[pypy-dev] An idea about automatic parallelization in PyPy/RPython

[黄若尘]

Hi Fijaklowski,

Thank you very much for your reply.

Yes, you are right, it’s too hard for me to implement automatic parallelization for the whole PyPy’s trace JIT. I think maybe I can firstly do some work with a very simple interpreter (for example the example-interpreter introduced by PyPy documentation), and try to change some behaviors of RPython JIT.

By the way, could you tell me how can I get the traces and handle them before compiled to native code? I just want to try to convert some of the traces to OpenCL kernel codes and run them in other devices like GPU.

Best Regards,
Huang Ruochen

> 在 2014年11月21日，上午12:05，Maciej Fijalkowski <fijall at gmail.com> 写道：
> 
> Hi 黄若尘
> 
> This is generally a hard problem that projects like GCC or LLVM didn't
> get very far. The problem is slightly more advanced with PyPys JIT,
> but not much more.
> 
> However, the problem is you can do it for simple loops, but the
> applications are limited outside of pure numerics (e.g. numpy) and
> also doing SSE stuff in such cases first seems like both a good
> starting point and a small enough project for master thesis.
> 
> Cheers,
> fijal
> 
> On Tue, Nov 18, 2014 at 3:46 AM, 黄若尘 <hrc706 at gmail.com> wrote:
>> Hi everyone,
>> 
>>   I’m a master student in Japan and I want to do some research in PyPy/RPython.
>>   I have read some papers about PyPy and I also had some ideas about it.  I have communicated with Mr. Bloz and been advised to send my question here.
>> 
>>   Actually, I wonder if it is possible to make an automatic parallelization for the trace generated by JIT, that is, check if the hot loop is a parallel loop, if so, then try to run the trace parallel in multi-core CPU or GPU, make it faster.
>>   I think it maybe suitable because:
>>   1. The traced-base JIT is targeting on loops, which is straight to parallel computation.
>>   2. There is no control-flow in trace, which is suitable to the fragment program in GPU.
>>   3. We may use the hint of @elidable in interpreter codes, since the elidable functions are nonsensitive in the execution ordering so can be executed parallel.
>> 
>>   What do you think about it?
>> 
>> Best Regards,
>> Huang Ruochen
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