On Jul 6, 2009, at 9:39 PM, Duncan Murdoch wrote:
On 06/07/2009 4:16 PM, Peter Dalgaard wrote:
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..
Hmm, it's slightly tricky because R often duplicates objects, so
you may hit the limit only transiently. Also, R has an internal 2GB
limit on single vectors. But something like this
Is it a 2 GB limit in size, or in the number of elements? I'm still
spending almost all my time in 32 bit land, so it's hard to check.
It's 2 GB in length, 8 GB in size.
> big_vector=c(1:2000000000)
# 15 minutes later ...
> object.size(big_vector)
8000000040 bytes
Duncan Murdoch
Y <- replicate(30, rnorm(2^28-1))
should create an object of about 30*2GB. Then lapply(Y, mean)
should generate 30 very good and very expensive approximations to 0.
(For obvious reasons, I haven't tested this on a 1GB ThinkPad
X40....)
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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