[pypy-dev] Re: psyco in Python was: Minimal Python project

Edward K. Ream edream at tds.net
Fri Jan 17 17:34:40 CET 2003

Somehow it seems like Michaels reply got to the mail server before my
original message.  Here it is again.  My apologies if this is redundant:

Hi Michael,

I'm going to copy this to pypy-dev so that other people can see this

> > > > I just don't see how to implement psyco in Python in a production
> > > > package without using the C interpreter as a bootstrap!  And yes, I
> > > > suspect that a C language version of psyco will be faster than psyco
> > > > in Python.

> > > Even after you've run psyco over itself?
> >
> > Yes, maybe even then.  C compilers should do better _on the same
> > algorithm_ on hand-written code than can any GIT.

> Weeeeellll, this is the thing that gets me jumping-up-and-down-excited
> about things like psyco: pysco has MORE information than the C compiler to
> work with: while the C compiler only knows the algorithm to run, psyco
> knows both the algorithm *and the data it is to run on*.  Obviously, this
> approach only wins if what you do next resembles what you did last, but I
> think CPU caches have proved there is some mileage in that idea :)

I don't think CPU caches have much bearing on this discussion.  Caches speed
up frequently used values.  The problems here are not the same.

The more I study psyco, the more I am convinced that there is _no way_ that
psyco will ever produce better code than a C compiler.  In brief, my reasons
are as follows:

1. psyco has, in fact, no better information than does a C compiler.
Remember that the "values" that psyco specializes upon are in fact
_interpreter_ vars, so knowing those values is equivalent to knowing the
types of operands.  This does indeed allow psyco to emit machine code that
is better than the equivalent C interp generic code.  In fact, though, most
of what psyco is doing is quite routine: replacing bytecode by assembly
language.  This will speed up the code a _lot_, but it is doing just like a
C compiler does.

2. I have looked again at the code generators of the optimizing C compiler I
wrote about 10 years ago.  I can find no instance where knowing the value
of an operand, rather than just its type, would produce better code.  Yes,
my compiler did constant folding (which eliminates code rather than improves
it), but I do not believe doing constant folding at runtime is going to
improve matters much, and is in fact very likely to slow down the running
times of typical programs, for reasons given below.

3. It's easy to get seduced by the "psyco has more knowledge" mantra.  In
fact, one mustn't lose track of the obstacles psyco faces in outperforming C
compilers.  First, and very important, everything that psyco does must be
done at run time.  This means that even very effective optimizations like
peephole optimizations become dubious for psyco.  Second, and also very
important, is that fact that psyco (or rather the code that psyco produces)
must determine whether the code that psyco has already produced is suitable
for the task at hand.

The "Structure of Psyco" paper says this:  "Dispatching is the set of
techniques used to store a trace of the compiled code buffers and _find if
one already matches the current compiler state_" (emphasis mine)  In other
words, it's not enough to specialize code!  Psyco must determine whether the
types used to compile a function match the types presently given to psyco.
This searching is done in the "compatible" routine, and no matter how it is
done is _pure overhead_ compared to the code generated by a C compiler.

As I see it, both these issues are "gotchas".  Neither can be eliminated
completely, and each introduces overhead that will make the code emitted by
psyco clearly inferior to the code produced by any decent C compiler. In
particular, the more psyco specializes on runtime values of operands the
bigger the code bloat and the more severe the problem of finding out what
already-compiled code to use.

4. I have seen _nothing_ in psyco's algorithms or the code emitted by psyco
that leads me to believe that psyco is "doing enough work" to outperform a C

When I was a mathematics graduate student 30 years ago one of my professors
made the remark that a mathematician need to develop a feel for the level of
work required to do a new proof.  Deep insights are not going to come from
elementary methods, and there is no use in using deep methods to prove
elementary results.

I believe the same kind of analysis can (and should!) be done here.  Yes,
psyco is clever.  In some ways it is _very_ clever, especially the idea of
modeling the compiler on the structure on the original C interp.  Yes, it
is easy to seen how psyco can improve the running speed of the present C
interpreter: _any_ compiling will do so!  But with respect, I dispute the
assertion that knowing actual values of operands is going to produce faster
code that that produce by a decent C compiler applied to an _equivalent_
algorithm done in C.

Please note: it is _precisely_ this extremely strong assertion that must be
true for there to be any speedup gained in translating Python's C libraries
into Python!  We already have C libraries that work very well.  I would be
_very_ leery of messing with them...

5. Another way to see that psyco isn't really doing much is the following.
Basically, psyco emits calls to the same runtime helpers that the C
interpreter does.  Yes, psyco may improve the calling sequences to these
helpers, but it does not eliminate calls to those helpers.  But a C compiler
will never do worse that the code in the helpers, and will often do _much_

I challenge this group to provide even one example of Python code, for which
psyco will emit better code than that produced by a C compiler on a
transliteration of that code into C.  I believe no such counter-example will
ever be found.

I am not much interested in anecdotal reports of Python being faster than C,
and I would be happy to study in depth any purported counterexample. If you
believe you have a real counterexample, please first make sure that the
algorithms appear identical.  Second, please try to make the counterexample
as small as possible.

If I am wrong, the counter-example should provide a _clear reason_ why all
my arguments above are wrong.  And wouldn't that be great :-) I believe the
arguments given should be given greater weight than the "psyco has more
knowledge" mantra. You want to convince me otherwise?  Show me the code, or
provide a _detailed_ explanation for how my arguments do not apply.


P.S.  I trust that this group will interpret my remarks as constructive.  I
believe it is important at the beginning of any project to have proper goals
and expectations.  Make no mistake: I believe this project has the potential
to be extremely valuable, even if my arguments are correct.  And I would
indeed be happy to be _proved_ wrong.  However, I think it a dubious policy
to assume that Python can outperform C, for several reasons:

1. There is no need to burden this project with unrealistic expectations.
Python doesn't have to beat C for Python to rule the world! :-)

2. I am concerned that assuming that we can do the impossible will skew our
efforts.  Yes, by all means, experiment using Python!  Using C to do
experiments would not be too swift :-)  But lets not spend a lot of time
worrying about bootstrapping until we are _sure_ that we won't be doing
the final version in C!

3. There may be another way to speed up Python, namely replace (parts of)
the byte code with compiled code.  Armin mentioned in his papers that some
ways of doing so might produce code bloat.  But is that the end of the
story?  I think not.  The same tricks that psyco uses in the "dispatcher"
might well be applied to intermix machine code with byte code.  I wouldn't
want to focus _only_ on the "psyco way (tm)" in this project.  Compile-time
optimizations deserve at least some considerations, IMO.

Edward K. Ream   email:  edream at tds.net
Leo: Literate Editor with Outlines
Leo: http://personalpages.tds.net/~edream/front.html

More information about the Pypy-dev mailing list