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(gprof.info)Sampling Error

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Statistical Sampling Error
==========================

   The run-time figures that `gprof' gives you are based on a sampling
process, so they are subject to statistical inaccuracy.  If a function
runs only a small amount of time, so that on the average the sampling
process ought to catch that function in the act only once, there is a
pretty good chance it will actually find that function zero times, or
twice.

   By contrast, the number-of-calls and basic-block figures are derived
by counting, not sampling.  They are completely accurate and will not
vary from run to run if your program is deterministic.

   The "sampling period" that is printed at the beginning of the flat
profile says how often samples are taken.  The rule of thumb is that a
run-time figure is accurate if it is considerably bigger than the
sampling period.

   The actual amount of error can be predicted.  For N samples, the
_expected_ error is the square-root of N.  For example, if the sampling
period is 0.01 seconds and `foo''s run-time is 1 second, N is 100
samples (1 second/0.01 seconds), sqrt(N) is 10 samples, so the expected
error in `foo''s run-time is 0.1 seconds (10*0.01 seconds), or ten
percent of the observed value.  Again, if the sampling period is 0.01
seconds and `bar''s run-time is 100 seconds, N is 10000 samples,
sqrt(N) is 100 samples, so the expected error in `bar''s run-time is 1
second, or one percent of the observed value.  It is likely to vary
this much _on the average_ from one profiling run to the next.
(_Sometimes_ it will vary more.)

   This does not mean that a small run-time figure is devoid of
information.  If the program's _total_ run-time is large, a small
run-time for one function does tell you that that function used an
insignificant fraction of the whole program's time.  Usually this means
it is not worth optimizing.

   One way to get more accuracy is to give your program more (but
similar) input data so it will take longer.  Another way is to combine
the data from several runs, using the `-s' option of `gprof'.  Here is
how:

  1. Run your program once.

  2. Issue the command `mv gmon.out gmon.sum'.

  3. Run your program again, the same as before.

  4. Merge the new data in `gmon.out' into `gmon.sum' with this command:

          gprof -s EXECUTABLE-FILE gmon.out gmon.sum

  5. Repeat the last two steps as often as you wish.

  6. Analyze the cumulative data using this command:

          gprof EXECUTABLE-FILE gmon.sum > OUTPUT-FILE