[Rd] Parallel computing: how to transmit multiple parameters to a function in parLapply?

alexios ghalanos
2013-12-24T12:20:45Z

This works:

clusterExport(cl, c("f","y"), envir=environment())
r <- parLapply(cl, x, function(x) f(x,y))

You need to export your function (?f?) and additional variables (?y?), and then
define that function inside parLapply ("f(x,y)?). If you were to also make
use of
additional libraries (or source some scripts) then you should also consult
?clusterEvalQ?.
The makeCluster command (at least in windows via socket) just initializes new R
processes which do not know about your functions or variables unless you
export those to them.

Perhaps a question best suited for R-help.

Alexios



On 24 Dec 2013, at 06:15, Yu Wan <walterwan at 126.com> wrote:

> 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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