[R] Speeding up resampling of rows from a large matrix

[Juan Pablo Lewinger]

I'm trying to:

Resample with replacement pairs of distinct rows from a 120 x 65,000 
matrix H of 0's and 1's. For each resampled pair sum the resulting 2 
x 65,000 matrix by column:

     0 1 0 1 ...
+
     0 0 1 1 ...
    _______
=  0 1 1 2 ...

For each column accumulate the number of 0's, 1's and 2's over the 
resamples to obtain a 3 x 65,000 matrix G.

For those interested in the background, H is a matrix of haplotypes, 
each pair of haplotypes forms a genotype, and each column corresponds 
to a SNP. I'm using resampling to compute the null distribution of 
the maximum over correlated SNPs of a simple statistic.


The code:
#-------------------------------------------------------------------------------
nSNPs <- 1000
H <- matrix(sample(0:1, 120*nSNPs , replace=T), nrow=120)
G <- matrix(0, nrow=3, ncol=nSNPs)
# Keep in mind that the real H is 120 x 65000

nResamples <- 3000
pair <- replicate(nResamples, sample(1:120, 2))

gen <- function(x){g <- sum(x); c(g==0, g==1, g==2)}

for (i in 1:nResamples){
    G <- G + apply(H[pair[,i],], 2, gen)
}
#-------------------------------------------------------------------------------
The problem is that the loop takes about 80 mins to complete and I 
need to repeat the whole thing 10,000 times, which would then take 
over a year and a half!

Is there a way to speed this up so that the full 10,000 iterations 
take a reasonable amount of time (say a week)?

My machine has an Intel Xeon 3.40GHz CPU with 1GB of RAM

 > sessionInfo()
R version 2.5.0 (2007-04-23)
i386-pc-mingw32

I would greatly appreciate any help.

Juan Pablo Lewinger
Department of Preventive Medicine
Keck School of Medicine
University of Southern California