I'm trying to run the following code: ## N <- 100000; a <- numeric(N); b <- numeric(N) e <- rnorm(N)
for(i in 1:N) {
a[i] <- exp(e[i])
b[i] <- log(abs(a[i]))
}
##When I run it on the head node, everything is fine. However, when I send the R process off to one of the cluster nodes (i.e. using mosrun from the head node) the program takes about 10 times longer (in wall-clock time, cpu time is roughly the same).
Interestingly, when I tried running the following code: ## N <- 100000; a <- numeric(N); b <- numeric(N) e <- rnorm(N)
for(i in 1:N) {
a[i] <- exp(e[i])
b[i] <- exp(abs(a[i]))
}
##I didn't experience any slow-down! That is the wall-clock time is the same when run on the head node or on the cluster nodes. The only difference between the two programs is that one takes a log in the for() loop and the other one takes an exponential.
I guess my question is why would taking the log() produce a 10 fold increase in runtime? I know that Mosix clusters can experience serious performance hits if you make a lot of system calls or write out data to files but I don't think I'm doing that here. Is there some major difference in the way that exp() and log() are implemented?
I'm pretty sure this isn't an R problem but I'm wondering if R is doing something behind the scenes that's affecting performance in the openMosix setting.
Thanks in advance for any help.
-roger
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