I think you need to distinguish a release of R (which is a source tarball)
from a binary redistribution. The sources are set up to allow you to
build x86_64 on MacOS, and also multiple architectures (and have been for
some time if you don't want MacOS-specific features such as quartz() --
that came in 2.7.0).
On Wed, 7 May 2008, Michael Braun wrote:
Is it possible to get some clarification regarding what is considered a
"release" configuration of R and what is considered "experimental?" I would
like to run a 64-bit build of R 2.7.0 on an x86_64 Mac Pro with 18GB of RAM
running Leopard (10.5.2). When I install the precompiled "universal" binary,
According to the FAQ 2.5, 'universal' means Intel and PowerPC. The
package instructions in 5.4 are for i386 and ppc.
it appears that I am getting a 32-bit build (I think this because I cannot
allocate objects of size >3GB and because .Platform$r_arch="i386").
The real test is .Machine$sizeof.pointer . 'r_arch' is just a label.
I checked out the r.research.att.com site, but I am hesitant to install
an "experimental" build. Also, I tried compiling R myself, but ran into
a large number of issues (including not passing make check, which I now
see is addressed in the FAQ).
So my question is, once 10.5.3 is released, should the binary I download from
CRAN install a 64-bit version of R? Will I need to compile from source? Or
am I still too far ahead of the curve?
I suggest you do compile from sources. I find Apple's compilers flaky and
that is the issue. Despite the comment in the FAQ, I've had no problems
with x86_64 on MacOS 10.5.2 *if I turn optimization off* . The issue with
log10 appears to be on-CPU calls and not calls to the log10 in libm.
(OTOH, since I get higher performance from x86_64 Linux and Solaris boxes,
I haven't done extensive testing.) Note that this may not be the only
such issue: gcc 4.3.x has a similar problem with sqrt on x86_64 when
optimizing.
You'll need to compile everything from sources (including all packages),
and it does sometimes help to have the same compilers used throughout.
Thanks,
Michael
Michael Braun
Assistant Professor of Management Science (Marketing Group)
MIT Sloan School of Management
One Amherst St., E40-169
Cambridge, MA 02142
[EMAIL PROTECTED]
617-253-3436
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--
Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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