Structured parallel programming : patterns for efficient computation / Michael McCool, Arch Robison and James Reinders.

Includes bibliographical references and index.

Listings -- Preface -- Preliminaries -- 1 Introduction -- 1.1 Think Parallel -- 1.2 Performance -- 1.3 Motivation: Pervasive Parallelism -- 1.3.1 Hardware Trends Encouraging Parallelism -- 1.3.2 Observed Historical Trends in Parallelism -- 1.3.3 Need for Explicit Parallel Programming -- 1.4 Structured Pattern-Based Programming -- 1.5 Parallel Programming Models -- 1.5.1 Desired Properties -- 1.5.2 Abstractions Instead of Mechanisms. 1.5.3 Expression of Regular Data Parallelism -- 1.5.4 Composability -- 1.5.5 Portability of Functionality -- 1.5.6 Performance Portability -- 1.5.7 Safety, Determinism, and Maintainability -- 1.5.8 Overview of Programming Models Used -- Cilk Plus -- Threading Building Blocks (TBB) -- OpenMP -- Array Building Blocks (ArBB) -- OpenCL -- 1.5.9 When to Use Which Model? -- 1.6 Organization of this Book -- 1.7 Summary -- 2 Background -- 2.1 Vocabulary and Notation -- 2.2 Strategies -- 2.3 Mechanisms -- 2.4 Machine Models -- 2.4.1 Machine Model -- Instruction Parallelism -- Memory Hierarchy. Virtual Memory -- Multiprocessor Systems -- Attached Devices -- 2.4.2 Key Features for Performance -- Data Locality -- Parallel Slack -- 2.4.3 Flynn's Characterization -- 2.4.4 Evolution -- 2.5 Performance Theory -- 2.5.1 Latency and Throughput -- 2.5.2 Speedup, Efficiency, and Scalability -- 2.5.3 Power -- 2.5.4 Amdahl's Law -- 2.5.5 Gustafson-Barsis' Law -- 2.5.6 Work-Span Model -- 2.5.7 Asymptotic Complexity -- 2.5.8 Asymptotic Speedup and Efficiency -- 2.5.9 Little's Formula -- 2.6 Pitfalls -- 2.6.1 Race Conditions -- 2.6.2 Mutual Exclusion and Locks -- 2.6.3 Deadlock. 2.6.4 Strangled Scaling -- 2.6.5 Lack of Locality -- 2.6.6 Load Imbalance -- 2.6.7 Overhead -- 2.7 Summary -- I Patterns -- 3 Patterns -- 3.1 Nesting Pattern -- 3.2 Structured Serial Control Flow Patterns -- 3.2.1 Sequence -- 3.2.2 Selection -- 3.2.3 Iteration -- 3.2.4 Recursion -- 3.3 Parallel Control Patterns -- 3.3.1 Fork-Join -- 3.3.2 Map -- 3.3.3 Stencil -- 3.3.4 Reduction -- 3.3.5 Scan -- 3.3.6 Recurrence -- 3.4 Serial Data Management Patterns -- 3.4.1 Random Read and Write -- 3.4.2 Stack Allocation -- 3.4.3 Heap Allocation -- 3.4.4 Closures -- 3.4.5 Objects. 3.5 Parallel Data Management Patterns -- 3.5.1 Pack -- 3.5.2 Pipeline -- 3.5.3 Geometric Decomposition -- 3.5.4 Gather -- 3.5.5 Scatter -- 3.6 Other Parallel Patterns -- 3.6.1 Superscalar Sequences -- 3.6.2 Futures -- 3.6.3 Speculative Selection -- 3.6.4 Workpile -- 3.6.5 Search -- 3.6.6 Segmentation -- 3.6.7 Expand -- 3.6.8 Category Reduction -- 3.6.9 Term Graph Rewriting -- 3.7 Non-Deterministic Patterns -- 3.7.1 Branch and Bound -- 3.7.2 Transactions -- 3.8 Programming Model Support for Patterns -- 3.8.1 Cilk Plus -- Nesting, Recursion, Fork-Join -- Reduction -- Map, Workpile.

Programming is now parallel programming. Much as structured programming revolutionized traditional serial programming decades ago, a new kind of structured programming, based on patterns, is relevant to parallel programming today. Parallel computing experts and industry insiders Michael McCool, Arch Robison, and James Reinders describe how to design and implement maintainable and efficient parallel algorithms using a pattern-based approach. They present both theory and practice, and give detailed concrete examples using multiple programming models. Examples are primarily given using two of the most popular and cutting edge programming models for parallel programming: Threading Building Blocks, and Cilk Plus. These architecture-independent models enable easy integration into existing applications, preserve investments in existing code, and speed the development of parallel applications. Examples from realistic contexts illustrate patterns and themes in parallel algorithm design that are widely applicable regardless of implementation technology. The patterns-based approach offers structure and insight that developers can apply to a variety of parallel programming models. Develops a composable, structured, scalable, and machine-independent approach to parallel computing. Includes detailed examples in both Cilk Plus and the latest Threading Building Blocks, which support a wide variety of computers. Structured Parallel Programming offers the simplest way for developers to learn patterns for high-performance parallel programming. Written by parallel computing experts and industry insiders Michael McCool, Arch Robison, and James Reinders, this book explains how to design and implement maintainable and efficient parallel algorithms using a composable, structured, scalable, and machine-independent approach to parallel computing. It presents both theory and practice, and provides detailed concrete examples using multiple programming models.