Re: FreeBSD Compiler Benchmark: gcc-base vs. gcc-ports vs. clang

2011/3/12 Martin Matuska <mm_at_freebsd.org>

> Hi Poul-Henning,
>
> I have redone the test for majority of the processors, this time taking
> 5 samples of each whole testrun, calculating the average, standard
> deviation, relative standard deviation, standard error and relative
> standard error.
>
> The relative standard error is below 0.25% for ~91%, between 0.25% and
> 0.5% for ~7%, 0.5%-1.0% for ~1% and between 1.0%-2.0% for <1% of the
> tests. Under a "test" I mean 5 runs for the same setting of the same
> compiler on the same preocessor.
>
> So let's say I have now the string/base64 test for a core i7 showing the
> following (score +/- standard deviation):
> gcc421: 82.7892 points +/- 0.8314 (1%)
> gcc45-nocona: 96.0882 points +/- 1.1652 (1.21%)
>
> For a relative comparsion of two settings of the same test I could
> calculate the difference of averages = 13.299 (16.06%) points and sum of
> standard deviations = 2.4834 points (3.00%)
>
> Therefore if assuming normal distribution intervals I could say that:
> With a 95% probability gcc45-nocona is faster than gcc421 by at least
> 10.18% (16.06 - 1.96x3.00) or with a 99.9% probability by at least 6.12%
> (16,06 - 3.2906x3.00).
>
> So I should probably take a significance level (e.g. 95%, 99% or 99.9%)
> and normalize all the test scores for this level. Results out of the
> interval (difference is below zero) are then not significant.
>
> What significance level should I take?
>
> I hope this approach is better :)
>
> Dňa 11.03.2011 17:46, Poul-Henning Kamp  wrote / napísal(a):
> > In message <4D7A42CC.8020807_at_FreeBSD.org>, Martin Matuska writes:
> >
> >> But what I can say, e.g. for the Intel Atom processor, if there are
> >> performance gains in all but one test (that falls 2% behind), generic
> >> perl code (the routines benchmarked) on this processor is very likely to
> >> run faster with that setup.
> >
> > No, actually you cannot say that, unless you run all the tests at
> > least three times for each compiler(+flag), calculate the average
> > and standard deviation of all the tests, and see which, if any of
> > the results are statistically significant.
> >
> > Until you do that, you numbers are meaningless, because we have no
> > idea what the signal/noise ratio is.
> >
>
>

Additionally to possible answer by Poul-Henning Kamp , you may consider the
following pages because strength ( sensitivity ) of hypothesis tests are
determined by statistical power computations :

http://en.wikipedia.org/wiki/Statistical_power


http://en.wikipedia.org/wiki/Statistical_hypothesis_testing
http://en.wikipedia.org/wiki/Category:Hypothesis_testing

http://en.wikipedia.org/wiki/Category:Statistical_terminology


Thank you very much .

Mehmet Erol Sanliturk