2016-05-05 22:10 GMT+02:00 Øystein Schønning-Johansen :
> 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]: 100
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 wrote:
>
>> 2016-05-05 11:38 GMT+02:00 Øystein Schønning-Johansen > >:
>>
>>> 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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