Debugging reason for python running unreasonably slow when adding numbers
Thomas Passin
list1 at tompassin.net
Mon Mar 20 13:45:01 EDT 2023
On 3/20/2023 11:21 AM, Edmondo Giovannozzi wrote:
>
>>> def sum1():
>>> s = 0
>>> for i in range(1000000):
>>> s += i
>>> return s
>>>
>>> def sum2():
>>> return sum(range(1000000))
>> Here you already have the numbers you want to add.
>
> Actually using numpy you'll be much faster in this case:
>
> § import numpy as np
> § def sum3():
> § return np.arange(1_000_000, dtype=np.int64).sum()
>
> On my computer sum1 takes 44 ms, while the numpy version just 2.6 ms
> One problem is that sum2 gives the wrong result. This is why I used np.arange with dtype=np.int64.
On my computer they all give the same result.
Python 3.10.9, PyQt version 6.4.1
Windows 10 AMD64 (build 10.0.19044) SP0
Processor: 11th Gen Intel(R) Core(TM) i7-1165G7 @ 2.80GHz, 1690 Mhz, 4
Core(s), 8 Logical Processor(s)
> sum2 evidently doesn't uses the python "big integers" e restrict the result to 32 bits.
What about your system? Let's see if we can figure the reason for the
difference.
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