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Numpy Status Update

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Hello Everyone,

I've started to work on NumPyPy since the end of April and here is a short update :

  • I implemented pickling support on ndarrays and dtypes, it will be compatible with numpy's pickling protocol when the "numpypy" module will be renamed to "numpy".
  • I am now working on subarrays.

I would also like to thank everyone who donated and allowed me to work on this.

Cheers,
Romain Guillebert

Comments

Anonymous wrote on 2013-05-12 11:09:

No, thank you! Cannot wait till the day PyPy fully supports NumPy.

Anonymous wrote on 2013-05-13 00:19:

I second the anonymous comment above. The day PyPy fully supports NumPy is the day I switch from CPython.

Paul Jaros wrote on 2013-05-13 08:32:

Aww... Anonymous.

@Romain Guillebert Thank you for the hard work you are putting into it. I will be testing my code with the current release.

Anonymous wrote on 2013-05-13 18:38:

This (and to a lesser extent Python 3 support) is the only thing holding me back from switching to PyPy for all of my python programming. Thank you very much for this fantastic project!

Paul Jaros wrote on 2013-05-14 22:44:

Results from running my own little Benchmark: Labyrinth Generator
Array Size: 77711x711:

C-Code:
4.45 Seconds, ~50M Memory Usage.

Pypy with standard List:
14.5 Seconds, ~750M Memory Usage.

Pypy with Numpypy:
11.0 Seconds, ~78M Memory Usage.

Pretty impressive if you ask me. Older Numpypy where about as fast as the standard List. Also Pypy is approaching C-Performance with bigger steps than I dared hoping for.

CPython Benchmark intentionally left out... it takes ages.

Anonymous wrote on 2013-05-15 14:50:

It's great to see a progress in important libraries support.

Speed is important, but when we get acceptable speed then library support is what we need.

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