on Windows. The test_sequence() ERROR is easily repaired if we're
willing to add an os.unlink() line to mhlib's updateline(). The
test_listfolders FAIL I gave up on -- I don't remember enough about Unix
link esoterica to recall why a link count of 2 is something a well-
written program should be keenly interested in <wink>.
Builder carbon NIB files from Python. As-is, I may need to twiddle a few
things as he donated this long ago.
Donovan is now one of the four people in the world who know how to drive
bgen!
- steer people away from installing with sudo
- warn that fink-installed software may cause trouble
- explain why you might want a framework build and point people to
Mac/OSX/README.
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
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).
