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Parallel Computing for Data Science : With Examples in R, C++ and CUDA

Norman Matloff

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Parallel Computing for Data Science : With Examples in R, C++ and CUDA

by Norman Matloff
Parallel Computing for Data Science : With Examples in R, C++ and CUDA - Thryft
Parallel Computing for Data Science : With Examples in R, C++ and CUDA - Thryft

Master parallel computing for data science effortlessly.

Recommendations: - This book is perfect for data scientists and programmers looking to harness the power of parallel computing for data analysis. With comprehensive examples in R, C++, and CUDA, it covers not only the traditional matrix format but also various data structures like time series and network graph models. Whether you're working on multicore systems, clusters, or GPUs, this book provides the foundation and practical knowledge to supercharge your data computations. Take your data science skills to the next level and unlock the potential of parallel computing with this essential guide.

Note: While we do our best to ensure the accuracy of cover images, ISBNs may at times be reused for different editions of the same title which may hence appear as a different cover.

Parallel Computing for Data Science : With Examples in R, C++ and CUDA

by Norman Matloff
Condition guide

Book Condition Guide

Books in Like New Condition

This book may contain very minor cosmetic defects, but it is in pristine condition. There should be no yellowing, no foxing, no water damage, and no annotations of any kind on the cover and the pages. For paperbacks, there should not be any crease marks on the spine. This book is good as new — lucky you!

*Books may be missing bundle media (e.g. CD, e-book code), if included.

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This book has been used, but it is still in a clean condition. There should be no foxing or annotations of any kind on the inner pages, but a sparse amount may be present on the cover, title pages, or outside edges of the book. There should be no water damage of any kind. For paperbacks, there may be light crease marks on the spine.

*Books may be missing bundle media (e.g. CD, e-book code), if included.

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*Books may be missing bundle media (e.g. CD, e-book code), if included.

Books in Well Read Condition

This is a well-read book, which means that the previous owner probably really enjoyed it! The cover and pages may include moderate foxing and annotations, but the text is not obscured and still readable. Moderate cosmetic defects and minor water damage may be present on the edges of the book. For paperbacks, there may be multiple crease marks on the spine.

*Books may be missing bundle media (e.g. CD, e-book code), if included.

 

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ISBN: 9781466587014
Author: Norman Matloff
Publisher: Routledge
Date of Publication: 2015-06-05
Format: Hardcover
Related Collections: Science, Computers & Technology
Related Topics: Computers, Mathematics, Physics
Goodreads rating: 4.17
(rated by 6 readers)

Description

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.
 

Master parallel computing for data science effortlessly.

Recommendations: - This book is perfect for data scientists and programmers looking to harness the power of parallel computing for data analysis. With comprehensive examples in R, C++, and CUDA, it covers not only the traditional matrix format but also various data structures like time series and network graph models. Whether you're working on multicore systems, clusters, or GPUs, this book provides the foundation and practical knowledge to supercharge your data computations. Take your data science skills to the next level and unlock the potential of parallel computing with this essential guide.

Note: While we do our best to ensure the accuracy of cover images, ISBNs may at times be reused for different editions of the same title which may hence appear as a different cover.

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