R help - Feb 2011 - using SNOW and clusterApplyLB to run jobs parallel

I have this function and want to run it parallel with different sets of data.
Using SNOW and clusterApplyLB.

system.time(out <- mclapply(cData, plotGraph)) #each cData contains 100X6000
doubles
system.time(out <- mclapply(cData2, plotGraph))
system.time(out <- mclapply(cData3, plotGraph))
system.time(out <- mclapply(cData4, plotGraph))
system.time(out <- mclapply(cData5, plotGraph))
system.time(out <- mclapply(cData6, plotGraph))

plotGraph() <- function(cData)
{
cl = unname(cor(cData))

result = cbind(as.vector(row(cl)),as.vector(col(cl)),as.vector(cl))
result = result[result[,1] != result[,2],]

corm = result

corm =corm[abs(corm[,3]) >= CORRELATION, ]
# remove low cor pairs
library(network); library(sna)
net <- network(corm, directed = F)
# the network
cd <- component.dist(net)
# component analysis
delete.vertices(net, which(cd$csize[cd$membership] == 1))
# delete genes not connected with others
plot(net)
}

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On 01.02.2011 23:07, kparamas wrote:
>
> I have this function and want to run it parallel with different sets of
data.
> Using SNOW and clusterApplyLB.
Nice, but what is your question?
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.

Uwe Ligges




> system.time(out<- mclapply(cData, plotGraph)) #each cData contains
100X6000
> doubles
> system.time(out<- mclapply(cData2, plotGraph))
> system.time(out<- mclapply(cData3, plotGraph))
> system.time(out<- mclapply(cData4, plotGraph))
> system.time(out<- mclapply(cData5, plotGraph))
> system.time(out<- mclapply(cData6, plotGraph))
>
> plotGraph()<- function(cData)
> {
> cl = unname(cor(cData))
>
> result = cbind(as.vector(row(cl)),as.vector(col(cl)),as.vector(cl))
> result = result[result[,1] != result[,2],]
>
> corm = result
>
> corm =corm[abs(corm[,3])>= CORRELATION, ]
> # remove low cor pairs
> library(network); library(sna)
> net<- network(corm, directed = F)
> # the network
> cd<- component.dist(net)
> # component analysis
> delete.vertices(net, which(cd$csize[cd$membership] == 1))
> # delete genes not connected with others
> plot(net)
> }
>