Apologies at what must be a very basic question, but I have not found
any clear examples on how to design the following....
I would like to run iterative analysis over several processors. A toy
example of the analysis is attached; for a resampling function run 1k
times, with two different sets of conditioning variables i,j on some
data vec...
What is the usual way to attack such a problem using snow? My
understanding up to this point is that one should:
(1) set the random seed to uncorrelate the processors' actions in
select()
(2) make a function myfunc(vec,i,j) which returns the item of interest
(3) set up a wrapper which iterates through i,j, and makes the call to
the cluster
(4) call the cluster using clusterApply(cl,vec, myfunc)....
I must be terribly confused based on the results attached below....any
advice will be appreciated...
Many thanks,
Best,
Eric
--
Eric Rupley
University of Michigan, Museum of Anthropology
1109 Geddes Ave, Rm. 4013
Ann Arbor, MI 48109-1079
[EMAIL PROTECTED]
+1.734.276.8572
# set up
#
# cl <- makeCluster(7)
# 8 slaves are spawned successfully. 0 failed.
#clusterSetupRNG(cl)
#[1] "RNGstream"
vec <- runif(1000,1,100)
d <- NULL; c.j <- NULL;c.i <- NULL
# the toy function
analysis.func <- function (vec,i,j) {
b <- NULL
for (k in c(1:1000)) {
a <- sample(vec,1000,replace=T) #requires randoms...
b <- append(b, mean(a))
}
c <- (sum(b)*j)/i
return(c)
}
# the "analysis"
system.time(for (i in c(2,4)) { # a series of nested iterations...
for (j in c(5:6)) {
d <-
append( mean( as.numeric( clusterApply(cl,vec,analysis.func,i,j) ) ) ,
d)
# this is ugly and contorted; there has to be a better way?
c.j <- append(j, c.j)
c.i <- append(i, c.i)
}
})
# user system elapsed
# 9.758 0.291 48.771
#>
# but the old way is faster...
d <- NULL; c.j <- NULL; c.i <- NULL # set up again
system.time(for (i in c(2,4)) { # a series of nested iterations...
for (j in c(5:6)) {
d <-append( mean( as.numeric( analysis.func(vec,i,j) )) ,d)
# keeping it ugly for timing comparision...
c.j <- append(j, c.j)
c.i <- append(i, c.i)
}
})
# user system elapsed
# 0.299 0.002 0.299
#> # arrgrgrgrgrg!!!
stopCluster(cl)
#[1] 1
sessionInfo()
#R version 2.7.1 (2008-06-23)
#i386-apple-darwin8.10.1
#
#locale:
#en_US.UTF-8/en_US.UTF-8/C/C/en_US.UTF-8/en_US.UTF-8
#
#attached base packages:
#[1] stats graphics grDevices utils datasets methods base
#
#other attached packages:
#[1] rlecuyer_0.1 boot_1.2-33 snow_0.3-3 Rmpi_0.5-5
#
#loaded via a namespace (and not attached):
#[1] tools_2.7.1
date()
#[1] "Sat Aug 23 04:25:50 2008"
#>
#Too late for a drink. Pity.
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