see sf #506436

be longer than the old string.

this was trying to assert, but the name it referenced didn't exist.

directly when no comparison function is specified.  This saves a layer
of function call on every compare then.  Measured speedups:

 i    2**i  *sort  \sort  /sort  3sort  +sort  %sort  ~sort  =sort  !sort
15   32768  12.5%   0.0%   0.0% 100.0%   0.0%  50.0% 100.0% 100.0% -50.0%
16   65536   8.7%   0.0%   0.0%   0.0%   0.0%   0.0%  12.5%   0.0%   0.0%
17  131072   8.0%  25.0%   0.0%  25.0%   0.0%  14.3%   5.9%   0.0%   0.0%
18  262144   6.3% -10.0%  12.5%  11.1%   0.0%   6.3%   5.6%  12.5%   0.0%
19  524288   5.3%   5.9%   0.0%   5.6%   0.0%   5.9%   5.4%   0.0%   2.9%
20 1048576   5.3%   2.9%   2.9%   5.1%   2.8%   1.3%   5.9%   2.9%   4.2%

The best indicators are those that take significant time (larger i), and
where sort doesn't do very few compares (so *sort and ~sort benefit most
reliably).  The large numbers are due to roundoff noise combined with
platform variability; e.g., the 14.3% speedup for %sort at i=17 reflects
a printed elapsed time of 0.18 seconds falling to 0.17, but a change in
the last digit isn't really meaningful (indeed, if it really took 0.175
seconds, one electron having a lazy nanosecond could shift it to either
value <wink>).  Similarly the 25% at 3sort i=17 was a meaningless change
from 0.05 to 0.04.  However, almost all the "meaningless changes" were
in the same direction, which is good.  The before-and-after times for
*sort are clearest:

before after
  0.18  0.16
  0.25  0.23
  0.54  0.50
  1.18  1.11
  2.57  2.44
  5.58  5.30

2.2 bugfix candidate (may cause RuntimeError for applications that
currently work fine).

Piers obviously couldn't have passed on any platform.  Fiddling it so it
works (for a meaning of "works" no stronger than "doesn't fail" <wink>).

Change the parser and compiler to use PyMalloc.

Only the files implementing processes that will request memory
allocations small enough for PyMalloc to be a win have been
changed, which are:-
 - Python/compile.c
 - Parser/acceler.c
 - Parser/node.c
 - Parser/parsetok.c

This augments the aggressive overallocation strategy implemented by
Tim Peters in PyNode_AddChild() [Parser/node.c], in reducing the
impact of platform malloc()/realloc()/free() corner case behaviour.
Such corner cases are known to be triggered by test_longexp and
test_import.

Jeremy Hylton, in accepting this patch, recommended this as a
bugfix candidate for 2.2.  While the changes to Python/compile.c
and Parser/node.c backport easily (and could go in), the changes
to Parser/acceler.c and Parser/parsetok.c require other not
insignificant changes as a result of the differences in the memory
APIs between 2.3 and 2.2, which I'm not in a position to work
through at the moment.  This is a pity, as the Parser/parsetok.c
changes are the most important after the Parser/node.c changes, due
to the size of the memory requests involved and their frequency.

- implement viable library search routine for EMX

Fixes #589429.

elements.

[ 589427 ] standard include paths on command line

_ssl still got /usr/include on the command line.

currently-smallest value, and add item, in one gulp.  See the second
N-Best algorithm in the test suite for a natural use.

substantially fewer array-element compares.  This is best practice as of
Kntuh Volume 3 Ed 2, and the code is actually simpler this way (although
the key idea may be counter-intuitive at first glance!  breaking out of
a loop early loses when it costs more to try to get out early than getting
out early saves).
Also added a comment block explaining the difference and giving some real
counts; demonstrating that heapify() is more efficient than repeated
heappush(); and emphasizing the obvious point thatlist.sort() is more
efficient if what you really want to do is sort.

The PyArg_ParseTuple() error string still said "msort".  Changed to "sort".

in the test file.  I have docs for heapq.heapify ready to check in, but
Jack appears to have left behind a stale lock in the Doc/lib directory.

Added new heapify() function, which transforms an arbitrary list into a
heap in linear time; that's a fundamental tool for using heaps in real
life <wink>.

Added heapyify() test.  Added a "less naive" N-best algorithm to the test
suite, and noted that this could actually go much faster (building on
heapify()) if we had max-heaps instead of min-heaps (the iterative method
is appropriate when all the data isn't known in advance, but when it is
known in advance the tradeoffs get murkier).

etc. Still not enough, probably, but better than what we had.

surprises later (the IDE won't work without waste).