[Numpy-discussion] Slow divide of int64?

Charles R Harris charlesr.harris@gmail....
Mon Aug 13 23:49:57 CDT 2012


On Mon, Aug 13, 2012 at 10:32 PM, Charles R Harris <
charlesr.harris@gmail.com> wrote:

>
>
> On Sat, Aug 11, 2012 at 6:36 PM, Matthew Brett <matthew.brett@gmail.com>wrote:
>
>> Hi,
>>
>> A friend of mine just pointed out that dividing by int64 is
>> considerably slower than multiplying in numpy:
>>
>> <script>
>> from timeit import timeit
>>
>> import numpy as np
>> import numpy.random as npr
>>
>> sz = (1024,)
>> a32 = npr.randint(1, 5001, sz).astype(np.int32)
>> b32 = npr.randint(1, 5001, sz).astype(np.int32)
>> a64 = a32.astype(np.int64)
>> b64 = b32.astype(np.int64)
>>
>> print 'Mul32', timeit('d = a32 * b32', 'from __main__ import a32, b32')
>> print 'Div32', timeit('d = a32 / b32', 'from __main__ import a32, b32')
>> print 'Mul64', timeit('d = a64 * b64', 'from __main__ import a64, b64')
>> print 'Div64', timeit('d = a64 / b64', 'from __main__ import a64, b64')
>> </script>
>>
>> gives (64 bit Debian Intel system, numpy trunk):
>>
>> Mul32 2.71295905113
>> Div32 6.61985301971
>> Mul64 2.78101611137
>> Div64 22.8217148781
>>
>> with similar values for numpy 1.5.1.
>>
>> Crude testing with Matlab and Octave suggests they do not seem to have
>> this same difference:
>>
>> >> divtest
>> Mul32 4.300662
>> Div32 5.638622
>> Mul64 7.894490
>> Div64 18.121182
>>
>> octave:2> divtest
>> Mul32 3.960577
>> Div32 6.553704
>> Mul64 7.268324
>> Div64 13.670760
>>
>> (files attached)
>>
>> Is there something specific about division in numpy that would cause
>> this slowdown?
>>
>>
> Numpy is doing an integer divide unless you are using Python 3.x. The
> np.true_divide ufunc will speed things up a bit. I'm not sure what
> Matlab/Octave are doing for division in this case.
>
>
For int64:

In [23]: timeit multiply(a, b)
100000 loops, best of 3: 3.31 us per loop

In [24]: timeit true_divide(a, b)
100000 loops, best of 3: 9.35 us per loop


> Chuck
>
>
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