Title: Optimizing Global Variable/Attribute Access
Author: email@example.com (Skip Montanaro)
Type: Standards Track
The bindings for most global variables and attributes of other
modules typically never change during the execution of a Python
program, but because of Python's dynamic nature, code which
accesses such global objects must run through a full lookup each
time the object is needed. This PEP proposes a mechanism that
allows code that accesses most global objects to treat them as
local objects and places the burden of updating references on the
code that changes the name bindings of such objects.
Consider the workhorse function sre_compile._compile. It is the
internal compilation function for the sre module. It consists
almost entirely of a loop over the elements of the pattern being
compiled, comparing opcodes with known constant values and
appending tokens to an output list. Most of the comparisons are
with constants imported from the sre_constants module. This means
there are lots of LOAD_GLOBAL bytecodes in the compiled output of
this module. Just by reading the code it's apparent that the
author intended LITERAL, NOT_LITERAL, OPCODES and many other
symbols to be constants. Still, each time they are involved in an
expression, they must be looked up anew.
Most global accesses are actually to objects that are "almost
constants". This includes global variables in the current module
as well as the attributes of other imported modules. Since they
rarely change, it seems reasonable to place the burden of updating
references to such objects on the code that changes the name
bindings. If sre_constants.LITERAL is changed to refer to another
object, perhaps it would be worthwhile for the code that modifies
the sre_constants module dict to correct any active references to
that object. By doing so, in many cases global variables and the
attributes of many objects could be cached as local variables. If
the bindings between the names given to the objects and the
objects themselves changes rarely, the cost of keeping track of
such objects should be low and the potential payoff fairly large.
In an attempt to gauge the effect of this proposal, I modified the
Pystone benchmark program included in the Python distribution to
cache global functions. Its main function, Proc0, makes calls to
ten different functions inside its for loop. In addition, Func2
calls Func1 repeatedly inside a loop. If local copies of these 11
global idenfiers are made before the functions' loops are entered,
performance on this particular benchmark improves by about two per
cent (from 5561 pystones to 5685 on my laptop). It gives some
indication that performance would be improved by caching most
global variable access. Note also that the pystone benchmark
makes essentially no accesses of global module attributes, an
anticipated area of improvement for this PEP.
I propose that the Python virtual machine be modified to include
TRACK_OBJECT and UNTRACK_OBJECT opcodes. TRACK_OBJECT would
associate a global name or attribute of a global name with a slot
in the local variable array and perform an initial lookup of the
associated object to fill in the slot with a valid value. The
association it creates would be noted by the code responsible for
changing the name-to-object binding to cause the associated local
variable to be updated. The UNTRACK_OBJECT opcode would delete
any association between the name and the local variable slot.
Operation of this code in threaded programs will be no different
than in unthreaded programs. If you need to lock an object to
access it, you would have had to do that before TRACK_OBJECT would
have been executed and retain that lock until after you stop using
FIXME: I suspect I need more here.
Global variables and attributes rarely change. For example, once
a function imports the math module, the binding between the name
"math" and the module it refers to aren't likely to change.
Similarly, if the function that uses the math module refers to its
"sin" attribute, it's unlikely to change. Still, every time the
module wants to call the math.sin function, it must first execute
a pair of instructions:
If the client module always assumed that math.sin was a local
constant and it was the responsibility of "external forces"
outside the function to keep the reference correct, we might have
code like this:
If the LOAD_FAST was in a loop the payoff in reduced global loads
and attribute lookups could be significant.
This technique could, in theory, be applied to any global variable
access or attribute lookup. Consider this code:
l = 
for i in range(10):
Even though l is a local variable, you still pay the cost of
loading l.append ten times in the loop. The compiler (or an
optimizer) could recognize that both math.sin and l.append are
being called in the loop and decide to generate the tracked local
code, avoiding it for the builtin range() function because it's
only called once during loop setup. Performance issues related to
accessing local variables make tracking l.append less attractive
than tracking globals such as math.sin.
According to a post to python-dev by Marc-Andre Lemburg ,
LOAD_GLOBAL opcodes account for over 7% of all instructions
executed by the Python virtual machine. This can be a very
expensive instruction, at least relative to a LOAD_FAST
instruction, which is a simple array index and requires no extra
function calls by the virtual machine. I believe many LOAD_GLOBAL
instructions and LOAD_GLOBAL/LOAD_ATTR pairs could be converted to
Code that uses global variables heavily often resorts to various
tricks to avoid global variable and attribute lookup. The
aforementioned sre_compile._compile function caches the append
method of the growing output list. Many people commonly abuse
functions' default argument feature to cache global variable
lookups. Both of these schemes are hackish and rarely address all
the available opportunities for optimization. (For example,
sre_compile._compile does not cache the two globals that it uses
most frequently: the builtin len function and the global OPCODES
array that it imports from sre_constants.py.
Q. What about threads? What if math.sin changes while in cache?
A. I believe the global interpreter lock will protect values from
being corrupted. In any case, the situation would be no worse
than it is today. If one thread modified math.sin after another
thread had already executed "LOAD_GLOBAL math", but before it
executed "LOAD_ATTR sin", the client thread would see the old
value of math.sin.
The idea is this. I use a multi-attribute load below as an
example, not because it would happen very often, but because by
demonstrating the recursive nature with an extra call hopefully
it will become clearer what I have in mind. Suppose a function
defined in module foo wants to access spam.eggs.ham and that
spam is a module imported at the module level in foo:
x = spam.eggs.ham
Upon entry to somefunc, a TRACK_GLOBAL instruction will be
TRACK_GLOBAL spam.eggs.ham n
"spam.eggs.ham" is a string literal stored in the function's
constants array. "n" is a fastlocals index. "&fastlocals[n]"
is a reference to slot "n" in the executing frame's fastlocals
array, the location in which the spam.eggs.ham reference will
be stored. Here's what I envision happening:
1. The TRACK_GLOBAL instruction locates the object referred to
by the name "spam" and finds it in its module scope. It
then executes a C function like
_PyObject_TrackName(m, "spam.eggs.ham", &fastlocals[n])
where "m" is the module object with an attribute "spam".
2. The module object strips the leading "spam." stores the
necessary information ("eggs.ham" and &fastlocals[n]) in
case its binding for the name "eggs" changes. It then
locates the object referred to by the key "eggs" in its
dict and recursively calls
_PyObject_TrackName(eggs, "eggs.ham", &fastlocals[n])
3. The eggs object strips the leading "eggs.", stores the
("ham", &fastlocals[n]) info, locates the object in its
namespace called "ham" and calls _PyObject_TrackName once
_PyObject_TrackName(ham, "ham", &fastlocals[n])
4. The "ham" object strips the leading string (no "." this
time, but that's a minor point), sees that the result is
empty, then uses its own value (self, probably) to update
the location it was handed:
&fastlocals[n] = self;
At this point, each object involved in resolving
"spam.eggs.ham" knows which entry in its namespace needs to be
tracked and what location to update if that name changes.
Furthermore, if the one name it is tracking in its local
storage changes, it can call _PyObject_TrackName using the new
object once the change has been made. At the bottom end of
the food chain, the last object will always strip a name, see
the empty string and know that its value should be stuffed
into the location it's been passed.
When the object referred to by the dotted expression
"spam.eggs.ham" is going to go out of scope, an
"UNTRACK_GLOBAL spam.eggs.ham n" instruction is executed. It
has the effect of deleting all the tracking information that
The tracking operation may seem expensive, but recall that the
objects being tracked are assumed to be "almost constant", so
the setup cost will be traded off against hopefully multiple
local instead of global loads. For globals with attributes
the tracking setup cost grows but is offset by avoiding the
extra LOAD_ATTR cost. The TRACK_GLOBAL instruction needs to
perform a PyDict_GetItemString for the first name in the chain
to determine where the top-level object resides. Each object
in the chain has to store a string and an address somewhere,
probably in a dict that uses storage locations as keys
(e.g. the &fastlocals[n]) and strings as values. (This dict
could possibly be a central dict of dicts whose keys are
object addresses instead of a per-object dict.) It shouldn't
be the other way around because multiple active frames may
want to track "spam.eggs.ham", but only one frame will want to
associate that name with one of its fast locals slots.
What about this (dumb) code?
l = 
lock = threading.Lock()
for i in range(1000):
elt = l.pop()
It's not clear from a static analysis of the code what the lock is
protecting. (You can't tell at compile-time that threads are even
involved can you?) Would or should it affect attempts to track
"l.append" or "math.sin" in the fill_l function?
If we annotate the code with mythical track_object and untrack_object
builtins (I'm not proposing this, just illustrating where stuff would
go!), we get
l = 
lock = threading.Lock()
for i in range(1000):
elt = l.pop()
Is that correct both with and without threads (or at least equally
incorrect with and without threads)?
Nested Scopes -
The presence of nested scopes will affect where TRACK_GLOBAL finds
a global variable, but shouldn't affect anything after that. (I
Missing Attributes -
Suppose I am tracking the object referred to by "spam.eggs.ham"
and "spam.eggs" is rebound to an object that does not have a "ham"
attribute. It's clear this will be an AttributeError if the
programmer attempts to resolve "spam.eggs.ham" in the current
Python virtual machine, but suppose the programmer has anticipated
if hasattr(spam.eggs, "ham"):
elif hasattr(spam.eggs, "bacon"):
print "what? no meat?"
You can't raise an AttributeError when the tracking information is
recalculated. If it does not raise AttributeError and instead
lets the tracking stand, it may be setting the programmer up for a
very subtle error.
One solution to this problem would be to track the shortest
possible root of each dotted expression the function refers to
directly. In the above example, "spam.eggs" would be tracked, but
"spam.eggs.ham" and "spam.eggs.bacon" would not.
Who does the dirty work? -
In the Questions section I postulated the existence of a
_PyObject_TrackName function. While the API is fairly easy to
specify, the implementation behind-the-scenes is not so obvious.
A central dictionary could be used to track the name/location
mappings, but it appears that all setattr functions might need to
be modified to accommodate this new functionality.
If all types used the PyObject_GenericSetAttr function to set
attributes that would localize the update code somewhat. They
don't however (which is not too surprising), so it seems that all
getattrfunc and getattrofunc functions will have to be updated.
In addition, this would place an absolute requirement on C
extension module authors to call some function when an attribute
changes value (PyObject_TrackUpdate?).
Finally, it's quite possible that some attributes will be set by
side effect and not by any direct call to a setattr method of some
sort. Consider a device interface module that has an interrupt
routine that copies the contents of a device register into a slot
in the object's struct whenever it changes. In these situations,
more extensive modifications would have to be made by the module
author. To identify such situations at compile time would be
impossible. I think an extra slot could be added to PyTypeObjects
to indicate if an object's code is safe for global tracking. It
would have a default value of 0 (Py_TRACKING_NOT_SAFE). If an
extension module author has implemented the necessary tracking
support, that field could be initialized to 1 (Py_TRACKING_SAFE).
_PyObject_TrackName could check that field and issue a warning if
it is asked to track an object that the author has not explicitly
said was safe for tracking.
Jeremy Hylton has an alternate proposal on the table . His
proposal seeks to create a hybrid dictionary/list object for use
in global name lookups that would make global variable access look
more like local variable access. While there is no C code
available to examine, the Python implementation given in his
proposal still appears to require dictionary key lookup. It
doesn't appear that his proposal could speed local variable
attribute lookup, which might be worthwhile in some situations if
potential performance burdens could be addressed.
I don't believe there will be any serious issues of backward
compatibility. Obviously, Python bytecode that contains
TRACK_OBJECT opcodes could not be executed by earlier versions of
the interpreter, but breakage at the bytecode level is often
assumed between versions.
TBD. This is where I need help. I believe there should be either
a central name/location registry or the code that modifies object
attributes should be modified, but I'm not sure the best way to go
about this. If you look at the code that implements the
STORE_GLOBAL and STORE_ATTR opcodes, it seems likely that some
changes will be required to PyDict_SetItem and PyObject_SetAttr or
their String variants. Ideally, there'd be a fairly central place
to localize these changes. If you begin considering tracking
attributes of local variables you get into issues of modifying
STORE_FAST as well, which could be a problem, since the name
bindings for local variables are changed much more frequently. (I
think an optimizer could avoid inserting the tracking code for the
attributes for any local variables where the variable's name
I believe (though I have no code to prove it at this point), that
implementing TRACK_OBJECT will generally not be much more
expensive than a single LOAD_GLOBAL instruction or a
LOAD_GLOBAL/LOAD_ATTR pair. An optimizer should be able to avoid
converting LOAD_GLOBAL and LOAD_GLOBAL/LOAD_ATTR to the new scheme
unless the object access occurred within a loop. Further down the
line, a register-oriented replacement for the current Python
virtual machine  could conceivably eliminate most of the
LOAD_FAST instructions as well.
The number of tracked objects should be relatively small. All
active frames of all active threads could conceivably be tracking
objects, but this seems small compared to the number of functions
defined in a given application.
This document has been placed in the public domain.