OUTPUT
output
In this example, we create a cluster with 4 cores, register the cluster with the doParallel package, and then use foreach to parallelize the for loop. The code calculates the product of each value in the vector vec by 2, and stores the results in the variable result.
- Squaring numbers in parallel :-Suppose we have a vector of numbers and we want to square each number in parallel. Here’s how we can do it with foreach:
R
library(foreach)library(doParallel)# Create a cluster with 4 corescl <- makeCluster(4)registerDoParallel(cl)# Define a vector of numbersvec <- 1:10# Parallelize the for loopresult <- foreach(i = 1:length(vec), .combine = c) %dopar% { vec[i]^2}# Stop the clusterstopCluster(cl)# Print the resultprint(result) |
The 1:100 notation produces an integer vector from 1 to 100, which is a vector of values.The for loop that comes after it is parallelized using the foreach() method. The loop loops through the values in the vector, multiplying each value by two as it goes. The outcomes of the parallel calculations are combined into a single vector using the.combine = c argument.The foreach() loop uses the %dopar% operator to indicate that the loop should run concurrently on all available cores.The result variable holds the output of the concurrent computation.The computation’s findings are shown using the print() function.
OUTPUT :
output
This code creates a cluster with 4 cores, defines a vector of numbers, and then parallelizes the for loop to square each number using the %dopar% operator. The .combine = c argument tells foreach to combine the results into a single vector. Finally, the code stops the cluster and prints the result.
- Finding the maximum of a list of matrices:
Suppose we have a list of matrices and we want to find the maximum value across all the matrices in parallel. Here’s how we can do it with foreach:
R
library(foreach)library(doParallel)# Create a cluster with 4 corescl <- makeCluster(4)registerDoParallel(cl)# Define a list of matriceslst <- list(matrix(1:9, ncol = 3), matrix(10:18, ncol = 3), matrix(19:27, ncol = 3))# Parallelize the for loopresult <- foreach(mat = lst, .combine = max) %dopar% { max(mat)}# Stop the clusterstopCluster(cl)print(lst)# Print the resultprint(result) |
OUTPUT :
output
foreach parallel computing using external packages
Parallel computing is a method of breaking down large computational tasks into smaller ones that can be executed simultaneously on multiple processors or cores, leading to faster results. The foreach loop is a popular construct in R programming, which allows users to iterate over a list or vector of elements. In parallel computing, foreach can be used to execute code in parallel across multiple processors or cores, leading to significant speedups in performance. In this article, we will discuss the concepts related to foreach parallel computing and the steps needed to use it, along with some good examples.
CONCEPTS:
The concept behind parallel computing is to break down a large computational task into smaller sub-tasks and execute them simultaneously across multiple processors or cores. In R programming, parallel computing can be achieved using the parallel package, which provides support for multiple types of parallelism, including fork-based, socket-based, and cluster-based parallelism.
The foreach loop is another popular construct in R programming that allows users to iterate over a list or vector of elements. The foreach package provides support for parallel computing using foreach loops, which allows users to execute code in parallel across multiple processors or cores.
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