Understanding Parallel Processing with foreach in R

Parallel processing has become an essential tool for many data-intensive tasks, particularly in scientific computing and machine learning. The foreach package in R provides a convenient way to parallelize loops, making it easier to take advantage of multiple CPU cores or even distributed clusters. In this article, we’ll delve into the world of parallel processing with foreach, focusing on a specific issue that may arise when using this function.

Background and Terminology

Before diving into the topic, let’s briefly discuss some key concepts in parallel processing:

• Clustering: A process that splits tasks across multiple processors or nodes to take advantage of their capabilities.
• Parallel loops: Loops that execute simultaneously on multiple processors, speeding up computation time.
• Iteration variables: Variables within a loop that determine the iteration order.

Now, let’s examine the provided R code snippet and explore why it seems to be producing only results for i=1.

Examining the Provided Code Snippet

The given code uses the foreach package in combination with doMC for parallel processing:

library(doMC)
library(foreach)

# Set up the number of CPUs
number_of_cpus = 4

# Create a cluster and register it for parallel processing
cl <- makeCluster(number_of_cpus)
registerDoMC(cores = 4)

# Initialize the split_factors vector with data for i=1 to i=3
split_results2 <- 

  foreach(i = 1:3, .combine = rbind, .inorder = TRUE, mc.cores = 4) %dopar% {  

    # Extract the Split_factor based on the value of i  
    Split_factor = as.character(split_factors[1, i])

    # Update Data with the new values
    Data$Split_Factor = as.character(Data$Split_Factor)
    Data_new = Data[Data$Split_Factor == Split_factor,]

    # Call GetSplit function to process Data_new
    GetSplit(Data_new, Data_ind, num_vars, num_factors, r_jobs, probs)  
  } 

This code is designed to iterate over i values from 1 to 3 in parallel. However, the provided output seems to be truncated at only i=1.

The Issue: Understanding foreach Iteration Variables

The problem arises due to an incorrect usage of the iteration variable “mc.cores” in the foreach loop:

foreach(i=1:3, .combine=rbind, .inorder=TRUE, mc.cores=4) %dopar%  

Here, we have two iteration variables: i and “.combine”. The key to understanding this lies in how for-each works. For-each iterates over the specified variables in parallel.

The first variable in a foreach loop is used as the input to the main body of the loop function, and each iteration of that body processes the current value of that variable.

When you specify an additional argument like “.combine” or “.inorder”, it’s applied to all iterations of the function where it appears. In this case, “.combine = rbind” is a separate operation done on every single row produced by foreach loop and not inside loop itself.

But here’s the catch: when we do “.inorder=TRUE”, it means that the iteration variable will be evaluated in order (from start to finish). However, there’s another argument involved - “.mc.cores”.

This value is used for parallel processing, which means it limits the number of CPUs used by foreach loop. However, this value should only be present when you’re actually using multiple cores.

When we remove the “.mc.cores” argument, the foreach loop will not create separate processes based on that variable because its value was less than 1.

Here’s how for-each behaves with different iteration variables:

foreach(i=1:3, j=10) %dopar% {
    c(i, j)
}

foreach(i=1:3, j=10:100) %dopar% {
    c(i, j)
}

In the first case, since i has fewer values than j, all iterations will be processed with i.

However, in the second case, it’s the other way around because j has more values. The loop is then limited by the variable i which is smaller.

Why foreach Doesn’t Have a “mc.cores” Argument

The reason we can’t use “.mc.cores” as an argument with foreach is that it would cause an error because foreach already processes all iteration variables simultaneously in parallel.

So, instead of using .mc.cores or any other arguments inside the foreach loop body, you should always declare them before the %dopar% keyword at the end. Here’s how to correct the original code:

library(doMC)
library(foreach)

# Set up the number of CPUs
number_of_cpus = 4

# Create a cluster and register it for parallel processing
cl <- makeCluster(number_of_cpus)
registerDoMC(cores = 4)

# Initialize the split_factors vector with data for i=1 to i=3
split_results2 <- 

  foreach(i = 1:3) %dopar% {
    # Extract the Split_factor based on the value of i  
    Split_factor = as.character(split_factors[1, i])

    # Update Data with the new values
    Data$Split_Factor = as.character(Data$Split_Factor)
    Data_new = Data[Data$Split_Factor == Split_factor,]

    # Call GetSplit function to process Data_new
    GetSplit(Data_new, Data_ind, num_vars, num_factors, r_jobs, probs)  
  } 

Removing Unnecessary Clusters

In addition to fixing the issue with “.mc.cores”, we should also remove any unnecessary clusters from our code. The provided example creates a cluster and then immediately registers it for parallel processing.

# Create a cluster and register it for parallel processing
cl <- makeCluster(number_of_cpus)
registerDoMC(cores = 4)

However, if you want to create multiple clusters at once or handle different types of data in parallel, this can be useful. But in most cases, you won’t need to use the cluster itself.

# Register a single cluster for parallel processing
registerDoMC(cores = number_of_cpus)

Conclusion

In conclusion, when working with foreach loops and parallel processing in R, it’s essential to understand how iteration variables work and how to correctly use them. The key takeaways from this example are:

• Iterate over values using “foreach” instead of “for”.
• Remove any unnecessary arguments that might cause issues.
• Understand how to specify multiple variables for iteration.

By following these guidelines, you can write efficient and effective parallel code in R.

Last modified on 2025-03-14