Can rbind be parallelized in R?

Because you said that you want to rbind data.frame objects you should use the data.table package. It has a function called rbindlist that enhance drastically rbind. I am not 100% sure but I would bet any use of rbind would trigger a copy when rbindlist does not. Anyway a data.table is a data.frame so you do not loose anything to try.

EDIT:

library(data.table)
system.time(dt <- rbindlist(pieces))
utilisateur     système      écoulé 
       0.12        0.00        0.13 
tables()
     NAME  NROW MB COLS                        KEY
[1,] dt   1,000 8  X1,X2,X3,X4,X5,X6,X7,X8,...    
Total: 8MB

Lightning fast...

I haven't found a way to do this in parallel either thus far. However for my dataset (this one is a list of about 1500 dataframes totaling 4.5M rows) the following snippet seemed to help:

while(length(lst) > 1) {
    idxlst <- seq(from=1, to=length(lst), by=2)

    lst <- lapply(idxlst, function(i) {
        if(i==length(lst)) { return(lst[[i]]) }

        return(rbind(lst[[i]], lst[[i+1]]))
    })
}

where lst is the list. It seemed to be about 4 times faster than using do.call(rbind, lst) or even do.call(rbind.fill, lst) (with rbind.fill from the plyr package). In each iteration this code is halving the amount of dataframes.