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Parallel R
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Parallel R
by Stephen Weston, Q. Ethan McCallum
Parallel R
Parallel R
A Note Regarding Supplemental Files
Preface
Conventions Used in This Book
Using Code Examples
Safari® Books Online
How to Contact Us
Acknowledgments
Q. Ethan McCallum
Stephen Weston
1. Getting Started
Why R?
Why Not R?
The Solution: Parallel Execution
A Road Map for This Book
What We’ll Cover
Looking Forward…
What We’ll Assume You Already Know
In a Hurry?
snow
multicore
parallel
R+Hadoop
RHIPE
Segue
Summary
2. snow
Quick Look
How It Works
Setting Up
Working with It
Creating Clusters with makeCluster
Parallel K-Means
Initializing Workers
Load Balancing with clusterApplyLB
Task Chunking with parLapply
Vectorizing with clusterSplit
Load Balancing Redux
Functions and Environments
Random Number Generation
snow Configuration
Installing Rmpi
Executing snow Programs on a Cluster with Rmpi
Executing snow Programs with a Batch Queueing System
Troubleshooting snow Programs
When It Works…
…And When It Doesn’t
The Wrap-up
3. multicore
Quick Look
How It Works
Setting Up
Working with It
The mclapply Function
The mc.cores Option
The mc.set.seed Option
Load Balancing with mclapply
The pvec Function
The parallel and collect Functions
Using collect Options
Parallel Random Number Generation
The Low-Level API
When It Works…
…And When It Doesn’t
The Wrap-up
4. parallel
Quick Look
How It Works
Setting Up
Working with It
Getting Started
Creating Clusters with makeCluster
Parallel Random Number Generation
Summary of Differences
When It Works…
…And When It Doesn’t
The Wrap-up
5. A Primer on MapReduce and Hadoop
Hadoop at Cruising Altitude
A MapReduce Primer
Thinking in MapReduce: Some Pseudocode Examples
Calculate Average Call Length for Each Date
Number of Calls by Each User, on Each Date
Run a Special Algorithm on Each Record
Binary and Whole-File Data: SequenceFiles
No Cluster? No Problem! Look to the Clouds…
The Wrap-up
6. R+Hadoop
Quick Look
How It Works
Setting Up
Working with It
Simple Hadoop Streaming (All Text)
Streaming, Redux: Indirectly Working with Binary Data
The Java API: Binary Input and Output
Processing Related Groups (the Full Map and Reduce Phases)
When It Works…
…And When It Doesn’t
The Wrap-up
7. RHIPE
Quick Look
How It Works
Setting Up
Working with It
Phone Call Records, Redux
Tweet Brevity
More Complex Tweet Analysis
When It Works…
…And When It Doesn’t
The Wrap-up
8. Segue
Quick Look
How It Works
Setting Up
Working with It
Model Testing: Parameter Sweep
When It Works…
…And When It Doesn’t
The Wrap-up
9. New and Upcoming
doRedis
RevoScale R and RevoConnectR (RHadoop)
cloudNumbers.com
About the Authors
Copyright
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A Note Regarding Supplemental Files
Parallel R
Q. Ethan McCallum
Stephen Weston
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O’Reilly Media
Beijing ⋅ Cambridge ⋅ Farnham ⋅ Köln ⋅ Sebastopol ⋅ Tokyo
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