows 7 64-bit i5-2400 (Quad core 3.10 GHz) ticks 3,020,556 times per second. That sounds like a lot; but at that rate, the processor could execute a thousand instructions between ticks. That means if you are trying to measure the time it takes to execute a single instruction, you are either going to way under or way overshoot.
You would be better off profiling at the method level. Even then you will run into frequency issues, particularly if you have small well-factored methods. But the results will be more reliable than at the line level; and once you have identified the method that is causing the bottleneck, you can examine it directly to determine how to optimize it to perform better.
All of this leaves aside the many caveats that go along with performance profiling in general, which would be outside the scope of this post. Make sure you do additional research on the subject to understand how you should interpret whatever results you get. As a quick example, your profiling may reveal that the majority of the time in your program is being spent in a particular method; but does that mean that the method itself is the problem, or that other methods are calling it to often? Answering that kind of question is where the real difficulty in profiling lies.