November 2015
Intermediate to advanced
328 pages
10h 12m
English
Content preview from Parallel Computing for Data Science
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Contents
Preface xix
Bio xxiii
1 Introduction to Parallel Processing in R 1
1.1 Recurring Theme: The Principle of Pretty Good Parallelism 1
1.1.1 Fast Enough . . . . . . . . . . . . . . . . . . . . . . 1
1.1.2 “R+X” . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 A Note on Machines . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Recurring Theme: Hedging One’s Bets . . . . . . . . . . . . 3
1.4 Extended Example: Mutual Web Outlinks . . . . . . . . . . 4
1.4.1 Serial Code . . . . . . . . . . . . . . . . . . . . . . . 5
1.4.2 Choice of Parallel Tool . . . . . . . . . . . . . . . . . 7
1.4.3 Meaning of “snow” in This Book . . . . . . . . . . . 8
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.