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 _______________________________________________ pypy-dev mailing list pypy-dev@python.org https://mail.python.org/mailman/listinfo/pypy-dev