[pypy-dev] Numpy on PyPy : cpyext

[Singh, Yashwardhan]

Hi Maciej,

I have applied for clearance to publicly upload the code. I will upload it once I get the permission.

Regards
Yash

-----Original Message-----
From: Maciej Fijalkowski [mailto:fijall at gmail.com] 
Sent: Friday, March 3, 2017 4:41 AM
To: Singh, Yashwardhan <yashwardhan.singh at intel.com>
Cc: pypy-dev at python.org
Subject: Re: [pypy-dev] Numpy on PyPy : cpyext

Hi Yash

Is your software open source? I'm happy to check it out for you

I think the c-level profiling for vmprof is relatively new, you would need to use pypy nightly in order to get that level of insight.
Additionally, we're working on cpyext improvements *right now* stay tuned.

If there is a good case for speeding up numpy, we can get it a lot faster than it is right now and seek some funding for that. Neural networks might be one of those!

Best regards,
Maciej Fijalkowski

On Fri, Mar 3, 2017 at 2:31 AM, Singh, Yashwardhan <yashwardhan.singh at intel.com> wrote:
> Hi Everyone,
>
> I am using numpy on pypy to train a deep neural network. For my 
> workload numpy on pypy is taking twice the time to train as numpy on 
> Cpython. I am using Numpy via cpyext.
>
> I read in the documentation, "Performance-wise, the speed is mostly 
> the same as CPython's NumPy (it is the same code); the exception is 
> that interactions between the Python side and NumPy objects are 
> mediated through the slower cpyext layer (which hurts a few benchmarks 
> that do a lot of element-by-element array accesses, for example)." Is 
> there any way in which I can profile my application to see how much 
> additional overhead cypext layer is adding or is it the numpy via pypy 
> which is slowing down the things. I have tried vmprof, but I couldn't 
> figure out from it how much time cpyext layer is taking.
>
> Any help will be highly appreciated.
>
> Regards
> Yash
>
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