Parallel computing in R with foreach()

Parallel computing in R is defined as the practice of utilizing multiple CPU cores or processors to perform computations concurrently. This speeds up tasks such as data analysis and statistical computing.

Motivation

Generally, R code runs perfectly fine on a single processor. So why do we still require parallel computing? This is because sometimes computations in R can be:

CPU-bound: R operations can take too much CPU time.
Memory-bound: R operations can consume too much memory. With parallel computing, we can distribute the data across multiple nodes or processes.
I/O-bound: Reading or writing from disk can take a long time when programming in R. Such tasks can benefit from having parallel computing through asynchronous I/O operations.
Network-bound: Transfer time when using R can be a lot.

As we know, R is a popular programming language and environment for statistical computing and data analysis, but many of its built-in functions and packages aren’t inherently optimized for parallel execution. Some of the tools and packages in R that support parallelism to improve performance are explained below.

Built-in parallelism

One of the packages for parallelism that R provides is the parallel package. This package includes functions like mclapply() and mcMap(), enabling us to run functions in parallel using multiple cores. It provides us with facilities for parallelizing certain types of operations on data structures, such as splitting and combining large data frames.

Explicit parallelism with the `foreach` package

Another package commonly used for parallel processing in R is the foreach package. With the bewildering variety of existing looping constructs, we might doubt there’s a need for yet another construct. The main reason for using the foreach package is that it supports parallel execution. As a result of combining foreach with a parallel backend (e.g., doParallel, doMC, or doMPI), we can easily distribute iterations of a loop across multiple cores or even across a cluster of machines.

Parallelized functions and packages

Some R packages are designed to perform computations in parallel for certain tasks. For instance, the parallelDist package can calculate pairwise distances in parallel, and the snow package provides more control over parallel computing.

# Load the required libraries for parallelism
library(foreach)
library(doParallel)
# Set the number of cores to use
num_cores <- 4
cl <- makeCluster(num_cores)  # Create a cluster with specified number of cores
registerDoParallel(cl)  # Register the cluster for parallel processing
# Create a input vector
input_vector <- 1:10
# Parallel computation using foreach; the %dopar% operator ensures that the loop is executed in parallel
# The .combine=c line concatenates the result of each iteration into a single vector
output <- foreach(i = input_vector, .combine = c) %dopar% {
  i * i
}
#Cluster of parallel processes made by the makeCluster function is stopped and shut down
#This step prevents resource leaks associated with the cluster during parallel processing
stopCluster(cl)
# Print the input and output vectors
cat("Input:", input_vector, "\n")
cat("Output:", output, "\n")

Parallel computing in R with foreach()

Motivation

Built-in parallelism

Explicit parallelism with the `foreach` package

Parallelized functions and packages

Code example

Conclusion

Parallel computing in R with foreach()

Motivation

Built-in parallelism

Explicit parallelism with the foreach package

Parallelized functions and packages

Code example

Conclusion

Explicit parallelism with the `foreach` package