On Wed, Dec 30, 2015 at 6:34 AM, Nicolas P. Rougier < nicolas.roug...@inria.fr> wrote:
> > > On 28 Dec 2015, at 19:58, Chris Barker <chris.bar...@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.bar...@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.bar...@noaa.gov
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
