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.
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.
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).