- Neatened the braces in PyList_New().
- Made sure "indexerr" was initialized to NULL.
- Factored if blocks in PyList_Append().
- Made sure "allocated" is initialized in list_init().
close() calls would attempt to free() the buffer already free()ed on
the first close(). [bug introduced with patch #788249]
Making sure that the buffer is free()ed in file object deallocation is
a belt-n-braces bit of insurance against a memory leak.
the newly created tuples, but tuples added in the freelist are now cleared in
tupledealloc already (which is very cheap, because we are already
Py_XDECREF'ing all elements anyway).
Python should have a standard Py_ZAP macro like ZAP in pystate.c.
This gives another 30% speedup for operations such as
map(func, d.iteritems()) or list(d.iteritems()) which can both take
advantage of length information when provided.
* Split into three separate types that share everything except the
code for iternext. Saves run time decision making and allows
each iternext function to be specialized.
* Inlined PyDict_Next(). In addition to saving a function call, this
allows a redundant test to be eliminated and further specialization
of the code for the unique needs of each iterator type.
* Created a reusable result tuple for iteritems(). Saves the malloc
time for tuples when the previous result was not kept by client code
(this is the typical use case for iteritems). If the client code
does keep the reference, then a new tuple is created.
Results in a 20% to 30% speedup depending on the size and sparsity
of the dictionary.
* Factored constant structure references out of the inner loops for
PyDict_Next(), dict_keys(), dict_values(), and dict_items().
Gave measurable speedups to each (the improvement varies depending
on the sparseness of the dictionary being measured).
* Added a freelist scheme styled after that for tuples. Saves around
80% of the calls to malloc and free. About 10% of the time, the
previous dictionary was completely empty; in those cases, the
dictionary initialization with memset() can be skipped.
scheme in situations that likely won't benefit from it. This further
improves memory utilization from Py2.3 which always over-allocates
except for PyList_New().
Situations expected to benefit from over-allocation:
list.insert(), list.pop(), list.append(), and list.extend()
Situations deemed unlikely to benefit:
list_inplace_repeat, list_ass_slice, list_ass_subscript
The most gray area was for listextend_internal() which only runs
when the argument is a list or a tuple. This could be viewed as
a one-time fixed length addition or it could be viewed as wrapping
a series of appends. I left its over-allocation turned on but
could be convinced otherwise.
worth it to in-line the call to PyIter_Next().
Saves another 15% on most list operations that acceptable a general
iterable argument (such as the list constructor).
Martin v. Löwis
Raymond Hettinger
Walter Dörwald
Michael W. Hudson
Thomas Heller
Hye-Shik Chang
Andrew MacIntyre
Phillip J. Eby
Armin Rigo
Tim Peters
Nicholas Bastin
Brett Cannon
Guido van Rossum
Fred Drake