[pypy-dev] Pypy custom interpreter JIT question

Andrew Brown brownan at gmail.com
Mon Mar 28 19:21:01 CEST 2011

I tried that logging option once, but I didn't know how to read the logs.
They're not exactly self explanatory. Is there a resource somewhere that
explains how to read those logs?

Regardless, I've implemented your suggestion and moved reads from that
dictionary to a function decorated with @purefunction. Indeed, performance
is greatly improved! Thanks!

Current version:

A few questions:

When the optimizer encounters a "pure" function, it must compare the objects
passed in to previous invocations... does it consider the contents of
container or other mutatible objects? or just the object identity, to be
part of the function's input?
It looks like, from logs of my new version, it's not reading from the
dictionary at all during the trace, so I would guess it's not considering
the actual contents of the dictionary as part of the function's input. This
isn't surprising, but I just want to know for sure.

Second, I noticed in jit.py the function hint() which has a parameter:
"promote - promote the argument from a variable into a constant". Could this
be an appropriate alternate to the @purefunction solution? Or, I'm guessing,
does it just mean the name bracket_map won't change bindings, but does not
impose a restriction on mutating the dictionary?


On Fri, Mar 25, 2011 at 2:18 PM, Armin Rigo <arigo at tunes.org> wrote:

> Hi Andrew,
> On Fri, Mar 25, 2011 at 5:47 PM, Andrew Brown <brownan at gmail.com> wrote:
> > Thanks, it does indeed work now!
> The next step is to have a look at the traces produced (run with
> PYPYLOG=jit-log-opt:logfile), and spot the obvious missing
> optimizations.  The biggest issue seems to be the fact that the
> dictionary 'bracket_map' is green, but it is not enough to ensure that
> it is a constant dict (it could be mutated behind the JIT's back); so
> in the end, every trace contains reads from it.  You could fix it by
> moving the line
>            newpc = bracket_map[pc]
> to a new function to which you apply the decorator
> @pypy.rlib.jit.pure_function.
> A bientôt,
> Armin.
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