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@ -4,166 +4,184 @@ Version: $Revision$
Author: gmcm@hypernet.com (Gordon McMillan)
Status: Draft
Type: Standards Track
Python-Version: 2.1 Created: 14-Aug-2000
Created: 14-Aug-2000
Python-Version: 2.1
Post-History:
Introduction
This PEP discusses changes required to core Python in order to
efficiently support generators, microthreads and coroutines. It
is related to PEP 220, which describes how Python should be extended
efficiently support generators, microthreads and coroutines. It is
related to PEP 220, which describes how Python should be extended
to support these facilities. The focus of this PEP is strictly on
the changes required to allow these extensions to work.
While these PEPs are based on Christian Tismer's Stackless[1]
implementation, they do not regard Stackless as a reference
implementation. Stackless (with an extension module) implements
continuations, and from continuations one can implement coroutines,
microthreads (as has been done by Will Ware[2]) and generators. But
in more that a year, no one has found any other productive use of
continuations, so there seems to be no demand for their support.
While these PEPs are based on Christian Tismer's Stackless[1]
implementation, they do not regard Stackless as a reference
implementation. Stackless (with an extension module) implements
continuations, and from continuations one can implement
coroutines, microthreads (as has been done by Will Ware[2]) and
generators. But in more that a year, no one has found any other
productive use of continuations, so there seems to be no demand
for their support.
However, Stackless support for continuations is a relatively minor
piece of the implementation, so one might regard it as "a" reference
implementation (rather than "the" reference implementation).
piece of the implementation, so one might regard it as "a"
reference implementation (rather than "the" reference
implementation).
Background
Generators and coroutines have been implmented in a number of languages in
a number of ways. Indeed, Tim Peters has done pure Python implementations
of generators[3] and coroutines[4] using threads (and a thread-based
coroutine implementation exists for Java). However, the horrendous
overhead of a thread-based implementation severely limits the usefulness
of this approach.
Generators and coroutines have been implemented in a number of
languages in a number of ways. Indeed, Tim Peters has done pure
Python implementations of generators[3] and coroutines[4] using
threads (and a thread-based coroutine implementation exists for
Java). However, the horrendous overhead of a thread-based
implementation severely limits the usefulness of this approach.
Microthreads (a.k.a "green" or "user" threads) and coroutines involve
transfers of control that are difficult to accomodate in a language
implementation based on a single stack. (Generators can be done on a
single stack, but they can also be regarded as a very simple case of
coroutines.)
Microthreads (a.k.a "green" or "user" threads) and coroutines
involve transfers of control that are difficult to accommodate in
a language implementation based on a single stack. (Generators can
be done on a single stack, but they can also be regarded as a very
simple case of coroutines.)
Real threads allocate a full-sized stack for each thread of control, and
this is the major source of overhead. However, coroutines and microthreads
can be implemented in Python in a way that involves almost no overhead.
This PEP, therefor, offers a way for making Python able to realistically
manage thousands of separate "threads" of activity (vs. todays limit of
perhaps dozens of separate threads of activity).
Real threads allocate a full-sized stack for each thread of
control, and this is the major source of overhead. However,
coroutines and microthreads can be implemented in Python in a way
that involves almost no overhead. This PEP, therefor, offers a
way for making Python able to realistically manage thousands of
separate "threads" of activity (vs. todays limit of perhaps dozens
of separate threads of activity).
Another justification for this PEP (explored in PEP 220) is that
coroutines and generators often allow a more direct expression of
an algorithm than is possible in today's Python.
Another justification for this PEP (explored in PEP 220) is that
coroutines and generators often allow a more direct expression of an
algorithm than is possible in today's Python.
Discussion
The first thing to note is that Python, while it mingles interpreter data
(normal C stack usage) with Python data (the state of the interpreted
program) on the stack, the two are logically separate. They just happen to
use the same stack.
The first thing to note is that Python, while it mingles
interpreter data (normal C stack usage) with Python data (the
state of the interpreted program) on the stack, the two are
logically separate. They just happen to use the same stack.
A real thread gets something approaching a process-sized stack because the
implementation has no way of knowing how much stack space the thread will
require. The stack space required for an individual frame is likely to be
reasonable, but stack switching is an arcane and non-portable process,
not supported by C.
A real thread gets something approaching a process-sized stack
because the implementation has no way of knowing how much stack
space the thread will require. The stack space required for an
individual frame is likely to be reasonable, but stack switching
is an arcane and non-portable process, not supported by C.
Once Python stops putting Python data on the C stack, however, stack
switching becomes easy.
Once Python stops putting Python data on the C stack, however,
stack switching becomes easy.
The fundamental approach of the PEP is based on these two ideas. First,
separate C's stack usage from Python's stack usage. Secondly, associate
with each frame enough stack space to handle that frame's execution.
The fundamental approach of the PEP is based on these two
ideas. First, separate C's stack usage from Python's stack
usage. Secondly, associate with each frame enough stack space to
handle that frame's execution.
In the normal usage, Stackless Python has a normal stack
structure, except that it is broken into chunks. But in the
presence of a coroutine / microthread extension, this same
mechanism supports a stack with a tree structure. That is, an
extension can support transfers of control between frames outside
the normal "call / return" path.
In the normal usage, Stackless Python has a normal stack structure,
except that it is broken into chunks. But in the presence of a
coroutine / microthread extension, this same mechanism supports a stack
with a tree structure. That is, an extension can support transfers of
control between frames outside the normal "call / return" path.
Problems
The major difficulty with this approach is C calling Python. The problem
is that the C stack now holds a nested execution of the byte-code
interpreter. In that situation, a coroutine / microthread extension cannot
be permitted to transfer control to a frame in a different invocation of the
byte-code interpreter. If a frame were to complete and exit back to C from
the wrong interpreter, the C stack could be trashed.
The major difficulty with this approach is C calling Python. The
problem is that the C stack now holds a nested execution of the
byte-code interpreter. In that situation, a coroutine /
microthread extension cannot be permitted to transfer control to a
frame in a different invocation of the byte-code interpreter. If a
frame were to complete and exit back to C from the wrong
interpreter, the C stack could be trashed.
The ideal solution is to create a mechanism where nested executions of the
byte code interpreter are never needed. The easy solution is for the
coroutine / microthread extension(s) to recognize the situation and refuse
to allow transfers outside the current invocation.
The ideal solution is to create a mechanism where nested
executions of the byte code interpreter are never needed. The easy
solution is for the coroutine / microthread extension(s) to
recognize the situation and refuse to allow transfers outside the
current invocation.
We can categorize code that involves C calling Python into two camps:
Python's implementation, and C extensions. And hopefully we can offer a
compromise: Python's internal usage (and C extension writers who want to
go to the effort) will no longer use a nested invocation of the
interpreter. Extensions which do not go to the effort will still be
safe, but will not play well with coroutines / microthreads.
We can categorize code that involves C calling Python into two
camps: Python's implementation, and C extensions. And hopefully we
can offer a compromise: Python's internal usage (and C extension
writers who want to go to the effort) will no longer use a nested
invocation of the interpreter. Extensions which do not go to the
effort will still be safe, but will not play well with coroutines
/ microthreads.
Generally, when a recursive call is transformed into a loop, a bit of
extra bookkeeping is required. The loop will need to keep it's own
"stack" of arguments and results since the real stack can now only hold
the most recent. The code will be more verbose, because it's not quite
as obvious when we're done. While Stackless is not implemented this way,
it has to deal with the same issues.
Generally, when a recursive call is transformed into a loop, a bit
of extra bookkeeping is required. The loop will need to keep it's
own "stack" of arguments and results since the real stack can now
only hold the most recent. The code will be more verbose, because
it's not quite as obvious when we're done. While Stackless is not
implemented this way, it has to deal with the same issues.
In normal Python, PyEval_EvalCode is used to build a frame and execute
it. Stackless Python introduces the concept of a FrameDispatcher. Like
PyEval_EvalCode, it executes one frame. But the interpreter may signal
the FrameDispatcher that a new frame has been swapped in, and the new
frame should be executed. When a frame completes, the FrameDispatcher
follows the back pointer to resume the "calling" frame.
In normal Python, PyEval_EvalCode is used to build a frame and
execute it. Stackless Python introduces the concept of a
FrameDispatcher. Like PyEval_EvalCode, it executes one frame. But
the interpreter may signal the FrameDispatcher that a new frame
has been swapped in, and the new frame should be executed. When a
frame completes, the FrameDispatcher follows the back pointer to
resume the "calling" frame.
So Stackless transforms recursions into a loop, but it is not the
FrameDispatcher that manages the frames. This is done by the interpreter
(or an extension that knows what it's doing).
So Stackless transforms recursions into a loop, but it is not the
FrameDispatcher that manages the frames. This is done by the
interpreter (or an extension that knows what it's doing).
The general idea is that where C code needs to execute Python code, it
creates a frame for the Python code, setting its back pointer to the
current frame. Then it swaps in the frame, signals the FrameDispatcher
and gets out of the way. The C stack is now clean - the Python code can
transfer control to any other frame (if an extension gives it the means
to do so).
The general idea is that where C code needs to execute Python
code, it creates a frame for the Python code, setting its back
pointer to the current frame. Then it swaps in the frame, signals
the FrameDispatcher and gets out of the way. The C stack is now
clean - the Python code can transfer control to any other frame
(if an extension gives it the means to do so).
In the vanilla case, this magic can be hidden from the programmer (even,
in most cases, from the Python-internals programmer). Many situations
present another level of difficulty, however.
In the vanilla case, this magic can be hidden from the programmer
(even, in most cases, from the Python-internals programmer). Many
situations present another level of difficulty, however.
The map builtin function involves two obstacles to this approach. It
cannot simply construct a frame and get out of the way, not just because
there's a loop involved, but each pass through the loop requires some
"post" processing. In order to play well with others, Stackless
constructs a frame object for map itself.
The map builtin function involves two obstacles to this
approach. It cannot simply construct a frame and get out of the
way, not just because there's a loop involved, but each pass
through the loop requires some "post" processing. In order to play
well with others, Stackless constructs a frame object for map
itself.
Most recursions of the interpreter are not this complex, but fairly
frequently, some "post" operations are required. Stackless does not
fix these situations because of amount of code changes required. Instead,
Stackless prohibits transfers out of a nested interpreter. While not
ideal (and sometimes puzzling), this limitation is hardly crippling.
Most recursions of the interpreter are not this complex, but
fairly frequently, some "post" operations are required. Stackless
does not fix these situations because of amount of code changes
required. Instead, Stackless prohibits transfers out of a nested
interpreter. While not ideal (and sometimes puzzling), this
limitation is hardly crippling.
Advantages
For normal Python, the advantage to this approach is that C stack usage
becomes much smaller and more predictable. Unbounded recursion in Python
code becomes a memory error, instead of a stack error (and thus, in
non-Cupertino operating systems, something that can be recovered from).
The price, of course, is the added complexity that comes from transforming
recursions of the byte-code interpreter loop into a higher order loop
(and the attendant bookkeeping involved).
For normal Python, the advantage to this approach is that C stack
usage becomes much smaller and more predictable. Unbounded
recursion in Python code becomes a memory error, instead of a
stack error (and thus, in non-Cupertino operating systems,
something that can be recovered from). The price, of course, is
the added complexity that comes from transforming recursions of
the byte-code interpreter loop into a higher order loop (and the
attendant bookkeeping involved).
The big advantage comes from realizing that the Python stack is
really a tree, and the frame dispatcher can transfer control
freely between leaf nodes of the tree, thus allowing things like
microthreads and coroutines.
The big advantage comes from realizing that the Python stack is really
a tree, and the frame dispatcher can transfer control freely between
leaf nodes of the tree, thus allowing things like microthreads and
coroutines.
References
[1] www.stackless.com
[2] http://world.std.com/~wware/uthread.html
[3] Demo/threads/Generator.py in the source distribution
[4] http://www.stackless.com/coroutines.tim.peters.html
[1] www.stackless.com
[2] http://world.std.com/~wware/uthread.html
[3] Demo/threads/Generator.py in the source distribution
[4] http://www.stackless.com/coroutines.tim.peters.html
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