# [Numpy-discussion] NEP for faster ufuncs

John Salvatier jsalvati@u.washington....
Tue Dec 21 18:59:15 CST 2010

```That is an amazing christmas present.

On Tue, Dec 21, 2010 at 4:53 PM, Mark Wiebe <mwwiebe@gmail.com> wrote:

> Hello NumPy-ers,
>
> After some performance analysis, I've designed and implemented a new
> iterator designed to speed up ufuncs and allow for easier multi-dimensional
> iteration.  The new code is fairly large, but works quite well already.  If
> some people could read the NEP and give some feedback, that would be great!
>
>
>
> I would also love it if someone could try building the code and play around
> with it a bit.  The github branch is here:
>
>
> To give a taste of the iterator's functionality, below is an example from
> the NEP for how to implement a "Lambda UFunc."  With just a few lines of
> code, it's possible to replicate something similar to the numexpr library
> (numexpr still gets a bigger speedup, though).  In the example expression I
> chose, execution time went from 138ms to 61ms.
>
> Hopefully this is a good Christmas present for NumPy. :)
>
> Cheers,
> Mark
>
> Here is the definition of the ``luf`` function.::
>
>     def luf(lamdaexpr, *args, **kwargs):
>         """Lambda UFunc
>
>             e.g.
>             c = luf(lambda i,j:i+j, a, b, order='K',
>                                 casting='safe', buffersize=8192)
>
>             c = np.empty(...)
>             luf(lambda i,j:i+j, a, b, out=c, order='K',
>                                 casting='safe', buffersize=8192)
>         """
>
>         nargs = len(args)
>         op = args + (kwargs.get('out',None),)
>         it = np.newiter(op, ['buffered','no_inner_iteration'],
>                 order=kwargs.get('order','K'),
>                 casting=kwargs.get('casting','safe'),
>                 buffersize=kwargs.get('buffersize',0))
>         while not it.finished:
>             it[-1] = lamdaexpr(*it[:-1])
>             it.iternext()
>
>         return it.operands[-1]
>
> Then, by using ``luf`` instead of straight Python expressions, we
> can gain some performance from better cache behavior.::
>
>     In [2]: a = np.random.random((50,50,50,10))
>     In [3]: b = np.random.random((50,50,1,10))
>     In [4]: c = np.random.random((50,50,50,1))
>
>     In [5]: timeit 3*a+b-(a/c)
>     1 loops, best of 3: 138 ms per loop
>
>     In [6]: timeit luf(lambda a,b,c:3*a+b-(a/c), a, b, c)
>     10 loops, best of 3: 60.9 ms per loop
>
>     In [7]: np.all(3*a+b-(a/c) == luf(lambda a,b,c:3*a+b-(a/c), a, b, c))
>     Out[7]: True
>
>
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>
>
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