Debugging reason for python running unreasonably slow when adding numbers

[Thomas Passin]

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.