Can a profiler change how long recursive calls take to run in Java?

I'm working on refactoring some code in Java, so I I'm timing things to make sure the code doesn't get any s开发者_开发技巧lower. However, the new refactored code seems to take more time than the original code. Remarkably, when I run the code with a profiler, the new code is significantly faster than the old code. The primary difference is that the old code is recursive, while the new code is iterative. Can a profiler affect the recursive code by a factor of several hundred thousand while only affecting the iterative code by a factor of 1.5?

I'm running on Mac OS X 10.6.6, 3 GB RAM, 2.4 GHz CPU, using the default Netbeans 6.9 profiler with Java 1.6.0__22 64-Bit Server.

(Both methods have self-timing code using System.currentTimeMillis() to allow me to compare the times when not using a profiler, but this shouldn't affect things noticeably.)

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Yes. Most profiles do instrumentation at the level of method invocations. In recursive form, the profiler must take a lot more measurements than in iterative form. While profilers do try to extract their overhead from the reported numbers, this is very difficult to do reliable. Different profilers will be better/worse at this.

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I'm working on refactoring some code in Java, so I I'm timing things to make sure the code doesn't get any slower. However, the new refactored code seems to take more time than the original code.

Yes. Code typically runs slower under a profiler.

Therefore, you should compare times of old / new version of your application, either both run under the profiler, or both run normally.

Also be aware that a profiler can actually distort performance characteristics. And different profilers may disagree about where the code hotspots are. So it is a good idea to run/compare versions of your application without profiling before you adopt an optimization that you are trialing.

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(Both methods have self-timing code using System.currentTimeMillis() to allow me to compare the times when not using a profiler, but this shouldn't affect things noticeably.)

There are traps here too:

• The best possible granularity of currentTimeMillis() is 1 millisecond, but on some OSes it might be tens of milliseconds.

• If you don't take steps to avoid this, your manual timings can include distorting overheads such as the JIT compilation times and the GC times. The effects could be quite subtle.

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I would say if you want to measure speed, just measure speed, don't profile. They're not the same thing. Instrumenting profilers put a lot of overhead into each function call, and if all you want is an overall speed difference, it won't be accurate because you're partly measuring the cost of the instrumentation itself.

If you want to find out what is taking the time, that is different from measuring. A wall-clock-time stack-sampling profiler (not instrumentation) that reports line-level percent is your best bet. It doesn't matter if it slows the program down, because it's purpose is not to measure speed; it's purpose is to find out where the time is going and why, on a percentage basis. It would be OK if it or something else slowed the program down by 10%, or 10 times, if it showed you where the time was being taken, independent of speed.

I only say this because lots of people are confused about this point, and the confusion gets solidified into lots of profilers.

More on that subject.