Computer Architecture,

In Praise of Computer Architecture: A Quantitative Approach Fifth Edition

Dedication

Foreword

Preface

Why We Wrote This Book

This Edition

Topic Selection and Organization

An Overview of the Content

Navigating the Text

Chapter Structure

Case Studies with Exercises

Supplemental Materials

Helping Improve This Book

Concluding Remarks

Acknowledgments

Contributors to the Fifth Edition

Contributors to Previous Editions

1. Fundamentals of Quantitative Design and Analysis

1.1 Introduction

1.2 Classes of Computers

1.3 Defining Computer Architecture

1.4 Trends in Technology

1.5 Trends in Power and Energy in Integrated Circuits

1.6 Trends in Cost

1.7 Dependability

1.8 Measuring, Reporting, and Summarizing Performance

1.9 Quantitative Principles of Computer Design

1.10 Putting It All Together: Performance, Price, and Power

1.11 Fallacies and Pitfalls

1.12 Concluding Remarks

1.13 Historical Perspectives and References

Case Studies and Exercises by Diana Franklin

2. Memory Hierarchy Design

2.1 Introduction

2.2 Ten Advanced Optimizations of Cache Performance

2.3 Memory Technology and Optimizations

2.4 Protection: Virtual Memory and Virtual Machines

2.5 Crosscutting Issues: The Design of Memory Hierarchies

2.6 Putting It All Together: Memory Hierachies in the ARM Cortex-A8 and Intel Core i7

2.7 Fallacies and Pitfalls

2.8 Concluding Remarks: Looking Ahead

2.9 Historical Perspective and References

Case Studies and Exercises by Norman P. Jouppi, Naveen Muralimanohar, and Sheng Li

3. Instruction-Level Parallelism and Its Exploitation

3.1 Instruction-Level Parallelism: Concepts and Challenges

3.2 Basic Compiler Techniques for Exposing ILP

3.3 Reducing Branch Costs with Advanced Branch Prediction

3.4 Overcoming Data Hazards with Dynamic Scheduling

3.5 Dynamic Scheduling: Examples and the Algorithm

3.6 Hardware-Based Speculation

3.7 Exploiting ILP Using Multiple Issue and Static Scheduling

3.8 Exploiting ILP Using Dynamic Scheduling, Multiple Issue, and Speculation

3.9 Advanced Techniques for Instruction Delivery and Speculation

3.10 Studies of the Limitations of ILP

3.11 Cross-Cutting Issues: ILP Approaches and the Memory System

3.12 Multithreading: Exploiting Thread-Level Parallelism to Improve Uniprocessor Throughput

3.13 Putting It All Together: The Intel Core i7 and ARM Cortex-A8

3.14 Fallacies and Pitfalls

3.15 Concluding Remarks: What’s Ahead?

3.16 Historical Perspective and References

Case Studies and Exercises by Jason D. Bakos and Robert P. Colwell

4. Data-Level Parallelism in Vector, SIMD, and GPU Architectures

4.1 Introduction

4.2 Vector Architecture

4.3 SIMD Instruction Set Extensions for Multimedia

4.4 Graphics Processing Units

4.5 Detecting and Enhancing Loop-Level Parallelism

4.6 Crosscutting Issues

4.7 Putting It All Together: Mobile versus Server GPUs and Tesla versus Core i7

4.8 Fallacies and Pitfalls

4.9 Concluding Remarks

4.10 Historical Perspective and References

Case Study and Exercises by Jason D. Bakos

5. Thread-Level Parallelism

5.1 Introduction

5.2 Centralized Shared-Memory Architectures

5.3 Performance of Symmetric Shared-Memory Multiprocessors

5.4 Distributed Shared-Memory and Directory-Based Coherence

5.5 Synchronization: The Basics

5.6 Models of Memory Consistency: An Introduction

5.7 Crosscutting Issues

5.8 Putting It All Together: Multicore Processors and Their Performance

5.9 Fallacies and Pitfalls

5.10 Concluding Remarks

5.11 Historical Perspectives and References

Case Studies and Exercises by Amr Zaky and David A. Wood

6. Warehouse-Scale Computers to Exploit Request-Level and Data-Level Parallelism

6.1 Introduction

6.2 Programming Models and Workloads for Warehouse-Scale Computers

6.3 Computer Architecture of Warehouse-Scale Computers

6.4 Physical Infrastructure and Costs of Warehouse-Scale Computers

6.5 Cloud Computing: The Return of Utility Computing

6.6 Crosscutting Issues

6.7 Putting It All Together: A Google Warehouse-Scale Computer

6.8 Fallacies and Pitfalls

6.9 Concluding Remarks

6.10 Historical Perspectives and References

Case Studies and Exercises by Parthasarathy Ranganathan

A. Instruction Set Principles

A.1 Introduction

A.2 Classifying Instruction Set Architectures

A.3 Memory Addressing

A.4 Type and Size of Operands

A.5 Operations in the Instruction Set

A.6 Instructions for Control Flow

A.7 Encoding an Instruction Set

A.8 Crosscutting Issues: The Role of Compilers

A.9 Putting It All Together: The MIPS Architecture

A.10 Fallacies and Pitfalls

A.11 Concluding Remarks

A.12 Historical Perspective and References

Exercises by Gregory D. Peterson

B. Review of Memory Hierarchy

B.1 Introduction

B.2 Cache Performance

B.3 Six Basic Cache Optimizations

B.4 Virtual Memory

B.5 Protection and Examples of Virtual Memory

B.6 Fallacies and Pitfalls

B.7 Concluding Remarks

B.1 Historical Perspective and References

Exercises by Amr Zaky

C. Pipelining: Basic and Intermediate Concepts

C.1 Introduction

C.2 The Major Hurdle of Pipelining—Pipeline Hazards

C.3 How Is Pipelining Implemented?

C.4 What Makes Pipelining Hard to Implement?

C.5 Extending the MIPS Pipeline to Handle Multicycle Operations

C.6 Putting It All Together: The MIPS R4000 Pipeline

C.7 Crosscutting Issues

C.8 Fallacies and Pitfalls

C.9 Concluding Remarks

C.10 Historical Perspective and References

Updated Exercises by Diana Franklin

Index

Translation between GPU terms in book and official NVIDIA and OpenCL terms