On Sun, Mar 5, 2017 at 10:51 AM David Mertz <me...@gnosis.cx> wrote:
> Thanks for the hard work, this looks very promising.
>
Thank you!
> Real world data tends to be mostly-sorted. So it would be useful for your
> benchmarks to include:
>
> A) Performance on completely sorted data
> i) Of homogenous type
> ii) Of mixed type
> B) Performance on mostly-sorted data
> i) Of homogenous type
> ii) Of mixed type
>
You are entirely correct, as the benchmarks below demonstrate. I used the
benchmark lists from Objects/listsort.txt, which are:
\sort: descending data
/sort: ascending data
3sort: ascending, then 3 random exchanges
+sort: ascending, then 10 random at the end
%sort: ascending, then randomly replace 1% of elements w/ random
values
~sort: many duplicates
=sort: all equal
My results are below (the script can be found at
https://github.com/embg/python-fastsort-benchmark/blob/master/bench-structured.py
):
Homogeneous ([int]):
\sort: 54.6%
/sort: 56.5%
3sort: 53.5%
+sort: 55.3%
%sort: 52.4%
~sort: 48.0%
=sort: 45.2%
Heterogeneous ([int]*n + [0.0]):
\sort: -17.2%
/sort: -19.8%
3sort: -18.0%
+sort: -18.8%
%sort: -10.0%
~sort: -2.1%
=sort: -21.3%
As you can see, because there's a lot less non-comparison overhead in the
structured lists, the impact of the optimization is much greater, both in
performance gain and in worst-case cost. However, I would argue that these
data do not invalidate the utility of my patch: the probability of
encountering a type-heterogeneous list is certainly less than 5% in
practice. So the expected savings, even for structured lists, is something
like (5%)(-20%) + (95%)(50%) = 46.5%. And, of course, not *all* the lists
one encounters in practice are structured; certainly not *this* structured.
So, overall, I would say the numbers above are extremely encouraging.
Thanks for pointing out the need for this benchmark, though!
Elliot
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