I usually need to compute P-values as following:

1. generate one sample (usually it is a matrix)

2. apply several methods (I already wrote a subfunction for each method,

and they are independent) to the generated sample to get pvalues.

3. compare the pvalues.

Since each method mentioned above takes long time (always different length

of time) to compute the pvalue, I am try to computing the pvalues parallel.

I want to assign computation of each method to each cores (I have intel

i7). Do you have any suggestion? I put the four subfunctions together as

main.fun=function(i,x,y,numper)

{

if (i==1) z=cca1(y,x,numper)

if (i==2) z=2

if (i==3) z=2

if (i==4) z=3

z

}

Each i indicates ith subfunction.

But I always get

task 1 failed - "Lapack routine dgesv: system is exactly singular:

U[84,84] = 0"

But when I only run `cca1` function (not using `foreach`), there is no

error.

The `foreach` is like this

pvalue=foreach(i=1:4,.combine=c,.packages=c("MASS","base")) %dopar%

main.fun(i,x,y,500)

The single computation is like this

pvalue=cca1(y,x,500)

I also put following in the top lines of my program

library(foreach)

library(doSNOW)

library(MASS)

cl=makeCluster(4,type="SOCK")

registerDoSNOW(cl)

**This looks like when I compute the `pvalue` separately not using

`foreach`, there is no error. But when I combine the subfuntions togeter

like `main.fun`, it has error.**

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