Using parSapply()

We previously played the following game:

Initialize: total = 0.
Roll a single die and add it to total.
If total is even, reset total to zero.
If total is greater than 10. The game finishes.

The game could be simulated using the play() function:

play <- function() {
  total <- no_of_rolls <- 0
  while(total < 10) {
    total <- total + sample(1:6, 1)

    # If even. Reset to 0
    if(total %% 2 == 0) total <- 0 
    no_of_rolls <- no_of_rolls + 1
  }
  no_of_rolls
}

To simulate the game 100 times, we could use a for loop or sapply():

res <- sapply(1:100, function(i) play())

This is perfect for running in parallel!

To make functions available on a cluster, you use the clusterExport() function. This takes a cluster and a string naming the function.

clusterExport(cl, "some_function")

This exercise is part of the course

Writing Efficient R Code

View Course

Exercise instructions

The play() function has been defined in your workspace.

Create a cluster using makeCluster(); set the number of cores used equal to 2. Store the result as cl.
Export the play() function to the cluster.
Rewrite the above sapply() function as parSapply().
Stop the cluster using stopCluster().

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

library("parallel")
# Create a cluster via makeCluster (2 cores)
cl <- ___

# Export the play() function to the cluster
___

# Re-write sapply as parSapply
res <- sapply(1:100, function(i) play())

# Stop the cluster
___

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