### Editorial Reviews

**Parallel Computing for Data Science: With Examples in R, C++ and CUDA** is one of the first parallel computing books to concentrate exclusively on parallel data structures, algorithms, software tools, and applications in data science. It includes examples not only from the classic “n observations, p variables” matrix format but also from time series, network graph models, and numerous other structures common in data science. The examples illustrate the range of issues encountered in parallel programming.

With the main focus on computation, the book shows how to compute on three types of platforms: multicore systems, clusters, and graphics processing units (GPUs). It also discusses software packages that span more than one type of hardware and can be used from more than one type of programming language. Readers will find that the foundation established in this book will generalize well to other languages, such as Python and Julia.

### Table of Contents

Chapter 1: Introduction to Parallel Processing in R

Chapter 2: “Why Is My Program So Slow?”: Obstacles to Speed

Chapter 3: Principles of Parallel Loop Scheduling

Chapter 4: The Shared-Memory Paradigm: A Gentle Introduction via R

Chapter 5: The Shared-Memory Paradigm: C Level

Chapter 6: The Shared-Memory Paradigm: GPUs

Chapter 7: Thrust and Rth

Chapter 8: The Message Passing Paradigm

Chapter 9: MapReduce Computation

Chapter 10: Parallel Sorting and Merging

Chapter 11: Parallel Pre x Scan

Chapter 12: Parallel Matrix Operations

Chapter 13: Inherently Statistical Approaches: Subset Methods

Appendix A: Review of Matrix Algebra

Appendix B: R Quick Start

Appendix C: Introduction to C for R Programmers

### Book Details

**Author:**Norman Matloff**Pages:**328 pages**Edition:**1**Publication Date:**2015-06-23**Publisher:**Chapman and Hall/CRC**Language:**English**ISBN-10:**1466587016**ISBN-13:**9781466587014

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