Computer Architecture

Computer Architecture

A Quantitative Approach

5th Edition - September 16, 2011

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  • Authors: John Hennessy, David Patterson
  • eBook ISBN: 9780123838735

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Description

Computer Architecture: A Quantitative Approach, Fifth Edition, explores the ways that software and technology in the cloud are accessed by digital media, such as cell phones, computers, tablets, and other mobile devices. The book, which became a part of Intel's 2012 recommended reading list for developers, covers the revolution of mobile computing. It also highlights the two most important factors in architecture today: parallelism and memory hierarchy. This fully updated edition is comprised of six chapters that follow a consistent framework: explanation of the ideas in each chapter; a crosscutting issues section, which presents how the concepts covered in one chapter connect with those given in other chapters; a putting it all together section that links these concepts by discussing how they are applied in real machine; and detailed examples of misunderstandings and architectural traps commonly encountered by developers and architects. Formulas for energy, static and dynamic power, integrated circuit costs, reliability, and availability are included. The book also covers virtual machines, SRAM and DRAM technologies, and new material on Flash memory. Other topics include the exploitation of instruction-level parallelism in high-performance processors, superscalar execution, dynamic scheduling and multithreading, vector architectures, multicore processors, and warehouse-scale computers (WSCs). There are updated case studies and completely new exercises. Additional reference appendices are available online. This book will be a valuable reference for computer architects, programmers, application developers, compiler and system software developers, computer system designers and application developers.

Key Features

  • Part of Intel's 2012 Recommended Reading List for Developers
  • Updated to cover the mobile computing revolution
  • Emphasizes the two most important topics in architecture today: memory hierarchy and parallelism in all its forms.
  • Develops common themes throughout each chapter: power, performance, cost, dependability, protection, programming models, and emerging trends ("What's Next")
  • Includes three review appendices in the printed text. Additional reference appendices are available online.
  • Includes updated Case Studies and completely new exercises.

Readership

Computer Architects, Computer System Designers, Compiler and System Software Developers, Programmers, Application Developers

Table of Contents

  • 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

Product details

  • No. of pages: 856
  • Language: English
  • Copyright: © Morgan Kaufmann 2011
  • Published: September 16, 2011
  • Imprint: Morgan Kaufmann
  • eBook ISBN: 9780123838735

About the Authors

John Hennessy

John Hennessy
ACM named John L. Hennessy a recipient of the 2017 ACM A.M. Turing Award for pioneering a systematic, quantitative approach to the design and evaluation of computer architectures with enduring impact on the microprocessor industry. John L. Hennessy is a Professor of Electrical Engineering and Computer Science at Stanford University, where he has been a member of the faculty since 1977 and was, from 2000 to 2016, its tenth President. Prof. Hennessy is a Fellow of the IEEE and ACM; a member of the National Academy of Engineering, the National Academy of Science, and the American Philosophical Society; and a Fellow of the American Academy of Arts and Sciences. Among his many awards are the 2001 Eckert-Mauchly Award for his contributions to RISC technology, the 2001 Seymour Cray Computer Engineering Award, and the 2000 John von Neumann Award, which he shared with David Patterson. He has also received seven honorary doctorates.

Affiliations and Expertise

Departments of Electrical Engineering and Computer Science, Stanford University, USA

David Patterson

David Patterson is the Pardee Professor of Computer Science, Emeritus at the University of California at Berkeley, which he joined after graduating from UCLA in 1977.His teaching has been honored by the Distinguished Teaching Award from the University of California, the Karlstrom Award from ACM, and the Mulligan Education Medal and Undergraduate Teaching Award from IEEE. Prof. Patterson received the IEEE Technical Achievement Award and the ACM Eckert-Mauchly Award for contributions to RISC, and he shared the IEEE Johnson Information Storage Award for contributions to RAID. He also shared the IEEE John von Neumann Medal and the C & C Prize with John Hennessy. Like his co-author, Prof. Patterson is a Fellow of the American Academy of Arts and Sciences, the Computer History Museum, ACM, and IEEE, and he was elected to the National Academy of Engineering, the National Academy of Sciences, and the Silicon Valley Engineering Hall of Fame. He served on the Information Technology Advisory Committee to the U.S. President, as chair of the CS division in the Berkeley EECS department, as chair of the Computing Research Association, and as President of ACM. This record led to Distinguished Service Awards from ACM, CRA, and SIGARCH.

Affiliations and Expertise

Pardee Professor of Computer Science, Emeritus, University of California at Berkeley, USA

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