The usual way is to put the function in a package and load the package.
Otherwise, you could do something along these lines
auto_function <- function( code, ... ){
dots <- list(code, ...)
function(...){
do.call( cppFunction, dots )( ... )
}
}
This way the function knows how to compile itself, for example:
> fun <- auto_function(' double inner_Cpp(double a){ return 1; } ' )
# this takes a while the first time
> fun( 2 )
[1] 1
# this is instant thanks to caching of sourceCpp
> fun( 2 )
[1] 1
Romain
Le 26/09/13 18:57, Matteo Fasiolo a écrit :
Dear Rcpp developers,
I'm trying to parallelize some of my algorithms but I have encountered
the following problem:
# I have a cppFunction
cppFunction(' double inner_Cpp(double a){ return 1; } ')
# And an R wrapper around it
wrapper_R<- function(input)
{
inner_Cpp(input)
}
# And I want to call the wrappen in parallel within algorithm
algo <- function(input)
{
cl <- makeCluster(2)
clusterExport(cl, "inner_Cpp")
a <- clusterApply(cl, 1:2, wrapper_R)
stopCluster(cl)
a
}
algo(2)
Error in checkForRemoteErrors(val) :
2 nodes produced errors; first error: NULL value passed as symbol address
It seems that I'm unable to find the address of inner_Cpp.
If the inner function is an R function there is no problem:
inner_R <- function(input) 1
wrapper_R<- function(input)
{
inner_R(input)
}
algo <- function(input)
{
cl <- makeCluster(2)
clusterExport(cl, "inner_R")
a <- clusterApply(cl, 1:2, wrapper_R)
stopCluster(cl)
a
}
algo(2)
[[1]]
[1] 1
[[2]]
[1] 1
Do you have any idea about why this is happening?
Given that I have just started parallelizing my algorithms in this
way, any suggestion/criticism about the overall approach is
more then welcome!
Matteo
--
Romain Francois
Professional R Enthusiast
+33(0) 6 28 91 30 30
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