So other than numpy, do you think:
a[0] = (2**63 << 1) & (2**64 - 1)
is the best way I can do?
On Mon, Apr 4, 2016 at 3:52 PM, Tuom Larsen <tuom.lar...@gmail.com> wrote:
> Yes, I just want to write code which is portable (as for example that
> `& ...`) but still favors PyPy.
>
> On Mon, Apr 4, 2016 at 3:49 PM, Maciej Fijalkowski <fij...@gmail.com> wrote:
>> right, so there is no way to get wrap-around arithmetics in cpython
>> without modifications
>>
>> On Mon, Apr 4, 2016 at 3:48 PM, Tuom Larsen <tuom.lar...@gmail.com> wrote:
>>> I would like to avoid numpy, if possible. Even if it might be bundled
>>> with PyPy (is it?) I still would like the code to run in CPython with
>>> no dependencies.
>>>
>>> On Mon, Apr 4, 2016 at 3:45 PM, Maciej Fijalkowski <fij...@gmail.com> wrote:
>>>> so numpy64 will give you wrap-around arithmetics. What else are you
>>>> looking for? :-)
>>>>
>>>> On Mon, Apr 4, 2016 at 3:38 PM, Tuom Larsen <tuom.lar...@gmail.com> wrote:
>>>>> You mean I should first store the result into numpy's `int64`, and
>>>>> then to `array.array`? Like:
>>>>>
>>>>> x = int64(2**63 << 1)
>>>>> a[0] = x
>>>>>
>>>>> Or:
>>>>>
>>>>> x = int64(2**63)
>>>>> x[0] = x << 1
>>>>>
>>>>> What the "real types" goes, is this the only option?
>>>>>
>>>>> Thanks in any case!
>>>>>
>>>>>
>>>>> On Mon, Apr 4, 2016 at 3:32 PM, Maciej Fijalkowski <fij...@gmail.com>
>>>>> wrote:
>>>>>> one option would be to use integers from _numpypy module:
>>>>>>
>>>>>> from numpy import int64 after installing numpy.
>>>>>>
>>>>>> There are obscure ways to get it without installing numpy. Another
>>>>>> avenue would be to use __pypy__.intop.int_mul etc.
>>>>>>
>>>>>> Feel free to complain "no, I want real types that I can work with" :-)
>>>>>>
>>>>>> Cheers,
>>>>>> fijal
>>>>>>
>>>>>> On Mon, Apr 4, 2016 at 3:10 PM, Tuom Larsen <tuom.lar...@gmail.com>
>>>>>> wrote:
>>>>>>> Hello!
>>>>>>>
>>>>>>> Suppose I'm on 64-bit machine and there is an `a = arrar.array('L',
>>>>>>> [0])` (item size is 8 bytes). In Python, when an integer does not fit
>>>>>>> machine width it gets promoted to "long" integer of arbitrary size. So
>>>>>>> this will fail:
>>>>>>>
>>>>>>> a[0] = 2**63 << 1
>>>>>>>
>>>>>>> To fix this, one could instead write:
>>>>>>>
>>>>>>> a[0] = (2**63 << 1) & (2**64 - 1)
>>>>>>>
>>>>>>> My question is, when I know that the result will be stored in
>>>>>>> `array.array` anyway, how to prevent the promotion to long integers?
>>>>>>> What is the most performat way to perform such calculations? Is PyPy
>>>>>>> able to optimize away that `& (2**64 - 1)` when I use `'L'` typecode?
>>>>>>>
>>>>>>> I mean, in C I wouldn't have to worry about it as everything above the
>>>>>>> 63rd bit will be simply cut off. I would like to help PyPy to generate
>>>>>>> the best possible code, does anyone have some suggestions please?
>>>>>>>
>>>>>>> Thanks!
>>>>>>> _______________________________________________
>>>>>>> pypy-dev mailing list
>>>>>>> pypy-dev@python.org
>>>>>>> https://mail.python.org/mailman/listinfo/pypy-dev
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