Statistical theory behind OProfile

Hello,

I wanted to use oprofile and apply some statistical work while
profiling some the programs I work on.  I'm wondering what the
statistical theory and probably also computer science theory behind
the program is.  There are two sources I've found on the subject that
I can currently access.

"Yet another oprofile tutorial"[1] goes through statistical theory
behind it, but I can't seem to cross-reference the entire source with
anything and it does not have sources on its own.  It seems solid,
however, but it's in a weird place where since there's nothing to
verify all of it with that I am skeptical.

"A Randomized Sampling Clock for CPU Utilization Estimation and Code
Profiling"[2] is a paper on the subject that I would like to find more
material like it on, but I am terrible at traversing academia and the
like.  It goes through the general process that statistical profiling
is and what distributions it comes from, but more interestingly it
specifies the bias that can arise from timers that do not have
sufficient randomness attached to them.  However, the article is 12
years old, so the implementation details that produce bias may not
apply.

In short, I wondering what sort of behind-the-scenes process oprofile
is statistically.  It just returns samples.  I'd like to know the
distribution it would come from (which seems to be the Poisson
distribution based on my sources) so I may gather some intuition from
the output.  I also want to know if OProfile does its sampling in a
manner that may introduce bias.

The environment I am working on is an armv5 board, which means I have
to use release 0.9.9 for software interrupts.

Any insight is appreciated.

Thank you,
Donald Johnson

[1] - http://ssvb.github.io/2011/08/23/yet-another-oprofile-tutorial.html
[2] - http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.50.3208&rep=rep1&type=pdf

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