R devel - Nov 2003 - fairly OT: profiling

The following is from Eric Raymond's new book on Unix programming.

  You'll get more insight from using profilers if you think of them less
  as ways to collect individual performance numbers, and more as ways to
  learn how performance varies as a function of interesting parameters
  ... Try fitting those numbers to a model, using open-source software
  like R or a good-quality proprietary tool like MATLAB.

It's interesting to see the emphasis on statistical analysis of profiling
data (as well as the fact that R is sufficiently well-known not to need
explanation (or a reference or link)).

	-thomas

Thomas Lumley			Assoc. Professor, Biostatistics
tlumley@u.washington.edu	University of Washington, Seattle

Thomas Lumley <tlumley@u.washington.edu> writes:
> The following is from Eric Raymond's new book on Unix programming.
>
>   You'll get more insight from using profilers if you think of them
less
>   as ways to collect individual performance numbers, and more as ways to
>   learn how performance varies as a function of interesting parameters
>   ... Try fitting those numbers to a model, using open-source software
>   like R or a good-quality proprietary tool like MATLAB.
>
> It's interesting to see the emphasis on statistical analysis of
profiling
> data (as well as the fact that R is sufficiently well-known not to need
> explanation (or a reference or link)).
If you hang out with compiler writers more often (I do, about once a
year, thanks to an old friend who implements superscalars) you'd find
that they've felt the same way for years (and spend a good bit of time
with machine learning algorithms working on weak points, discovery,
and optimization).

(staying off topic, it's also amusing that some of the better groups
generally run on relatively ancient hardware, but that's another
story).

best,
-tony

-- 
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