[pypy-dev] Shift and typed array

[Maciej Fijalkowski]

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.larsen at 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 <fijall at 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.larsen at 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 <fijall at 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.larsen at 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!
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