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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.



Introduction to Parallel Processing in R

Recurring Theme: The Principle of Pretty Good Parallelism

A Note on Machines

Recurring Theme: Hedging One´s Bets

Extended Example: Mutual Web Outlinks

´Why Is My Program So Slow?´: Obstacles to Speed

Obstacles to Speed

Performance and Hardware Structures

Memory Basics

Network Basics

Latency and Bandwidth

Thread Scheduling

How Many Processes/Threads?

Example: Mutual Outlink Problem

´Big O´ Notation

Data Serialization

´Embarrassingly Parallel´ Applications

Principles of Parallel Loop Scheduling

General Notions of Loop Scheduling

Chunking in Snow

A Note on Code Complexity

Example: All Possible Regressions

The partools Package

Example: All Possible Regressions, Improved Version

Introducing Another Tool: multicore

Issues with Chunk Size

Example: Parallel Distance Computation

The foreach Package

Stride

Another Scheduling Approach: Random Task Permutation

Debugging snow and multicore Code

The Shared Memory Paradigm: A Gentle Introduction through R

So, What Is Actually Shared?

Clarity and Conciseness of Shared-Memory Programming

High-Level Introduction to Shared-Memory Programming: Rdsm Package

Example: Matrix Multiplication

Shared Memory Can Bring a Performance Advantage

Locks and Barriers

Example: Finding the Maximal Burst in a Time Series

Example: Transformation of an Adjacency Matrix

Example: k-Means Clustering

The Shared Memory Paradigm: C Level

OpenMP

Example: Finding the Maximal Burst in a Time Series

OpenMP Loop Scheduling Options

Example: Transformation an Adjacency Matrix

Example: Transforming an Adjacency Matrix, R-Callable Code

Speedup in C

Run Time vs. Development Time

Further Cache/Virtual Memory Issues

Reduction Operations in OpenMP

Debugging

Intel Thread Building Blocks (TBB)

Lockfree Synchronization

The Shared Memory Paradigm: GPUs

Overview

Another Note on Code Complexity

Goal of This Chapter

Introduction to NVIDIA GPUs and CUDA

Example: Mutual Inlinks Problem

Synchronization on GPUs

R and GPUs

The Intel Xeon Phi Chip

Thrust and Rth

Hedging One´s Bets

Thrust Overview

Rth

Skipping the C++

Example: Finding Quantiles

Introduction to Rth

The Message Passing Paradigm

Message Passing Overview

The Cluster Model

Performance Issues

Rmpi

Example: Pipelined Method for Finding Primes

Memory Allocation Issues

Message-Passing Performance Subtleties

MapReduce Computation

Apache Hadoop

Other MapReduce Systems

R Interfaces to MapReduce Systems

An Alternative: ´Snowdoop´

Parallel Sorting and Merging

The Elusive Goal of Optimality

Sorting Algorithms

Example: Bucket Sort in R

Example: Quicksort in OpenMP

Sorting in Rth

Some Timing Comparisons

Sorting on Distributed Data

Parallel Prefix Scan

General Formulation

Applications

General Strategies for Parallel Scan Computation

Implementations of Parallel Prefix Scan

Parallel cumsum() with OpenMP

Example: Moving Average

Parallel Matrix Operations

Tiled Matrices

Example: Snowdoop Approach to Matrix Operations

Parallel Matrix Multiplication

BLAS Libraries

Example: A Look at the Performance of OpenBLAS

Example: Graph Connectedness

Solving Systems of Linear Equations

Sparse Matrices

Inherently Statistical Approaches: Subset Methods

Chunk Averaging

Bag of Little Bootstraps

Subsetting Variables

Appendix A: Review of Matrix Algebra

Appendix B: R Quick Start

Appendix C: Introduction to C for R Programmers