[R] Memory problem on a linux cluster using a large data set

Martin Morgan
Mon, 18 Dec 2006 18:32:05 -0000

Iris --

I hope the following helps; I think you have too much data for a
32-bit machine.

Martin

Iris Kolder <iriskolder at yahoo.com> writes:

> Hello,
>
> I have a large data set 320.000 rows and 1000 columns. All the data
> has the values 0,1,2.

It seems like a single copy of this data set will be at least a couple
of gigabytes; I think you'll have access to only 4 GB on a 32-bit
machine (see section 8 of the R Installation and Administration guide),
and R will probably end up, even in the best of situations, making at
least a couple of copies of your data. Probably you'll need a 64-bit
machine, or figure out algorithms that work on chunks of data.

> on a linux cluster with R version R 2.1.0.  which operates on a 32

This is quite old, and in general it seems like R has become more
sensitive to big-data issues and tracking down unnecessary memory
copying.

> ?cannot allocate vector size 1240 kb?. I?ve searched through

use traceback() or options(error=recover) to figure out where this is
actually occurring.

> SNP <- read.table("file.txt", header=FALSE, sep="")
# read in data file

This makes a data.frame, and data frames have several aspects (e.g.,
automatic creation of row names on sub-setting) that can be problematic
in terms of memory use. Probably better to use a matrix, for which:

     'read.table' is not the right tool for reading large matrices,
     especially those with many columns: it is designed to read _data
     frames_ which may have columns of very different classes. Use
     'scan' instead.

(from the help page for read.table). I'm not sure of the details of
the algorithms you'll invoke, but it might be a false economy to try
to get scan to read in 'small' versions (e.g., integer, rather than
numeric) of the data -- the algorithms might insist on numeric data,
and then make a copy during coercion from your small version to
numeric.

> SNP$total.NAs = rowSums(is.na(SN         # calculate the number of NA per
row and adds a colum with total Na's

This adds a column to the data.frame or matrix, probably causing at
least one copy of the entire data. Create a separate vector instead,
even though this unties the coordination between columns that a data
frame provides.

> SNP  = t(as.matrix(SNP))                          # transpose rows and
columns

This will also probably trigger a copy;

> snp.na<-SNP

R might be clever enough to figure out that this simple assignment
does not trigger a copy. But it probably means that any subsequent
modification of snp.na or SNP *will* trigger a copy, so avoid the
assignment if possible.

> snp.roughfix<-na.roughfix(snp.na)
> fSNP<-factor(snp.roughfix[, 1])                # Asigns factor to case
control status
>
> snp.narf<- randomForest(snp.roughfix[,-1], fSNP, na.action=na.roughfix,
ntree=500, mtry=10, importance=TRUE, keep.forest=FALSE, do.trace=100)

Now you're entirely in the hands of the randomForest. If memory
problems occur here, perhaps you'll have gained enough experience to
point the package maintainer to the problem and suggest a possible
solution.

> set it should be able to cope with that amount. Perhaps someone has
> tried this before in R or is Fortram a better choice? I added my R

If you mean a pure Fortran solution, including coding the random
forest algorithm, then of course you have complete control over memory
management. You'd still likely be limited to addressing 4 GB of
memory.


> I wrote a script to remove all the rows with more than 46 missing
> values. This works perfect on a smaller dataset. But the problem
> arises when I try to run it on the larger data set I get an error
> ?cannot allocate vector size 1240 kb?. I?ve searched through
> previous posts and found out that it might be because i?m running it
> on a linux cluster with R version R 2.1.0.  which operates on a 32
> bit processor. But I could not find a solution for this problem. The
> cluster is a really fast one and should be able to cope with these
> large amounts of data the systems configuration are Speed: 3.4 GHz,
> memory 4GByte. Is there a way to change the settings or processor
> under R? I want to run the function Random Forest on my large data
> set it should be able to cope with that amount. Perhaps someone has
> tried this before in R or is Fortram a better choice? I added my R
> script down below.
>
> Best regards,
>
> Iris Kolder
>
> SNP <- read.table("file.txt", header=FALSE, sep="")
# read in data file
> SNP[SNP==9]<-NA                                   # change missing
values from a 9 to a NA
> SNP$total.NAs = rowSums(is.na(SN         # calculate the number of NA per
row and adds a colum with total Na's
> SNP = SNP[ SNP$total.NAs < 46,  ]         # create a subset with no more
than 5%(46) NA's
> SNP$total.NAs=NULL                              # remove added column with
sum of NA's
> SNP  = t(as.matrix(SNP))                          # transpose rows and
columns
> set.seed(1)
> snp.na<-SNP
> snp.roughfix<-na.roughfix(snp.na)
> fSNP<-factor(snp.roughfix[, 1])                # Asigns factor to case
control status
>
> snp.narf<- randomForest(snp.roughfix[,-1], fSNP, na.action=na.roughfix,
ntree=500, mtry=10, importance=TRUE, keep.forest=FALSE, do.trace=100)
>
> print(snp.narf)
>
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--
Martin T. Morgan
Bioconductor / Computational Biology