I've been searching around the internet, trying to understand parallel processing.
What they all seem to assume is that I have some kind of loop function operating on e.g. every Nth row of a data set divided among N cores and combined afterwards, and I'm pointed towards a lot of parallelized apply() functions.
(Warning, ugly code below)
My situation though is that I have is on form
tempJob <- myFunction(filepath, string.arg1, string.arg2)
where the path is a file location, and the string arguments are various ways of sorting my data.
My current workflow is simply amassing a lot of
tempjob1 <- myFunction(args)
tempjob2 <- myFunction(other args)
...
tempjobN <- myFunction(some other args here)
# Make a list of all temporary outputs in the global environment
temp.list <- lapply(ls(pattern = "temp"), get)
# Stack them all
df <- rbindlist(temp.list)
# Remove all variables from workspace matching "temp"
rm(list=ls(pattern="temp"))
These jobs are entirely independent, and could in principle be run in 8 separate instances of R (although that would be a bother to manage I guess). How can I separate the first 8 jobs out to 8 cores, and whenever a core finishes its job and returns a treated dataset to the global environment it'll simply take whichever job is next in line.