Parallelizing Computations with Multicore and MApply in R

Understanding multicore and mapply

In R, the multicore package provides a convenient way to parallelize computations on multiple CPU cores. However, when working with this library, many users find themselves struggling to achieve the same level of vectorization as their base R code.

One common issue arises when trying to apply a function to multiple values in parallel using mclapply. While sapply is an excellent tool for achieving this in serial mode, its equivalent in the multicore package doesn’t seem to exist out of the box. In this post, we’ll delve into the details of how R’s parallel computing works and explore alternatives to sapply that can be used with mclapply.

The mapply function

In base R, mapply (short for “multi-apply”) is a function that applies a given function to multiple arguments in a specified order. It’s a powerful tool for vectorizing operations, but its behavior can be tricky to understand at first.

For example, consider the following code:

f <- function(x, y) x + y
x <- c(1, 2, 3)
y <- c(4, 5, 6)
mapply(f, x, y)

This will output c(5 7 9).

However, if we try to apply the same function to multiple values in parallel using mclapply, we’ll encounter a problem:

library(multicore)
f <- function(x) x
x <- c(1:8)
mclapply(f, x)

This will output c(1 2 3 4 5 6 7 8).

Notice that the results are not vectorized as expected. This is because mapply in multicore doesn’t have a built-in equivalent to the SIMPLIFY argument found in base R’s mapply. Without this argument, the function returns a list where each element corresponds to a single value from the original input.

mcmapply: A parallel alternative

Luckily, there is an alternative to sapply that can be used with mclapply: mcmapply.

In base R, mcmapply applies a given function to multiple arguments in column-wise order. This means it treats each argument as a vector and applies the function to corresponding elements.

For example:

f <- function(x, y) x + y
x <- c(1 2 3)
y <- c(4 5 6)
mcmapply(f, x, y)

This will output c(5 7 9).

Now, let’s see how we can use mcmapply with multicore. We’ll apply the same function to multiple values in parallel using mclapply and compare the results:

library(parallel)
f <- function(x) x
x <- c(1:8)
mcmapply(f, x)

This will output c(1 2 3 4 5 6 7 8).

Notice that the result is now vectorized as expected. This is because mcmapply applies the function in column-wise order by default.

Conclusion

In conclusion, while there isn’t a direct equivalent to sapply in the multicore package, we can use mcmapply to achieve similar results. By understanding how mcmapply works and applying it correctly, we can efficiently parallelize computations on multiple CPU cores.

Common pitfalls

One common pitfall when working with mclapply is forgetting to convert the output from a list back into a vector using unlist. For example:

library(parallel)
f <- function(x) x
x <- c(1:8)
mclapply(f, x)

This will output c(1 2 3 4 5 6 7 8).

However, if we don’t use unlist, the results will be returned as a list:

library(parallel)
f <- function(x) x
x <- c(1:8)
mclapply(f, x)

This will output $``1$...$8$.

To avoid this pitfall, always remember to use unlist when working with the output of mclapply.

Alternative approaches

While mcmapply is an excellent tool for parallelizing computations on multiple CPU cores, there are other alternatives worth exploring. For example:

Parallel::pmap: This function applies a given function to each element of a vector in parallel and returns the results as a list. We can use this function to achieve similar results to sapply using multicore.
Parallel::foreach: This function applies a given function to multiple values in parallel, but it requires us to iterate over the input explicitly.

Conclusion

In conclusion, while there isn’t a direct equivalent to sapply in the multicore package, we can use various alternatives such as mcmapply, pmap, and foreach to achieve similar results. By understanding how these functions work and applying them correctly, we can efficiently parallelize computations on multiple CPU cores.

Additional considerations

SIMPLIFY argument: The SIMPLIFY argument in base R’s mapply applies the function to each element of a vector separately. This means it performs an element-wise operation. If we want to perform a vectorized operation, we need to use mcmapply or equivalent functions.
column-wise vs. row-wise: The order in which arguments are applied depends on the type of function used. For example, if we use mcmapply, it applies the function column-wise by default.

Common use cases

Here are some common use cases for multicore and mapply:

Data processing: When working with large datasets, parallelizing computations using multicore can significantly improve performance.
Machine learning: Many machine learning algorithms rely on vectorized operations to achieve efficient computation. Using multicore can help accelerate these computations.

Troubleshooting

If you encounter issues while working with multicore, here are some troubleshooting tips:

Check for errors: Make sure that there are no errors in your code, as these can cause unexpected behavior.
Verify input sizes: Ensure that the input sizes match between different parts of the code to avoid mismatched results.

Future work

The multicore package is an excellent tool for parallelizing computations on multiple CPU cores. However, there are many potential areas for future improvement:

Improved performance: Further optimization could help improve the performance of mclapply and other functions in the package.
New features: Additional features could be added to support more complex use cases or to provide better integration with other packages.

Example code

Here is an example code snippet that demonstrates how to use multicore and mapply together:

library(parallel)
f <- function(x) x^2
x <- 1:8
result <- mcmapply(f, x)
print(result)

This will output the squared values of each element in the vector x.

Last modified on 2024-04-10