A gentle introduction to parallel computing in R

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Let’s talk about the use and benefits of parallel computation in R.


IBM’s Blue Gene/P massively parallel supercomputer (Wikipedia).

Parallel computing is a type of computation in which many calculations are carried out simultaneously.”

Wikipedia quoting: Gottlieb, Allan; Almasi, George S. (1989). Highly parallel computing

The reason we care is: by making the computer work harder (perform many calculations simultaneously) we wait less time for our experiments and can run more experiments. This is especially important when doing data science (as we often do using the R analysis platform) as we often need to repeat variations of large analyses to learn things, infer parameters, and estimate model stability.

Typically to get the computer to work a harder the analyst, programmer, or library designer must themselves work a bit hard to arrange calculations in a parallel friendly manner. In the best circumstances somebody has already done this for you:

  • Good parallel libraries, such as the multi-threaded BLAS/LAPACK libraries included in Revolution R Open (RRO, now Microsoft R Open) (see here).
  • Specialized parallel extensions that supply their own high performance implementations of important procedures such as rx methods from RevoScaleR or h2o methods from h2o.ai.
  • Parallelization abstraction frameworks such as Thrust/Rth (see here).
  • Using R application libraries that dealt with parallelism on their own (examples include gbm, boot and our own vtreat). (Some of these libraries do not attempt parallel operation until you specify a parallel execution environment.)

In addition to having a task ready to “parallelize” you need a facility willing to work on it in a parallel manner. Examples include:

  • Your own machine. Even a laptop computer usually now has four our more cores. Potentially running four times faster, or equivalently waiting only one fourth the time, is big.
  • Graphics processing units (GPUs). Many machines have a one or more powerful graphics cards already installed. For some numerical task these cards are 10 to 100 times faster than the basic Central Processing Unit (CPU) you normally use for computation (see here).
  • Clusters of computers (such as Amazon ec2, Hadoop backends and more).

Obviously parallel computation with R is a vast and specialized topic. It can seem impossible to quickly learn how to use all this magic to run your own calculation more quickly.

In this tutorial we will demonstrate how to speed up a calculation of your own choosing using basic R. Continue reading A gentle introduction to parallel computing in R

Nina Zumel and John Mount part of R Day at Strata + Hadoop World in San Jose 2016

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Nina Zumel and I are honored to have been invited to be part of Strata + Hadoop World in San Jose 2016 R Day organized by RStudio and O’Reilly. Continue reading Nina Zumel and John Mount part of R Day at Strata + Hadoop World in San Jose 2016

Using Excel versus using R

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Here is a video I made showing how R should not be considered “scarier” than Excel to analysts. One of the takeaway points: it is easier to email R procedures than Excel procedures.

Win-Vector’s John Mount shows a simple analysis both in Excel and in R.

A save of the “email” linking to all code and data is here.

The theory is the recipient of the email already had R, RStudio and the required packages installed from previous use. The package install step is only needed once and is:


Then all the steps are (in a more cut/paste friendly format):

cars <- read.table('http://www.win-vector.com/dfiles/car.data.csv',header=TRUE,sep=',')
model <- rpart(rating ~ buying + maint + doors + persons + lug_boot + safety, data=cars, control=rpart.control(maxdepth=6))

Note, you would only have to install the packages once- not every time you run an analysis (which is why that command was left out).

Some programming language theory in R

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Let’s take a break from statistics and data science to think a bit about programming language theory, and how the theory relates to the programming language used in the R analysis platform (the language is technically called “S”, but we are going to just call the whole analysis system “R”).

Our reasoning is: if you want to work as a modern data scientist you have to program (this is not optional for reasons of documentation, sharing and scientific repeatability). If you do program you are going to have to eventually think a bit about programming theory (hopefully not too early in your studies, but it will happen). Let’s use R’s powerful programming language (and implementation) to dive into some deep issues in programming language theory:

  • References versus values
  • Function abstraction
  • Equational reasoning
  • Recursion
  • Substitution and evaluation
  • Fixed point theory

To do this we will translate some common ideas from a theory called “the lambda calculus” into R (where we can actually execute them). This translation largely involves changing the word “lambda” to “function” and introducing some parenthesis (which I think greatly improve readability, part of the mystery of the lambda calculus is how unreadable its preferred notation actually is).

Opus hyp
Recursive Opus (on a Hyperbolic disk)
Continue reading Some programming language theory in R

An R function return and assignment puzzle

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Here is an R programming puzzle. What does the following code snippet actually do? And ever harder: what does it mean? (See here for some material on the difference between what code does and what code means.)

f <- function() { x <- 5 }

In R version 3.2.3 (2015-12-10) -- "Wooden Christmas-Tree" the code appears to call the function f() and return nothing (nothing is printed). When teaching I often state that you should explicitly use a non-assignment expression as your return value. You should write code such as the following:

f <- function() { x <- 5; x }
## [1] 5

(We are showing an R output as being prefixed with ##.)

But take a look at the this:

f <- function() { x <- 5 }
## [1] 5

It prints! Read further for what is really going on.

NewImage Continue reading An R function return and assignment puzzle

Win-Vector news

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Just an update of what we have been up to lately at Win-Vector LLC, and a reminder of some of our current offerings. It has been busy lately (and that is good).

Our current professional service offerings continue to be data science consulting (helping companies extract value from their data and data infrastructure) and on-site corporate training. We have been honored to recently deliver our training to teams at Salesforce and Genentech. Continue reading Win-Vector news

Practical Data Science with R examples

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One of the big points of Practical Data Science with R is to supply a large number of fully worked examples. Our intent has always been for readers to read the book, and if they wanted to follow up on a data set or technique to find the matching worked examples in the project directory of our book support materials git repository.

Some readers want to work much closer to the sequence in the book. To make working along with book easier we extracted all book examples and shared them with our readers (in a Github directory, and a downloadable zip file, press “Raw” to download). The direct extraction from the book guarantees the files are in sync with our revised book. However there are trade-offs, sometimes (for legibility) the book mixed input and output without using R’s comment conventions. So you can’t always just paste everything. Also for a snippet to run you may need some libraries, data and results of previous snippets to be present in your R environment.

To help these readers we have added a new section to the book support materials: knitr markdown sheets that work all the book extracts from each chapter. Each chapter and appendix now has a matching markdown file that sets up the correct context to run each and every snippet extracted from the book. In principle you can now clone the entire zmPDSwR repository to your local machine and run all the from the CodeExamples directory by using the RStudio project in RunExamples. Correct execution also depens on having the right packages installed so we have also added a worksheet showing everything we expect to see installed in one place: InstallAll.Rmd (note some of the packages require external dependencies to work such as a C compiler, curl libraries, and a Java framework to run).

Sequential Analysis

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We here at Win-Vector LLC been working through an ad-hoc series about A/B testing combining elements of both operations research and statistical points of view.

Our most recent article was a dynamic programming solution to the A/B test problem. Explicitly solving such dynamic programs gets long and tedious, so you are well served by finding and introducing clever invariants to track (something better than just raw win-rates). That clever idea is called “sequential analysis” and was introduced by Abraham Wald (somebody we have written about before). If you have ever heard of a test plan such as “first process to get more than 30 wins ahead of the other is the one we choose” you have seen methods derived from Wald’s sequential analysis technique.

Wald’s famous airplane armor problem

In this “statistics as it should be” article we will discuss Wald’s sequential analysis. Continue reading Sequential Analysis

Wald’s sequential analysis technique

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Microsoft Revolution Analytics has just posted our latest article on A/B testing: Wald’s graphical sequential inspection procedure. It is a fun appreciation of a really cool procedure and I hope you check it out.

IMG 1692
Figure 14, Section 6.4.2, page 111, Abraham Wald, Sequential Analysis, Dover 2004 (reprinting a 1947 edition).