Hi R-developers
In the package Parallel, the function parLapply(cl, x, f) seems to allow
transmission of only one parameter (x) to the function f. Hence in order to
compute f(x, y) parallelly, I had to define f(x, y) as f(x) and tried to
access y within the function, whereas y was defined outside of f(x).
Script:
library(parallel)
f <- function(x) {
z <- 2 * x + .GlobalEnv$y # Try to access y in the global scope.
return(z)
}
np <- detectCores(logical = FALSE) # Two cores of my laptop
x <- seq(1, 10, by = 1)
y <- 0.5 # Y may be an array in reality.
cl <- makeCluster(np) # initiate the cluster
r <- parLapply(cl, x, f) # apply f to x for parallel computing
stopCluster(cl)
The r was a list with 10 empty elements which means f failed to access y.
Then I tested f without parallel computing:
z <- f(x)
print(z)
[1] 2.5 4.5 6.5 8.5 10.5 12.5 14.5 16.5 18.5 20.5
The results indicates that we can access y using .GlobalEnv$y in a function
without parLapply.
The question is, is there any method for me to transmit y to f, or access y
within f during parallel computing?
The version of my R is 3.0.1 and I am running it on a Win8-64x system.
Thanks,
Yu
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