From jte||er|@@rproject @end|ng |rom gm@||@com  Thu May 30 18:46:29 2019
From: jte||er|@@rproject @end|ng |rom gm@||@com (Juan Telleria Ruiz de Aguirre)
Date: Thu, 30 May 2019 18:46:29 +0200
Subject: [Rd] 
 Converting non-32-bit integers from python to R to use bit64:
 reticulate
In-Reply-To: <CAD4oTHH_ixXYEk1OEuFq_yJf83sDYmShbZ1nPGFovRvy-XZPnw@mail.gmail.com>
References: <CAJXDcw3jQd2B+hvfXuUUeczx=mmtw0MA_Udaesd-UgM+spyGJw@mail.gmail.com>
 <CAD4oTHH_ixXYEk1OEuFq_yJf83sDYmShbZ1nPGFovRvy-XZPnw@mail.gmail.com>
Message-ID: <CAJXDcw1V=2Z0BM31Ovc5SfiUEotuHoQRqWWi5z-8UfRg2qkM5w@mail.gmail.com>

Thank you Gabriel for valuable insights on the 64-bit integers topic.

In addition, my statement was wrong, as Python3 seems to have unlimited
(and variable) size integers. Here is related CPython Code:

https://github.com/python/cpython/blob/master/Objects/longobject.c

Division between Int-32 and Int-64 seems to only happen in Python2.

Best,
Juan

El mi?rcoles, 29 de mayo de 2019, Gabriel Becker <gabembecker at gmail.com>
escribi?:

> Hi Juan,
>
> Comments inline.
>
> On Wed, May 29, 2019 at 12:48 PM Juan Telleria Ruiz de Aguirre <
> jtelleria.rproject at 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! :)
>>
>>         [[alternative HTML version deleted]]
>>
>> ______________________________________________
>> R-devel at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-devel
>>
>

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[Rd] Converting non-32-bit integers from python to R to use bit64: reticulate
Juan Telleria Ruiz de Aguirre