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.
Note: To read more about parallel computing, visit this Answer.
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.
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.
foreach
packageAnother 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.
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.
Note: To read more about these parallelism packages in R, consult this the available CRAN packages.
# Load the required libraries for parallelismlibrary(foreach)library(doParallel)# Set the number of cores to usenum_cores <- 4cl <- makeCluster(num_cores) # Create a cluster with specified number of coresregisterDoParallel(cl) # Register the cluster for parallel processing# Create a input vectorinput_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 vectoroutput <- 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 processingstopCluster(cl)# Print the input and output vectorscat("Input:", input_vector, "\n")cat("Output:", output, "\n")
Note: Here, explicit parallelism is being used via the
foreach
anddoParallel
libraries.
It’s important to have a sound understanding of parallel programming in R, as not all tasks are well-suited for parallel execution. Such tasks include the ones with sequential dependencies, small workloads, and irregular computations. In addition, it can be complex to manage parallelism and avoid issues such as race conditions or data dependencies. Therefore, we must carefully consider the problem given to us in order to perform parallel computing effectively.
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