Parallel Processing in R: Performance Comparison of Parallel and Sequential Techniques

When working with large datasets, computational efficiency becomes critical. In this post, we will explore different methods of parallel processing in R to improve execution time, leveraging the parallel, foreach, and future packages. We'll also compare sequential and parallel strategies for linear modeling and matrix operations.

1. Using the parallel Package

The parallel package allows you to distribute operations across multiple CPU cores. Here, we use parApply to calculate the row-wise mean of a large matrix in parallel.

# Load necessary libraries
library(parallel)
library(doParallel)

# Create a large matrix with random numbers
mat = matrix(rnorm(10000000),ncol=1000)

# Measure time for parallel computation
system.time({
    # Create a cluster with 4 CPU cores
    clust = makeCluster(4)
    
    # Apply the mean function to each row of the matrix in parallel
    Test = parApply(clust, mat, 1, mean)
    
    # Stop the cluster after use
    stopCluster(clust)
})
    

This code utilizes 4 cores to compute the row means of a 10-million element matrix. By distributing the work across the cores, we achieve a significant reduction in computation time, especially for large-scale data. The system.time() function measures the total runtime.

2. Using the foreach Package

Next, we employ the foreach package for parallelization. This package is highly flexible and works well with the %dopar% operator for distributing tasks across multiple cores.

# Load necessary libraries
library(foreach)
library(doParallel)

# Register 4 cores for parallel processing
registerDoParallel(cores = 4)

# Measure time for parallel computation
system.time({
    # Calculate the row means in parallel using foreach
    Test2 = foreach(i = 1:nrow(mat), .combine = c) %dopar% mean(mat[i, ])
})
    

The foreach package iterates over each row of the matrix, calculating the mean for each row in parallel across 4 cores. This approach is slightly different from the previous one, as it explicitly uses loops, but it also improves efficiency significantly.

3. Using the future Package

The future package allows you to switch between sequential and parallel execution plans with ease. Here, we first define a sequential plan, then switch to a parallel one using multisession. We use the %<-% operator, which enables asynchronous evaluation of expressions in parallel.

# Load the future package
library(future)

# Set the execution plan to sequential initially
plan("sequential")

# Switch to parallel execution using multiple sessions
plan("multisession")

# Generate synthetic data
n <- 10000000
x <- rnorm(n)
y <- 2 * x + 0.2 + rnorm(n)
w <- 1 + x ^ 2

# Measure the time for parallelized model fitting
system.time({
    # Fit multiple linear models in parallel
    fitA %<-% lm(y ~ poly(x, 3), weights = w)       # Model with offset
    fitB %<-% lm(y ~ poly(x, 3) - 1, weights = w)   # Model without offset
    fitC %<-% { w <- 1 + abs(x); lm(y ~ poly(x, 3), weights = w) } # Modified weights
})

# Print the results of the models
print(fitA)
print(fitB)
print(fitC)
    

In this example, we fit three linear models in parallel, each with a different configuration of predictors and weights. The %<-% operator, provided by future, allows for asynchronous evaluation, making the modeling process faster.

4. Model Comparison with BIC

Finally, we use the future package again to fit different models on the famous "cars" dataset. After fitting the models, we compare their Bayesian Information Criterion (BIC) scores to determine which model fits the data best.

# Load the necessary package
library(future)

# Set the plan to parallel execution
plan("multisession")

# Import the cars dataset
data(cars)

# Fit multiple models with varying complexity
model1 %<-% lm(dist ~ 1, data = cars)           # Intercept model
model2 %<-% lm(dist ~ speed, data = cars)       # Linear model
model3 %<-% lm(dist ~ poly(speed, 2), data = cars) # Quadratic model
model4 %<-% lm(dist ~ poly(speed, 3), data = cars) # Cubic model

# Compare models using Bayesian Information Criterion (BIC)
BIC(model1, model2, model3, model4)
    

We fit four different models, ranging from simple to complex, and compute their BIC values. The model with the lowest BIC is typically preferred, as it balances model fit and complexity.

Conclusion

Parallel processing techniques in R, such as those offered by the parallel, foreach, and future packages, can significantly reduce computation time for large datasets. Each package has its own strengths, and choosing the right one depends on your specific use case. Whether you are applying functions to matrices or fitting complex models, these tools provide a flexible and powerful framework for optimizing performance in R.