Parallel Computing in R: Returning Multiple Outputs from foreach dopar Loop

Introduction

The foreach package in R provides a flexible way to parallelize loops, making it easier to perform computationally intensive tasks. One common use case is to execute a loop multiple times with different inputs or operations. However, when working with the dopar method, which runs the body of the loop in parallel using multiple cores, it can be challenging to return multiple outputs from each iteration.

In this article, we will explore how to return multiple outputs from a foreach dopar loop and provide practical examples using the foreach package in R. We’ll also delve into the underlying concepts and implementation details of parallel computing in R.

Understanding Parallel Computing in R

Before diving into the specifics of returning multiple outputs from a foreach dopar loop, it’s essential to understand how parallel computing works in R. The foreach package provides two main methods for parallelizing loops: .sequential and .dopar. The .sequential method uses the mclust package to execute the body of the loop sequentially on multiple cores, while the .dopar method runs the body of the loop in parallel using multiple cores.

When working with the .dopar method, each iteration of the loop is executed independently on a separate core. This allows for faster execution times and better utilization of system resources compared to the sequential method. However, it also introduces some challenges when trying to return multiple outputs from each iteration.

The Problem: Returning Multiple Outputs from foreach dopar Loop

Consider the following example:

library(foreach)
library(doParallel)

cl <- makeCluster(3)
registerDoParallel(cl)

oper1 <- foreach(i = 1:100000) %dopar% {
    i + 2
}

oper2 <- foreach(i = 1:100000) %dopar% {
    i + 3
}

In this example, we create a cluster with three cores and register it using the registerDoParallel function. We then define two separate loops using the .dopar method: oper1 and oper2. The first loop returns values of type numeric, while the second loop returns values of type character.

The problem arises when trying to return both outputs in a single list. As shown in the original question, simply assigning oper1[[i]] = i + 2 and then returning i + 3 from within the loop does not populate the oper1 list with the desired values.

Solution: Returning Multiple Outputs using combine Function

To return multiple outputs from a .dopar loop, we can use the combine function provided by the foreach package. The combine function allows us to specify how the output should be combined and transformed.

In the example below, we define a custom comb function that takes a list of values as input and returns a new list with two sublists containing the original values.

comb <- function(x, ...) {
  lapply(seq_along(x),
         function(i) c(x[[i]], lapply(list(...), function(y) y[[i]])))
}

We then pass this custom comb function to the .combine argument of the foreach loop using the ... notation. This tells the foreach package to use our custom comb function to combine and transform the output.

Here’s the modified code:

oper <- foreach(i = 1:10, .combine = 'comb', .multicombine = TRUE,
                 .init = list(list(), list())) %dopar% {
  list(i + 2, i + 3)
}

In this example, we’ve defined the comb function and passed it to the .combine argument of the foreach loop. The output is then stored in two separate sublists: oper[[1]] and oper[[2]].

Implementation Details

When using the foreach package with parallel computing, there are several important implementation details to keep in mind:

Initialization: Before executing the loop, you need to initialize the cluster using registerDoParallel. This ensures that the cluster is properly set up and ready for use.
Combine Function: The .combine argument of the foreach loop allows you to specify how the output should be combined and transformed. You can define your own custom combine function or use a default one provided by the foreach package.
Multicombining: When using multicombining (i.e., .multicombine = TRUE), the combine function is applied to all sublists simultaneously, resulting in a single output list containing multiple sublists. This can be useful when working with large datasets or when you need to combine outputs from multiple loops.
Error Handling: When using parallel computing, it’s essential to handle errors properly. You can use the tryCatch function to catch and handle any errors that occur during execution.

Conclusion

Returning multiple outputs from a .dopar loop in R can be challenging due to the parallel nature of the computation. However, by using the combine function provided by the foreach package, you can specify how the output should be combined and transformed. By following best practices and implementing details such as initialization, combine functions, multicombining, and error handling, you can efficiently and effectively work with parallel computing in R.

Last modified on 2023-05-30