Understanding Lazy Evaluation in R with Parallel Computing: The Impact of Lazy Evaluation on Variable Behavior.

Understanding Lazy Evaluation in R with Parallel Computing

Introduction

In the realm of parallel computing, especially when working with packages like parallel in R, it’s not uncommon to encounter situations where variables passed as function arguments don’t seem to be behaving as expected. The question at hand revolves around why variables within a function passed as an argument do not pass to the cluster when using parallel computing. To delve into this, we must first understand the concept of lazy evaluation and its implications in R.

What is Lazy Evaluation?

Lazy evaluation is a programming paradigm that defers the actual computation of an expression until its value is actually needed. This contrasts with eager evaluation, where the entire computation occurs at compile time or at the beginning of execution. In R, many functions follow the principle of lazy evaluation by default, meaning that arguments are only evaluated when their values are required.

The Problem with Variables in Function Arguments

The question’s example demonstrates a scenario where the function test seems to work correctly when called without any arguments (test()), but fails when an argument is provided (test(x)). This anomaly can be traced back to how R handles lazy evaluation and its interaction with parallel computing.

When you call test(), the function is executed in the global environment, and variables defined within it are indeed available. However, when you pass x as an argument (test(x)), the situation changes because of how lazy evaluation works in this context. The argument a = 1 isn’t immediately evaluated; instead, its value becomes part of the function’s environment. When parSapply() executes the function across multiple cores within a cluster, it doesn’t have access to x until after result has been calculated.

Understanding Clustering in R

In parallel computing with R’s parallel package, clustering refers to dividing work into multiple tasks that are executed concurrently. The makeCluster() function creates a pool of worker processes (cores), and parSapply() uses this cluster to execute the specified number of applications across each core.

Fixing the Issue

The solution to this problem lies in the application of force() to ensure immediate evaluation of a = 1. When you add force(a) before calculating result, you’re telling R to evaluate the expression for a right away, making its value available within the function’s environment.

test <- function(a = 1) {
    no_cores <- detectCores() - 1
    clust <- makeCluster(no_cores)
    force(a)
    result <- parSapply(clust, 1:10, function(x){ a + x })
    stopCluster(clust)
    return(result)
}

x = 1
test(x)

Additional Considerations and Implications

This scenario highlights the importance of understanding how R’s lazy evaluation interacts with parallel computing. While force() provides a straightforward solution to this particular issue, other strategies could be employed depending on the specific context.

For instance, if you need to ensure that variables are always evaluated in certain situations (e.g., to avoid potential errors due to undefined variables), consider using explicit assignments or functions like eval(). However, these approaches should be carefully balanced with performance considerations and code readability, as they can introduce additional complexity.

Conclusion

Variables passed as arguments to a function within parallel computing environments in R indeed face challenges due to lazy evaluation. By employing the force() function to explicitly evaluate variables before their use, we can ensure that values are accessible across multiple cores. Understanding this concept not only resolves specific issues but also contributes to more robust and predictable R code.