Executing Multiple Scripts and Substracting Results

Introduction

In this article, we will explore the process of executing multiple scripts in parallel using R’s parLapply function. We will also discuss how to handle the results of these scripts and subtract them as required.

R’s parallel processing capabilities allow us to run multiple scripts simultaneously, making it an efficient way to perform computationally intensive tasks. In this article, we will focus on executing multiple scripts in parallel using R’s parLapply function.

Defining Multiple Scripts

Before we can execute multiple scripts in parallel, we need to define them. Let’s consider two simple scripts, Script1 and Script2, which are stored in separate files (e.g., Script1.R and Script2.R).

Script1.R

# Script1.R

# Define a vector of values
a <- c(11, 12, 13, 14)

# Print the vector
print(a)

Script2.R

# Script2.R

# Define a vector of values
b <- c(1, 2, 3, 4)

# Print the vector
print(b)

Loading R’s parallel processing library

To execute multiple scripts in parallel, we need to load R’s foreach and doParallel packages. These packages provide a convenient interface for parallel processing.

# Install and load required packages
install.packages("foreach")
install.packages("doParallel")

library(foreach)
library(doParallel)

Defining Multiple Scripts to be Executed in Parallel

Now that we have defined our scripts, let’s modify our main script to execute them in parallel using parLapply.

# Define the vectors of values for Script1 and Script2
scripts <- c("Script1.R", "Script2.R")

# Detect the number of available cores
NbCores <- detectCores() - 1

# Create a cluster with the specified number of cores
cl <- makeCluster(NbCores)

# Execute Script1 and Script2 in parallel using parLapply
parLapply(cl, scripts, function(script) {
  # Load the script to be executed
  source(paste0scriptpath, script))

  # Run the script
  res <- system(paste0(scriptpath, script), wait = FALSE)
  
  # Save the result to an RDS file
  saveRDS(res, paste0(scriptpath, "res.rds"))
})

# Stop the cluster when finished
stopCluster(cl)

Running Multiple Scripts in Parallel

Now that we have defined our scripts and loaded the necessary packages, let’s run them in parallel using parLapply.

# Run the multiple script in parallel using parLapply

## Step 1: Define the script vectors
scripts <- c("Script1.R", "Script2.R")

## Step 2: Detect the number of available cores
NbCores <- detectCores() - 1

## Step 3: Create a cluster with the specified number of cores
cl <- makeCluster(NbCores)

## Step 4: Execute Script1 and Script2 in parallel using parLapply
parLapply(cl, scripts, function(script) {
  # Load the script to be executed
  source(paste0("scripts", script))

  # Run the script
  res <- system(paste0("scripts", script), wait = FALSE)
  
  # Save the result to an RDS file
  saveRDS(res, paste0("scripts", script), "res.rds"))
})

# Stop the cluster when finished
stopCluster(cl)

Understanding the Results

After running our scripts in parallel, we can now access their results. Let’s assume that Script1 returns a vector of values a and Script2 returns a vector of values b. We want to subtract these vectors as follows:

a - b = 10, 10, 10, 10

To achieve this, we need to load the RDS files generated by each script.

# Load the RDS file for Script1
res1 <- readRDS("scripts/Script1.res.rds")

# Load the RDS file for Script2
res2 <- readRDS("scripts/Script2.res.rds")

Subtracting the Results

Now that we have loaded the results, let’s subtract them as required.

# Subtract the results
result <- res1 - res2

# Print the result
print(result)

Troubleshooting

In this section, we will discuss common issues and troubleshooting steps when executing multiple scripts in parallel using parLapply.

When loading an RDS file generated by a script, you might encounter the error “127”. This is because the system function in the script is returning a non-zero exit code.

To fix this issue, check your scripts to ensure that they are not returning any errors. Make sure that all necessary packages and libraries are loaded before running the scripts.

Error: Cluster Not Started

When you encounter an error stating that the cluster has not been started, it might be because of incorrect parameters passed to makeCluster or stopCluster.

To fix this issue, ensure that the correct number of cores is specified when creating the cluster. You can use the detectCores function to detect the available cores.

Conclusion

Executing multiple scripts in parallel using R’s parLapply function allows us to perform computationally intensive tasks efficiently. By following the steps outlined in this article, you should now be able to run your own scripts in parallel and handle their results as needed.

In conclusion, parLapply is a powerful tool for executing multiple scripts in parallel. It can significantly improve performance when working with large datasets or performing computationally intensive tasks. However, it requires careful planning and execution to ensure that errors are minimized and results are accurately calculated.

Last modified on 2024-11-20