[Numpy-discussion] C-coded dot 1000x faster than numpy?

[Roman Yurchak]

For the first benchmark apparently A.dot(B) with A real and B complex is 
a known issue performance wise https://github.com/numpy/numpy/issues/10468

In general, it might be worth trying different BLAS backends. For 
instance, if you install numpy from conda-forge you should be able to 
switch between OpenBLAS, MKL and BLIS: 
https://conda-forge.org/docs/maintainer/knowledge_base.html#switching-blas-implementation

Roman

On 23/02/2021 19:32, Andrea Gavana wrote:
> Hi,
> 
> On Tue, 23 Feb 2021 at 19.11, Neal Becker <ndbecker2 at gmail.com 
> <mailto:ndbecker2 at gmail.com>> wrote:
> 
>     I have code that performs dot product of a 2D matrix of size (on the
>     order of) [1000,16] with a vector of size [1000].  The matrix is
>     float64 and the vector is complex128.  I was using numpy.dot but it
>     turned out to be a bottleneck.
> 
>     So I coded dot2x1 in c++ (using xtensor-python just for the
>     interface).  No fancy simd was used, unless g++ did it on it's own.
> 
>     On a simple benchmark using timeit I find my hand-coded routine is on
>     the order of 1000x faster than numpy?  Here is the test code:
>     My custom c++ code is dot2x1.  I'm not copying it here because it has
>     some dependencies.  Any idea what is going on?
> 
> 
> 
> I had a similar experience - albeit with an older numpy and Python 2.7, 
> so my comments are easily outdated and irrelevant. This was on Windows 
> 10 64 bit, way more than plenty RAM.
> 
> It took me forever to find out that numpy.dot was the culprit, and I 
> ended up using fortran + f2py. Even with the overhead of having to go 
> through f2py bridge, the fortran dot_product was several times faster.
> 
> Sorry if It doesn’t help much.
> 
> Andrea.
> 
> 
> 
> 
>     import numpy as np
> 
>     from dot2x1 import dot2x1
> 
>     a = np.ones ((1000,16))
>     b = np.array([ 0.80311816+0.80311816j,  0.80311816-0.80311816j,
>             -0.80311816+0.80311816j, -0.80311816-0.80311816j,
>              1.09707981+0.29396165j,  1.09707981-0.29396165j,
>             -1.09707981+0.29396165j, -1.09707981-0.29396165j,
>              0.29396165+1.09707981j,  0.29396165-1.09707981j,
>             -0.29396165+1.09707981j, -0.29396165-1.09707981j,
>              0.25495815+0.25495815j,  0.25495815-0.25495815j,
>             -0.25495815+0.25495815j, -0.25495815-0.25495815j])
> 
>     def F1():
>          d = dot2x1 (a, b)
> 
>     def F2():
>          d = np.dot (a, b)
> 
>     from timeit import timeit
>     print (timeit ('F1()', globals=globals(), number=1000))
>     print (timeit ('F2()', globals=globals(), number=1000))
> 
>     In [13]: 0.013910860987380147 << 1st timeit
>     28.608758996007964  << 2nd timeit
>     -- 
>     Those who don't understand recursion are doomed to repeat it
>     _______________________________________________
>     NumPy-Discussion mailing list
>     NumPy-Discussion at python.org <mailto:NumPy-Discussion at python.org>
>     https://mail.python.org/mailman/listinfo/numpy-discussion
>     <https://mail.python.org/mailman/listinfo/numpy-discussion>
> 
> 
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion at python.org
> https://mail.python.org/mailman/listinfo/numpy-discussion
>