Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    big data analytics in transporation
    Turning Data Into Decisions: How Analytics Improves Transportation Strategy
    3 Min Read
    sales and data analytics
    How Data Analytics Improves Lead Management and Sales Results
    9 Min Read
    data analytics and truck accident claims
    How Data Analytics Reduces Truck Accidents and Speeds Up Claims
    7 Min Read
    predictive analytics for interior designers
    Interior Designers Boost Profits with Predictive Analytics
    8 Min Read
    image fx (67)
    Improving LinkedIn Ad Strategies with Data Analytics
    9 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Parallel Processing in R for Windows
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > R Programming Language > Parallel Processing in R for Windows
R Programming Language

Parallel Processing in R for Windows

DavidMSmith
DavidMSmith
3 Min Read
SHARE

The doSMP package (and its companion package, revoIPC), previously bundled only with Revolution R, is now available on CRAN for use with open-source R under the GPL2 license.

The doSMP package (and its companion package, revoIPC), previously bundled only with Revolution R, is now available on CRAN for use with open-source R under the GPL2 license.

In short, doSMP makes it easy to do SMP parallel processing on a Windows box with multiple processors. (It works on Mac and Linux too, but it’s been relatively easy to do parallel processing on those systems for a while with doMC/multicore package combo. Windows, not so much.) Basically, you tell it how many processors you have, write a loop using the foreach function, and the iterations of the loop run in parallel, using multiple processors. For embarassingly parallel problems like simulations and optimizations and such, if you have 2 processors you can get close to halving the processing time; reduce it to near 25% with 4 processors, and so on. (Whether these are true, independent CPUs or cores within a processor matters a little, but not much.)

More Read

data science online education
Data Science Education Gets Personal
Data Mining Interview: Luis Torgo
Big Data: Will Open Source Software Challenge BI & Analytics Software Vendors
Who Is Winning the Real Cyber War?
Fantasy Football Modeling with R

You can see some examples in the doSMP vignette, from which I adapted the following example. Suppose you want to bootstrap parameter estimates from a logistic regression using 1000 samples:

x <- iris[which(iris[, 5] != "setosa"), c(1, 5)]
trials <- 10000
chunkSize <- ceiling(trials/getDoParWorkers())
smpopts <- list(chunkSize = chunkSize)
r <- foreach(icount(trials), .combine = cbind, .options.smp = smpopts)
  %dopar% {
  ind <- sample(100, 100, replace = TRUE)
  result1 <- glm(x[ind, 2] ~ x[ind, 1], family = binomial(logit))
  coefficients(result1)
}

Created by Pretty R at inside-R.org

Note the use of foreach to run the bootstrap models in parallel. On a 4-core machine, you could reduce processing time from 104 seconds to 57 seconds compared to using a regular for loop. Not quite a fourfold reduction, but a significant reduction in time nonetheless. (Tip: if you’re using Revolution R, you might want to try turning off MKL multithreading when using doSMP/foreach, to avoid contention between the small-grain threading of MKL, and the large-grain parallelism of foreach.)

I’ve written about foreach several times before (here, here and here for example) using other parallel backends like doMC and doSNOW. Now you can use those same examples on Windows with open-source R and the doSMP package.

doSMP package: Getting Started with doSMP and foreach

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

big data analytics in transporation
Turning Data Into Decisions: How Analytics Improves Transportation Strategy
Analytics Big Data Exclusive
AI and fund manager software
AI And The Acceleration Of Information Flows From Fund Managers To Investors
Artificial Intelligence Exclusive
sales and data analytics
How Data Analytics Improves Lead Management and Sales Results
Analytics Big Data Exclusive
ai in marketing
How AI and Smart Platforms Improve Email Marketing
Artificial Intelligence Exclusive Marketing

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

Big Data Statistics in the Search for a Cure for MS

0 Min Read

Building Diversified Portfolios with R

4 Min Read

Revolution Analytics Partners with Cloudera

4 Min Read

The Environmental Performance Index, Visualized with R

2 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

ai in ecommerce
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence
ai chatbot
The Art of Conversation: Enhancing Chatbots with Advanced AI Prompts
Chatbots

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?