Parallel computing for data science : with examples in R, C++ and CUDA
著者
書誌事項
Parallel computing for data science : with examples in R, C++ and CUDA
（The R series）
CRC Press, c2016
- : hardback
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注記
Includes index
内容説明・目次
内容説明
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.
目次
Introduction to Parallel Processing in R. "Why Is My Program So Slow?": Obstacles to Speed. Principles of Parallel Loop Scheduling. The Shared Memory Paradigm: A Gentle Introduction through R. The Shared Memory Paradigm: C Level. The Shared Memory Paradigm: GPUs. Thrust and Rth. The Message Passing Paradigm. MapReduce Computation. Parallel Sorting and Merging. Parallel Prefix Scan. Parallel Matrix Operations. Inherently Statistical Approaches: Subset Methods. Appendices.
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