ABSTRACT

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,

chapter 1|16 pages

Introduction to Parallel Processing in R

chapter 3|44 pages

Principles of Parallel Loop Scheduling

chapter 5|42 pages

The Shared-Memory Paradigm: C Level

chapter 6|22 pages

Overview

chapter 7|10 pages

Thrust and Rth

chapter 8|18 pages

The Message Passing Paradigm

chapter 9|12 pages

MapReduce Computation

chapter 10|14 pages

Parallel Sorting and Merging

chapter 11|18 pages

Parallel Prefix Scan

chapter 12|24 pages

Parallel Matrix Operations