[Numpy-discussion] Calling C code that assumes SIMD aligned data.

[Francesc Alted]

2016-05-05 11:38 GMT+02:00 Øystein Schønning-Johansen <oysteijo at gmail.com>:

> Hi!
>
> I've written a little code of numpy code that does a neural network
> feedforward calculation:
>
>     def feedforward(self,x):
>         for activation, w, b in zip( self.activations, self.weights,
> self.biases ):
>             x = activation( np.dot(w, x) + b)
>
> This works fine when my activation functions are in Python, however I've
> wrapped the activation functions from a C implementation that requires the
> array to be memory aligned. (due to simd instructions in the C
> implementation.) So I need the operation np.dot( w, x) + b to return a
> ndarray where the data pointer is aligned. How can I do that? Is it
> possible at all?
>

Yes.  np.dot() does accept an `out` parameter where you can pass your
aligned array.  The way for testing if numpy is returning you an aligned
array is easy:

In [15]: x = np.arange(6).reshape(2,3)

In [16]: x.ctypes.data % 16
Out[16]: 0

but:

In [17]: x.ctypes.data % 32
Out[17]: 16

so, in this case NumPy returned a 16-byte aligned array which should be
enough for 128 bit SIMD (SSE family).  This kind of alignment is pretty
common in modern computers.  If you need 256 bit (32-byte) alignment then
you will need to build your container manually.  See here for an example:
http://stackoverflow.com/questions/9895787/memory-alignment-for-fast-fft-in-python-using-shared-arrrays

Francesc


>
> (BTW: the function works  correctly about 20% of the time I run it, and
> else it segfaults on the simd instruction in the the C function)
>
> Thanks,
> -Øystein
>
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>


-- 
Francesc Alted
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