Moving to parApply

To run code in parallel using the parallel package, the basic workflow has three steps.

Create a cluster using makeCluster().
Do some work.
Stop the cluster using stopCluster().

The simplest way to make a cluster is to pass a number to makeCluster(). This creates a cluster of the default type, running the code on that many cores.

The object dd is a data frame with 10 columns and 100 rows. The following code uses apply() to calculate the column medians:

apply(dd, 2, median)

To run this in parallel, you swap apply() for parApply(). The arguments to this function are the same, except that it takes a cluster argument before the usual apply() arguments.

This is a part of the course

“Writing Efficient R Code”

View Course

Exercise instructions

Use the detectCores() function to print the number of available cores to the console.
Create a cluster using makeCluster(); set the number of cores used equal to 2. Save the result as cl.
Rewrite the above apply() function as parApply(). Remember, the first argument should now be the cluster object, cl.
Stop the cluster using stopCluster().

Hands-on interactive exercise

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

# Determine the number of available cores
___

# Create a cluster via makeCluster
cl <- makeCluster(___)

# Parallelize this code
apply(dd, 2, median)

# Stop the cluster
stopCluster(cl)

Edit and Run Code