Parallel computing for data science : with examples in R, C++ and Cuda / by Norman Matloff.

Format:

Book

Author/Creator:

Matloff, Norman S., author.

Series:

Chapman & Hall/CRC the R series (CRC Press)

Chapman & Hall/CRC The R Series

Language:

English

Subjects (All):

Parallel programming (Computer science).

Electronic data processing.

R (Computer program language).

Physical Description:

1 online resource (336 p.)

Edition:

First edition.

Place of Publication:

Boca Raton, FL : Chapman and Hall/CRC, an imprint of Taylor and Francis, 2015.

Language Note:

English

Summary:

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.

Contents:

Front Cover; Contents; Preface; Author's Biography; 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 MethodsAppendix A: Review of Matrix Algebra; Appendix B: R Quick Start; Appendix C: Introduction to C for R Programmers; Back Cover

Notes:

"A Chapman & Hall Book."

Description based on print version record.

ISBN:

1-04-005525-7

0-429-07216-3

9780429072161

OCLC:

911396007