Here's an obligatory plug for the two following PRs: https://github.com/numpy/numpy/pull/5457 https://github.com/numpy/numpy/pull/5470
Regards Antoine. On Fri, 6 May 2016 15:01:32 +0200 Francesc Alted <fal...@gmail.com> wrote: > 2016-05-05 22:10 GMT+02:00 Øystein Schønning-Johansen <oyste...@gmail.com>: > > > Thanks for your answer, Francesc. Knowing that there is no numpy solution > > saves the work of searching for this. I've not tried the solution described > > at SO, but it looks like a real performance killer. I'll rather try to > > override malloc with glibs malloc_hooks or LD_PRELOAD tricks. Do you think > > that will do it? I'll try it and report back. > > > > I don't think you need that much weaponry. Just create an array with some > spare space for alignment. Realize that you want a 64-byte aligned double > precision array. With that, create your desired array + 64 additional > bytes (8 doubles): > > In [92]: a = np.zeros(int(1e6) + 8) > > In [93]: a.ctypes.data % 64 > Out[93]: 16 > > and compute the elements to shift this: > > In [94]: shift = (64 / a.itemsize) - (a.ctypes.data % 64) / a.itemsize > > In [95]: shift > Out[95]: 6 > > now, create a view with the required elements less: > > In [98]: b = a[shift:-((64 / a.itemsize)-shift)] > > In [99]: len(b) > Out[99]: 1000000 > > In [100]: b.ctypes.data % 64 > Out[100]: 0 > > and voila, b is now aligned to 64 bytes. As the view is a copy-free > operation, this is fast, and you only wasted 64 bytes. Pretty cheap indeed. > > Francesc > > > > > > Thanks, > > -Øystein > > > > On Thu, May 5, 2016 at 1:55 PM, Francesc Alted <fal...@gmail.com> wrote: > > > >> 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 > >> > >> > > > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@scipy.org > > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > > > > > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
