2016-05-05 11:38 GMT+02:00 Øystein Schønning-Johansen <oyste...@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 > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > -- Francesc Alted
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
