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@gmail.com] Sent: Friday, March 3, 2017 4:41 AM To: Singh, Yashwardhan <yashwardhan.singh@intel.com> Cc: pypy-dev@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@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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