You need to be careful when using mclapply to operate on large data sets. It's easy to create too many workers for the amount of memory on your computer and the amount of memory used by your computation. It's hard to predict the memory requirements due to the complexity of R's memory management, so it's best to monitor memory usage carefully using a tool such as "top" or "htop".
You may be able to decrease the memory usage by splitting your work into more but smaller tasks since that may reduce the memory needed by the computation. I don't think that the choice of prescheduling affects the memory usage much, since mclapply will never fork more than mc.cores
workers at a time, regardless of the value of mc.prescheduling
.