Building a socket cluster is a way to execute parallel computation using the multiple cores on your computer via sockets. Sockets are simply a mechanism with which multiple processes or applications running on your computer (or different computers, for that matter) can communicate with each other.
To build a socket cluster in R, you can use the `makeCluster()` function. This function takes an integer argument that specifies the number of cores you want to use in your cluster. For example, to create a cluster with 4 cores, you would use the following code:
cl <- makeCluster(4)
The `cl` object is an abstraction of the entire cluster and is what you'll use to indicate to the various cluster functions that you want to do parallel computation.
Once you've created your cluster, you can use the `parLapply()` function to do an lapply() operation over the cluster. For example, the following code would use parLapply() to run a median bootstrap on a dataset:
med.boot <- parLapply(cl, 1:5000, function(i) {
xnew <- sample(sulf, replace = TRUE)
median(xnew)
})
The `med.boot` object will contain the results of the bootstrap, one for each core in the cluster.
Once you've finished using your cluster, it's important to clean up and stop the child processes. You can do this with the `stopCluster()` function:
stopCluster(cl)
Here is a complete example of building and using a socket cluster in R:
library(parallel)
# Create a cluster with 4 cores
cl <- makeCluster(4)
# Import the sulfate data
data(sulf)
# Export the sulfate data to the cluster
clusterExport(cl, "sulf")
# Run a median bootstrap on the sulfate data
med.boot <- parLapply(cl, 1:5000, function(i) {
xnew <- sample(sulf, replace = TRUE)
median(xnew)
})
# Collapse the results of the bootstrap into a vector
med.boot <- unlist(med.boot)
# Calculate the 2.5th and 97.5th percentiles of the bootstrap distribution
quantile(med.boot, c(0.025, 0.975))
# Stop the cluster
stopCluster(cl)
