[Python-checkins] CVS: python/nondist/peps pep-0266.txt,NONE,1.1

[Barry Warsaw]

Update of /cvsroot/python/python/nondist/peps
In directory usw-pr-cvs1:/tmp/cvs-serv21824

Added Files:
	pep-0266.txt 
Log Message:
PEP 266, Optimizing Global Variable/Attribute Access, Skip Montanaro

Minor editorial pass, spell checking, formatting.  I also had to
shorten the title, hope Skip doesn't mind!


--- NEW FILE: pep-0266.txt ---
PEP: 266
Title: Optimizing Global Variable/Attribute Access
Version: $Revision: 1.1 $
Author: skip@pobox.com (Skip Montanaro)
Status: Draft
Type: Standards Track
Python-Version: 2.3
Created: 13-Aug-2001
Post-History:


Abstract

    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.


Introduction

    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.


Proposed Change

    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.


Rationale

    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:

        LOAD_GLOBAL     math
        LOAD_ATTR       sin

    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:

        TRACK_OBJECT       math.sin
        ...
        LOAD_FAST          math.sin
        ...
        UNTRACK_OBJECT     math.sin

    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):
            l.append(math.sin(i))
        return l

    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.

    According to a post to python-dev by Marc-Andre Lemburg [1],
    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 LOAD_FAST instructions.

    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.


Discussion

    Jeremy Hylton has an alternate proposal on the table [2].  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.


Backwards Compatibility

    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.


Implementation

    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
    binding changes.)


Performance

    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 [3] 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.


References

    [1] http://mail.python.org/pipermail/python-dev/2000-July/007609.html

    [2] http://www.zope.org/Members/jeremy/CurrentAndFutureProjects/FastGlobalsPEP

    [3] http://www.musi-cal.com/~skip/python/rattlesnake20010813.tar.gz


Copyright

    This document has been placed in the public domain.



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