Accelerating Python Libraries with Numba (Part 2)

Re:Accelerating Python Libraries with Numba (Part 2) by Demiurge on Thursday May 23, 2013 @01:09PM (#5757982)

I would have liked to see PyPy test for the sum function in the comparison.

Update:

Well this is interesting ;D

time python test.py

real 0m11.169s

user 0m11.141s

sys 0m0.022s

time pypy test.py

real 0m0.259s

user 0m0.239s

sys 0m0.018s

cat test.py def python_sum(y): N = len(y) x = y[0] for i in xrange(1,N): x += y[i] return x

python_sum(xrange(100000000))

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Re:Accelerating Python Libraries with Numba (Part 2) by onalark on Thursday May 23, 2013 @05:00PM (#5759509)

Hi. I was reluctant to publish any results with PyPy because I am not an expert in working with their code base. If you'd like to look at the GrowCut benchmark being run on my Mac (Intel iCore 7 2.7 GHz) on PyPy vs. Anaconda Python (or even better, download and run it yourself), the gist is [here](https://gist.github.com/ahmadia/5638980).

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Re:Accelerating Python Libraries with Numba (Part 2) by freyrs3 on Thursday May 23, 2013 @03:29PM (#5758943)

Yes, it is well known that allocating 100000000 PyObjects is slow.

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Re:Accelerating Python Libraries with Numba (Part 2) by paddy_m on Thursday May 23, 2013 @01:31PM (#5758105)

To be clear, you are comparing pure pypy vs pure python?

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Re:Accelerating Python Libraries with Numba (Part 2) by Demiurge on Thursday May 23, 2013 @01:34PM (#5758130)

yes

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Re:Accelerating Python Libraries with Numba (Part 2) by Demiurge on Thursday May 23, 2013 @04:53PM (#5759471)

i'm curious why anyone would downmod my answer

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Re:Accelerating Python Libraries with Numba (Part 2) by meepmorp on Thursday May 23, 2013 @10:37PM (#5760965)

Possibly because the down and up vote buttons are so close together, it's easy to hit the wrong one if you're using a touchscreen device.

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