[Numpy-discussion] NEP for faster ufuncs
Mark Wiebe
mwwiebe@gmail....
Tue Dec 21 18:53:55 CST 2010
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!
Here's a link:
https://github.com/m-paradox/numpy/blob/mw_neps/doc/neps/new-iterator-ufunc.rst
I would also love it if someone could try building the code and play around
with it a bit. The github branch is here:
https://github.com/m-paradox/numpy/tree/new_iterator
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'],
[['readonly','nbo_aligned']]*nargs +
[['writeonly','allocate','no_broadcast']],
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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