On Jul 6, 2009, at 4:42 PM, Jonathan Greenberg wrote:
You could probably just make a big array and watch "top" usage -- a
5gb array would do the trick -- if you can break 4gb you are golden.
big_vector=c(1:1000000) and keep adding zeroes...
Except the maximum size for a vector (and I wonder also for an array?)
is 2 GB.
?"Memory-limits"
On a 10GB equipped machine (MacOSX with the 64 bit R 2.9.1) I get this
> big_vector=c(1:2500000000)
Error in 1:2.5e+09 : result would be too long a vector
And that is not because of lack of machine resources. You will need to
create a sizeable number (say 25) of 2 GB "big_vectors" to carry our
this suggestion.
--
DW
--j
Scott Zentz wrote:
Hello Everyone,
We have recently purchased a server which has 64GB of memory
running a 64bit OS and I have compiled R from source with the
following config
./configure --prefix=/usr/local/R-2.9.1 --enable-Rshlib --enable-
BLAS-shlib --enable-shared --with-readline --with-iconv --with-x --
with-tcktk --with-aqua --with-libpng --with-jpeglib
and I would like to verify that I can use 55GB-60GB of the 64GB of
memory within R. Does anyone know how this is possible? Will R be
able to access that amount of memory from a single process? I am
not an R user myself but I just wanted to test this before I turned
the server over to the researchers..
Thanks!
-scz
Jonathan A. Greenberg, PhD
Postdoctoral Scholar
Center for Spatial Technologies and Remote Sensing (CSTARS)
University of California, Davis
One Shields Avenue
The Barn, Room 250N
Davis, CA 95616
Cell: 415-794-5043
AIM: jgrn307, MSN: jgrn...@hotmail.com, Gchat: jgrn307
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