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It’s tough to argue with R as a high-quality, cross-platform, open source statistical software product—unless you’re in the business of crunching Big Data. This concise book introduces you to several strategies for using R to analyze large datasets, including three chapters on using R and Hadoop together. You’ll learn the basics of Snow, Multicore, Parallel, Segue, RHIPE, and Hadoop Streaming, including how to find them, how to use them, when they work well, and when they don’t.
With these packages, you can overcome R’s single-threaded nature by spreading work across multiple CPUs, or offloading work to multiple machines to address R’s memory barrier.
- Snow: works well in a traditional cluster environment
- Multicore: popular for multiprocessor and multicore computers
- Parallel: part of the upcoming R 2.14.0 release
- R+Hadoop: provides low-level access to a popular form of cluster computing
- RHIPE: uses Hadoop’s power with R’s language and interactive shell
- Segue: lets you use Elastic MapReduce as a backend for lapply-style operations
- Sales Rank: #721011 in Books
- Published on: 2011-11-05
- Released on: 2011-11-02
- Original language: English
- Number of items: 1
- Dimensions: 9.19" h x .27" w x 7.00" l, .47 pounds
- Binding: Paperback
- 126 pages
About the Author
Q Ethan McCallum is a consultant, writer, and technology enthusiast, though perhaps not in that order. His work has appeared online on The O’Reilly Network and Java.net, and also in print publications such as C/C++ Users Journal, Doctor Dobb’s Journal, and Linux Magazine. In his professional roles, he helps companies to make smart decisions about data and technology.
Stephen Weston has been working in high performance and parallelcomputing for over 25 years. He was employed at Scientific Computing Associates in the 90's, working on the Linda programming system, invented by David Gelernter. He was also a founder of Revolution Computing, leading the development of parallel computing packages for R, including nws, foreach, doSNOW, and doMC. He works at Yale University as an HPC Specialist.
Most helpful customer reviews
8 of 8 people found the following review helpful.
Great introductions to 6 approaches to distributed computing
By Joshua Ulrich
You have a problem: R is single-threaded, but your code would be faster if it could simultaneously run on more than one core. You have access to a cluster and/or your computer has multiple cores. Parallel R, by Q. Ethan McCallum and Stephen Weston, can help you put this extra computing power to use. The review on my blog ([...]) has several useful links.
The book describes 6 approaches to distributed computing:
1) snow
The chapter starts by showing you how to create a socket cluster on a single machine (later sections discuss MPI clusters, and socket clusters of several machines). Then a section describes how to initialize workers, with a later section giving a slightly advanced discussion on how functions are serialized to workers.
There's a great demonstration (including graphs) of why/when you should use clusterApplyLB instead of clusterApply. There's also a fantastic discussion on potential I/O issues (probably one of the most surprising/confusing issues to people new to distributed computing) and how parApply handles them. Then the authors provide a very useful parApplyLB function.
There are a few (but very important!) paragraphs on random number generation using the rsprng and rlecuyer packages.
2) multicore
The chapter starts by noting that the multicore package only works on a single computer running a POSIX compliant operating system (i.e. most anything except Windows).
The next section describes the mclapply function, and also explains how mclapply creates a cluster each time it's called, why this isn't a speed issue, and how it is actually beneficial. The next few sections describe some of the optional mclapply arguments, and how you can achieve load balancing with mclapply. A good discussion of pvec, parallel, and collect functions follow.
There are some great tips on how to use the rsprng and rlecuyer packages for random number generation, even though they aren't directly supported by the multicore package. The chapter concludes with a short, but effective, description of multicore's low-level API.
3) parallel (comes with R >= 2.14.0)
The chapter starts by noting that the parallel package is a combination of the snow and multicore packages. This chapter is relatively short, since those two packages were covered in detail over the prior two chapters. Most of the content discusses the implementation differences between parallel and snow/multicore.
4) R+Hadoop
There's a full chapter primer on Hadoop and MapReduce, for those who aren't familiar with the software and concept. The chapter ends with an introduction to Amazon's EC2 and EMR services, which significantly lower the barrier to using Hadoop.
The chapter on R+Hadoop is very little R and mostly Hadoop. This is because Hadoop requires more setup than the other approaches. You will need to do some work on the command line and with environment variables.
There are three examples; one Hadoop streaming and two using the Java API (which require writing/modifying some Java code). The authors take care to describe each block of code in all the examples, so it's accessible to those who haven't written Java.
5) RHIPE
Using three examples, this chapter provides a thorough treatment of how to use RHIPE to abstract-away a lot of the boilerplate code required for Hadoop. Everything is done in R. As with the Hadoop chapter, the authors describe each block of code.
RHIPE does require a little setup: it must be installed on your workstation and all cluster nodes. In the examples, the authors describe how RHIPE allows you to transfer R objects between Map and Reduce phases, and they mention the RHIPE functions you can use to manipulate HDFS data.
6) segue
This is a very short chapter because the segue package has very narrow scope: using Amazon's EMR service in two lines of code!
Final thoughts:
I would recommend this book to someone who is looking to move beyond the most basic distributed computing solutions. The authors are careful to point you in the right direction and warn you of potential pitfalls of each approach.
All but the most basic setups (e.g. a socket cluster on a single machine) will require some familiarity with the command line, environment variables, and networking. This isn't the fault of the authors or any of the approaches... parallel computing just isn't that easy.
I really expected to see something on using foreach, especially since Stephen Weston has done work on those packages. It is mentioned briefly at the end of the book, so maybe it will appear in later editions.
3 of 3 people found the following review helpful.
Just an overview: too little (or too much), not just right :-(
By Dennis
Adding 300pp or so would be very helpful. This book does not cover enough ground for sophisticated, statistics literate beginners in R (like me) and I think that less of it would probably be enough for people who know more about R and 'big data"tools.
I would pay many tenfolds the price for more information in this book. The author is definitely an expert: I hope he writes the right book soon as there is a market for it.
R is a great tool and many of us are very interested in parallel --but this book for some will be just an appetizer.
1 of 1 people found the following review helpful.
Four Stars
By Y
As a starter of parallel in R, this book does not really explain clearly.
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