[pypy-dev] Shift and typed array

[Maciej Fijalkowski]

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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