[Python-bugs-list] [ python-Bugs-574132 ] Major GC related performance regression

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Fri, 28 Jun 2002 07:50:33 -0700


Bugs item #574132, was opened at 2002-06-26 10:33
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Category: Python Interpreter Core
Group: Python 2.3
Status: Open
Resolution: None
Priority: 5
Submitted By: Kevin Jacobs (jacobs99)
Assigned to: Nobody/Anonymous (nobody)
Summary: Major GC related performance regression

Initial Comment:
A change in the Python 2.3 CVS tree has resulted
in a major performance regression.  It shows up
when allocating large lists of objects, but only when
the garbage collector is enabled.  I've attached a simple
script that I use to measure the time it takes to
incrementally append a large number of objects into
a single Python list.   For previous versions of
Python (up to and including 2.2.1), the time scaled
linearly with the number of elements added to the
list (or close enough for the list sizes considered).  
However, with the Python 2.3 CVS version,
the time required to build the list scales quadratically.

Here is the core of my benchmark:
  rows = []
  start = time.time()
  for i in indices:
    rows.append( (1,) )

See the attached file for the full source as well
as timing results for several versions of Python
with and without garbage collection enabled.


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>Comment By: Kevin Jacobs (jacobs99)
Date: 2002-06-28 09:50

Message:
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Here is a detailed analysis of the problem, using the following
kernel:

lst = []
for i in range(100000):
  lst.append( (1,) )

The key ingredients are:

  1) A method is called on a container (rather than 
__setitem__ or
     __setattr__).

  2) A new object is allocated while the method object lives on 
the Python
     VM stack, as shown by the disassembled bytecodes:

         40 LOAD_FAST                1 (lst)
         43 LOAD_ATTR                3 (append)
         46 LOAD_CONST               2 (1)
         49 BUILD_TUPLE              1
         52 CALL_FUNCTION            1

These ingredients combine in the following way to trigger 
quadratic-time
behavior in the Python garbage collector:

  * First, the LOAD_ATTR on "lst" for "append" is called, and 
a PyCFunction
    is returned from this code in descrobject.c:method_get:

        return PyCFunction_New(descr->d_method, obj);

    Thus, a _new_ PyCFunction is allocated every time the 
method is
    requested.

  * This new method object is added to generation 0 of the 
garbage
    collector, which holds a reference to "lst".

  * The BUILD_TUPLE call may then trigger a garbage 
collection cycle.

  * Since the "append" method is in generation 0, the 
reference traversal
    must also follow all objects within "lst", even if "lst" is in
    generation 1 or 2.  This traversal requires time linear in the 
number of
    objects in "lst", thus increasing the overall time complexity 
of the
    code to quadratic in the number of elements in "lst".

Also note that this is a much more general problem than this 
small example.
It can affect many types of objects in addition to methods, 
including
descriptors, iterator objects, and any other object that 
contains a "back
reference".

So, what can be done about this.... One simple solution 
would be to not
traverse some "back references" if we are collecting objects 
in generation
0.  This will avoid traversing virtually all of these ephemoral 
objects that
will trigger such expensive behavior.  If they live long enough 
to pass
through to generation one or two, then clearly they should be 
traversed.

So, what do all of you GC gurus think?  Provided that my 
analysis is sound,
I can rapidly propose a patch to demonstrate this approach if 
there is
sufficient positive sentiment.

PS: I have not looked into why this doesn't happen in Python 
2.2.x or
    before.  I suspect that it must be related to the recent GC 
changes in
    methodobject.py.  I'm not motivated to spend much time 
looking into
    this, because the current GC behavior is technically 
correct, though
    clearly sub-optimal.


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Comment By: Jeremy Hylton (jhylton)
Date: 2002-06-26 23:10

Message:
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The first 4 run calls in this test spenned 70 percent of 
their time in visit_move().

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