Using the future package in R for parallel computing

howto-future.R

## //////////////////
## FUTURE PACKAGE
## //////////////////


## here is a function that sums up really slow
## ---------------------------------------------
slow_sum <- function(vec) {
  SUM <- 0
  for (i in vec) {
    SUM <- SUM + i
    Sys.sleep(0.2)
  }  
  return(SUM)
}

## r package needed for microbenchmarking
library(microbenchmark)

## parameter
vec <- 1:20

## 1) traditional computation
## -------------------------

microbenchmark(lapply(1:20, FUN = slow_sum), times=10, unit = "us") ## 4'002'902


## 2) using parallel package
## -------------------------

microbenchmark(parallel::mclapply(1:20, FUN = slow_sum), times=10, unit = "us") ## 710'882


## 3) how future works 
## -------------------------

library(future)
plan(sequential)

tmp <- future(slow_sum(vec))
fa <- value(tmp)

## is identical to
fa %<-% slow_sum(vec)



## 4) using different plans
## -------------------------
?plan

## sequential
plan(sequential)
microbenchmark(fa %<-% future( slow_sum(vec) ), times=10, unit = "us")

## parallelel
plan(multiprocess)
microbenchmark(fb %<-% future( slow_sum(vec) ), times=10, unit = "us")

## multiprocess will use the maximal available cores
future::availableCores("mc.cores")



## 5) other options that use future as a backbone: future.apply and furrr
## -------------------------
#microbenchmark(future.apply::future_lapply(1:20, FUN = slow_sum), times=10, unit = "us") ## 1'160'609