On 28 Dec 2015, at 19:58, Chris Barker <chris.barker@noaa.gov> wrote:
On Wed, Dec 23, 2015 at 4:01 AM, Nicolas P. Rougier <Nicolas.Rougier@inria.fr> wrote: But my implementation is quite slow, especially when you add one item at a time:
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.
With my Accumulator class:
https://github.com/PythonCHB/NumpyExtras/blob/master/numpy_extras/accumulato...
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
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