[Rd] Converting non-32-bit integers from python to R to use bit64: reticulate

[Gabriel Becker]

Hi Juan,

Comments inline.

On Wed, May 29, 2019 at 12:48 PM Juan Telleria Ruiz de Aguirre <
jtelleria.rproject using gmail.com> wrote:

> Dear R Developers,
>
> There is an interesting issue related to "reticulate" R package which
> discusses how to convert Python's non-32 bit integers to R, which has had
> quite an exhaustive discussion:
>
> https://github.com/rstudio/reticulate/issues/323
>
> Python seems to handle integers differently from R, and is dependant on the
> system arquitecture: On 32 bit systems uses 32-bit integers, and on 64-bit
> systems uses 64-bit integers.
>
> So my question is:
>
> As regards R's C Interface, how costly would it be to convert INTSXP from
> 32 bits to 64 bits using C, on 64 bits Systems? Do the benefits surpass the
> costs? And should such development be handled from within R Core / Ordinary
> Members , or it shall be left to package maintainers?
>

Well, I am not an R-core member, but I can mention a few things:

1. This seems like it would make the results of R code non-reproducible
between 32 and 64bit versions of R; at least some code would give different
results (at the very least in terms of when integer values overflow to NA,
which is documented behavior).
2. Obviously all integer data would take twice as much memory, memory
bandwidth, space in caches, etc, even when it doesn't need it.
3. Various places treat data /data pointers coming out of INTSXP and LGLSXP
objects the same within the internal R sources (as currently they're both
int/int*). Catching and fixing all those wouldn't be impossible, but it
would take at least some doing.

For me personally 1 seems like a big problem, and 3 makes the conversion
more work than it might have seemed initially.

As a related side note, as far as I understand what I've heard from R-core
members directly, the choice to not have multiple types of integers is
intentional and unlikely to change.

Best,
~G




>
> Thank you! :)
>
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