On 02/02/15 18:56, Richard Plangger wrote:
On 01/31/2015 03:40 PM, Armin Rigo wrote:
ce optimizations utilizing type information."
This doesn't mean the performance of PyPy is perfectly optimal today.
There are certainly things to do and try. One of the major ones (in
terms of work involved) would be to add a method-JIT-like approach
with a quick-and-dirty initial JIT, able to give not-too-bad
performance but without the large warm-up times of our current
meta-tracing JIT. More about this or others in a later e-mail, if
you're interested.
A bientôt,
Armin.
Hi,
Sorry to bother again. I did not get any response yet. The problem is
that I need a better picture about a topic I could work on for my thesis
and I really would like to contribute to pypy. In this week I would like
to decide what I'm aiming for (otherwise things might get shifted).
It would be nice to have the information you mentioned earlier in your
email about the method-JIT-like approach and others!
Best,
Richard
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Just to throw my uneducated opinions into the ring. It would be nice to
have someone study autovectorization and hardware acceleration in a JIT.
There are many possible directions: identifying vectorizable actions via
traces or user-supplied hints, resuse of llvm or gcc's strategies,
creating the proper guards, somehow modelling in costs of memory caching
into the tradeoff of what to parallelize, ...
Matti
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