On Wed, Dec 30, 2015 at 6:34 AM, Nicolas P. Rougier <Nicolas.Rougier@inria.fr> wrote:

> On 28 Dec 2015, at 19:58, Chris Barker <chris.barker@noaa.gov> wrote:
>
> >>> python benchmark.py
> Python list, append 100000 items: 0.01161
> Array list, append 100000 items: 0.46854
>
> are you pre-allocating any extra space? if not -- it's going to be really, really pokey when adding a little bit at a time.


Yes, I’m preallocating but it might not be optimal at all given your implementation is much faster.
I’ll try to adapt your code. Thanks.

sounds good -- I'll try to take a look at yours soon - maybe we can merge the projects. MIne is only operational in one small place, I think.

-CHB




 

>
> With my Accumulator class:
>
> https://github.com/PythonCHB/NumpyExtras/blob/master/numpy_extras/accumulator.py
>
> I pre-allocate a larger numpy array to start, and it gets re-allocated, with some extra, when filled, using ndarray.resize()
>
> this is quite fast.
>
> These are settable parameters in the class:
>
> DEFAULT_BUFFER_SIZE = 128 # original buffer created.
> BUFFER_EXTEND_SIZE = 1.25 # array.array uses 1+1/16 -- that seems small to me.
>
>
> I looked at the code in array.array (and list, I think), and it does stuff to optimize very small arrays, which I figured wasn't the use-case here :-)
>
> But I did a bunch of experimentation, and as long as you pre-allocate _some_ it doesn't make much difference how much :-)
>
> BTW,
>
> I just went in an updated and tested the Accumulator class code -- it needed some tweaks, but it's working now.
>
> The cython version is in an unknown state...
>
> some profiling:
>
> In [11]: run profile_accumulator.py
>
>
> In [12]: timeit accum1(10000)
>
> 100 loops, best of 3: 3.91 ms per loop
>
> In [13]: timeit list1(10000)
>
> 1000 loops, best of 3: 1.15 ms per loop
>
> These are simply appending 10,000 integers in a loop -- with teh list, the list is turned into a numpy array at the end. So it's still faster to accumulate in a list, then make an array, but only a about a factor of 3 -- I think this is because you are staring with a python integer -- with the accumulator function, you need to be checking type and pulling a native integer out with each append. but a list can append a python object with no type checking or anything.
>
> Then the conversion from list to array is all in C.
>
> Note that the accumulator version is still more memory efficient...
>
> In [14]: timeit accum2(10000)
>
> 100 loops, best of 3: 3.84 ms per loop
>
> this version pre-allocated the whole internal buffer -- not much faster the buffer re-allocation isn't a big deal (thanks to ndarray.resize using realloc(), and not creating a new numpy array)
>
> In [24]: timeit list_extend1(100000)
>
> 100 loops, best of 3: 4.15 ms per loop
>
> In [25]: timeit accum_extend1(100000)
>
> 1000 loops, best of 3: 1.37 ms per loop
>
> This time, the stuff is added in chunks 100 elements at a time -- the chunks being created ahead of time -- a list with range() the first time, and an array with arange() the second. much faster to extend with arrays...
>
> -CHB
>
>
>
> --
>
> Christopher Barker, Ph.D.
> Oceanographer
>
> Emergency Response Division
> NOAA/NOS/OR&R            (206) 526-6959   voice
> 7600 Sand Point Way NE   (206) 526-6329   fax
> Seattle, WA  98115       (206) 526-6317   main reception
>
> Chris.Barker@noaa.gov
> _______________________________________________
> 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



--

Christopher Barker, Ph.D.
Oceanographer

Emergency Response Division
NOAA/NOS/OR&R            (206) 526-6959   voice
7600 Sand Point Way NE   (206) 526-6329   fax
Seattle, WA  98115       (206) 526-6317   main reception

Chris.Barker@noaa.gov