Might be os-specific, too. Some virtual memory management systems might
special case the zeroing out of memory. Try doing the same thing with a
different value than zero.
On Dec 26, 2016 6:15 AM, "Nicolas P. Rougier" <nicolas.roug...@inria.fr>
wrote:
Thanks for the explanation Sebastian, makes sense.
Nicolas
> On 26 Dec 2016, at 11:48, Sebastian Berg <sebast...@sipsolutions.net>
wrote:
>
> On Mo, 2016-12-26 at 10:34 +0100, Nicolas P. Rougier wrote:
>> Hi all,
>>
>>
>> I'm trying to understand why viewing an array as bytes before
>> clearing makes the whole operation faster.
>> I imagine there is some kind of special treatment for byte arrays but
>> I've no clue.
>>
>
> Sure, if its a 1-byte width type, the code will end up calling
> `memset`. If it is not, it will end up calling a loop with:
>
> while (N > 0) {
> *dst = output;
> *dst += 8; /* or whatever element size/stride is */
> --N;
> }
>
> now why this gives such a difference, I don't really know, but I guess
> it is not too surprising and may depend on other things as well.
>
> - Sebastian
>
>
>>
>> # Native float
>> Z_float = np.ones(1000000, float)
>> Z_int = np.ones(1000000, int)
>>
>> %timeit Z_float[...] = 0
>> 1000 loops, best of 3: 361 µs per loop
>>
>> %timeit Z_int[...] = 0
>> 1000 loops, best of 3: 366 µs per loop
>>
>> %timeit Z_float.view(np.byte)[...] = 0
>> 1000 loops, best of 3: 267 µs per loop
>>
>> %timeit Z_int.view(np.byte)[...] = 0
>> 1000 loops, best of 3: 266 µs per loop
>>
>>
>> Nicolas
>> _______________________________________________
>> 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
_______________________________________________
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
https://mail.scipy.org/mailman/listinfo/numpy-discussion
