[pypy-dev] PyPy improving generated machine code

[Matti Picus]

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