Application of mclapply() to a function writing to a global variable - r

Question

I'm trying to use parallel::mclapply to speed up the calculation of the following code:
library(raster)
library(HistogramTools)#for AddHistogram
#Create a first h here for the first band... omitted for brevity
readNhist <- function(n,mconst) {
l <- raster(filename[i], varname=var[i], band=n, na.rm=T)
gain(l) <- mconst
h <<- AddHistograms(h, hist(l, plot=F, breaks=histbreaks,right=FALSE))
}
lapply( 1:10000, readNhist, mconst=1, mc.cores=7 )
#Then do stuff with the h histogram...
When performing the code above, all is fine. If using mclapply (below), the result is miles away from what I want to obtain: the histograms are all wrong.
library(raster)
library(HistogramTools)#for AddHistogram
library(parallel)
#Create a first h here for the first band... omitted for brevity
readNhist <- function(n,mconst) {
l <- raster(filename[i], varname=var[i], band=n, na.rm=T)
gain(l) <- mconst
h <<- AddHistograms(h, hist(l, plot=F, breaks=histbreaks,right=FALSE))
}
mclapply( 2:10000, readNhist, mconst=1 )
#Then do stuff with the h histogram...
I feel like there's something vital I'm missing with the application of parallel computation to this function.

Answer 1

The problem is the <<- which is bad practice in general as far as I can gather.
The function can be rearranged thusly:
readNhist <- function(n,mconst) {
l <- raster(filename, varname=var, band=n, na.rm=T)
gain(l) <- mconst
hist <- hist(l, plot=F, breaks=histbreaks,right=FALSE)
return(hist)
}
And called like this:
hists <- mclapply( 2:nbands, readNhist, mconst=gain, mc.cores=ncores )
ch <- AddHistograms(x=hists)
h <- AddHistograms(h, ch)
rm(ch, hists)
This is pretty fast even with a huge number of layers (and thus histograms).