[pypy-dev] Using RPython for a semi-statically-typed language

Ryan rymg19 at gmail.com
Sun Oct 27 22:12:52 CET 2013


Thank you!! That will probably help a lot. Even in those special cases, since all types will derive from a common base, it might still show a very slight speed boost.

Alex Gaynor <alex.gaynor at gmail.com> wrote:
>Hi Ryan,
>
>I'm not sure this completely answers your question, but the JIT does
>let
>you tell it when a class is statically known:
>https://bitbucket.org/pypy/pypy/src/061674dab6430beb1645f43449fc114a09d58834/rpython/rlib/jit.py?at=default#cl-979so
>this may help you.
>
>In general RPython (and the JIT), are designed for dynamically typed
>languages, but "semi-statically" may be the same as dynamic for all
>intents
>and purposes (I'm not sure).
>
>Alex
>
>
>On Sat, Oct 26, 2013 at 4:58 PM, Ryan Gonzalez <rymg19 at gmail.com>
>wrote:
>
>> Hello,
>>
>> I am in need of a little assistance. I am thinking of writing a
>language
>> in RPython. Now, here is what I want:
>>
>> -Polymorphism(C++) without pointers, i.e., Derived can be implicitly
>cast
>> to Base
>>
>> I am thinking of picking RPython because of the JIT. However, I am
>worried
>> about the speed. I know the JIT PyPy generates is fast, but if it's
>the
>> same speed as PyPy itself, someone would easily pick PyPy or Topaz or
>the
>> like.
>>
>> My question is: For a somewhat statically-typed language like I am
>> planning, are there any extra optimizations I could add to make the
>JIT
>> work faster? Since the variables have types, I was thinking there
>might be
>> a way to make it work faster.
>>
>> Thanks in advance!
>>
>> --
>> Ryan
>>
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>>
>
>
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