2015-03-08 21:47 GMT+01:00 Dp Docs <sdpan21@gmail.com>:
Hi all,
I am a CS 3rd Undergrad. Student from an Indian Institute (III T). I
believe I am good in Programming languages like C/C++, Python as I
have already done Some Projects using these language as a part of my
academics. I really like Coding (Competitive as well as development).
I really want to get involved in Numpy Development Project and want to
take  "Vector math library integration" as a part of my project. I
want to here any idea from your side for this project.
Thanks For your time for reading this email and responding back.

As Sturla and Gregor suggested, there are quite a few attempts to solve this shortcoming in NumPy.  In particular Gregor integrated MKL/VML support in numexpr quite a long time ago, and when combined with my own implementation of pooled threads (behaving better than Intel's implementation in VML), then the thing literally flies:

 https://github.com/pydata/numexpr/wiki/NumexprMKL

numba is also another interesting option and it shows much better compiling times than the integrated compiler in numexpr.  You can see a quick comparison about expected performances between numexpr and numba:

http://nbviewer.ipython.org/gist/anonymous/4117896

In general, numba wins for small arrays, but numexpr can achieve very good performance for larger ones.  I think there are interesting things to discover in both projects, as for example, how they manage memory in order to avoid temporaries or how they deal with unaligned data efficiently.  I would advise to look at existing docs and presentations explaining things in more detail too.

All in all, I would really love to see such a vector math library support integrated in NumPy because frankly, I don't have bandwidth for maintaining numexpr anymore (and I am afraid that nobody else would jump in this ship ;).

Good luck!

Francesc
 

My IRCnickname: dp

Real Name: Durgesh Pandey.
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--
Francesc Alted