Computer Architecture: A Quantitative Approach 6th Edition by John L. Hennessy, ISBN-13: 978-0128119051


Computer Architecture: A Quantitative Approach 6th Edition by John L. Hennessy, ISBN-13: 978-0128119051

[PDF eBook eTextbook]

  • Publisher: ‎ Morgan Kaufmann; 6th edition (December 7, 2017)
  • Language: ‎ English
  • 936 pages
  • ISBN-10: ‎ 0128119055
  • ISBN-13: ‎ 978-0128119051

Features the RISC-V (“RISC Five”) instruction set architecture, a modern RISC instruction set developed and designed to be a free and openly adoptable standard.

Computer Architecture: A Quantitative Approach, Sixth Edition has been considered essential reading by instructors, students and practitioners of computer design for over 20 years. The sixth edition of this classic textbook from Hennessy and Patterson, winners of the 2017 ACM A.M. Turing Award recognizing contributions of lasting and major technical importance to the computing field, is fully revised with the latest developments in processor and system architecture. The text now features examples from the RISC V (RISC Five) instruction set architecture, a modern RISC instruction set developed and designed to be a free and openly adoptable standard. It also includes a new chapter on domain specific architectures and an updated chapter on warehouse scale computing that features the first public information on Google’s newest WSC.

True to its original mission of demystifying computer architecture, this edition continues the longstanding tradition of focusing on areas where the most exciting computing innovation is happening, while always keeping an emphasis on good engineering design.

  • Winner of a 2019 Textbook Excellence Award (Texty) from the Textbook and Academic Authors Association
  • Includes a new chapter on domain specific architectures, explaining how they are the only path forward for improved performance and energy efficiency given the end of Moore’s Law and Dennard scaling
  • Features the first publication of several DSAs from industry
  • Features extensive updates to the chapter on warehouse scale computing, with the first public information on the newest Google WSC
  • Offers updates to other chapters including new material dealing with the use of stacked DRAM; data on the performance of new NVIDIA Pascal GPU vs. new AVX 512 Intel Skylake CPU; and extensive additions to content covering multicore architecture and organization
  • Includes “Putting It All Together” sections near the end of every chapter, providing real world technology examples that demonstrate the principles covered in each chapter
  • Includes review appendices in the printed text and additional reference appendices available online
  • Includes updated and improved case studies and exercises

Table of Contents:

Foreword ix
Preface xvii
Acknowledgments xxv
Chapter 1 Fundamentals of Quantitative Design and Analysis
1.1 Introduction 2
1.2 Classes of Computers 6
1.3 Defining Computer Architecture 11
1.4 Trends in Technology 18
1.5 Trends in Power and Energy in Integrated Circuits 23
1.6 Trends in Cost 29
1.7 Dependability 36
1.8 Measuring, Reporting, and Summarizing Performance 39
1.9 Quantitative Principles of Computer Design 48
1.10 Putting It All Together: Performance, Price, and Power 55
1.11 Fallacies and Pitfalls 58
1.12 Concluding Remarks 64
1.13 Historical Perspectives and References 67
Case Studies and Exercises by Diana Franklin 67
Chapter 2 Memory Hierarchy Design
2.1 Introduction 78
2.2 Memory Technology and Optimizations 84
2.3 Ten Advanced Optimizations of Cache Performance 94
2.4 Virtual Memory and Virtual Machines 118
2.5 Cross-Cutting Issues: The Design of Memory Hierarchies 126
2.6 Putting It All Together: Memory Hierarchies in the ARM Cortex-A53
and Intel Core i7 6700 129
2.7 Fallacies and Pitfalls 142
2.8 Concluding Remarks: Looking Ahead 146
2.9 Historical Perspectives and References 148
Case Studies and Exercises by Norman P. Jouppi, Rajeev
Balasubramonian, Naveen Muralimanohar, and Sheng Li 148
Chapter 3 Instruction-Level Parallelism and Its Exploitation
3.1 Instruction-Level Parallelism: Concepts and Challenges 168
3.2 Basic Compiler Techniques for Exposing ILP 176
3.3 Reducing Branch Costs With Advanced Branch Prediction 182
3.4 Overcoming Data Hazards With Dynamic Scheduling 191
3.5 Dynamic Scheduling: Examples and the Algorithm 201
3.6 Hardware-Based Speculation 208
3.7 Exploiting ILP Using Multiple Issue and Static Scheduling 218
3.8 Exploiting ILP Using Dynamic Scheduling, Multiple Issue, and
Speculation 222
3.9 Advanced Techniques for Instruction Delivery and Speculation 228
3.10 Cross-Cutting Issues 240
3.11 Multithreading: Exploiting Thread-Level Parallelism to Improve
Uniprocessor Throughput 242
3.12 Putting It All Together: The Intel Core i7 6700 and ARM Cortex-A53 247
3.13 Fallacies and Pitfalls 258
3.14 Concluding Remarks: What’s Ahead? 264
3.15 Historical Perspective and References 266
Case Studies and Exercises by Jason D. Bakos and Robert P. Colwell 266
Chapter 4 Data-Level Parallelism in Vector, SIMD, and GPU Architectures
4.1 Introduction 282
4.2 Vector Architecture 283
4.3 SIMD Instruction Set Extensions for Multimedia 304
4.4 Graphics Processing Units 310
4.5 Detecting and Enhancing Loop-Level Parallelism 336
4.6 Cross-Cutting Issues 345
4.7 Putting It All Together: Embedded Versus Server GPUs and
Tesla Versus Core i7 346
4.8 Fallacies and Pitfalls 353
4.9 Concluding Remarks 357
4.10 Historical Perspective and References 357
Case Study and Exercises by Jason D. Bakos 357
Chapter 5 Thread-Level Parallelism
5.1 Introduction 368
5.2 Centralized Shared-Memory Architectures 377
5.3 Performance of Symmetric Shared-Memory Multiprocessors 393
5.4 Distributed Shared-Memory and Directory-Based Coherence 404
5.5 Synchronization: The Basics 412
5.6 Models of Memory Consistency: An Introduction 417
5.7 Cross-Cutting Issues 422
5.8 Putting It All Together: Multicore Processors and Their Performance 426
5.9 Fallacies and Pitfalls 438
5.10 The Future of Multicore Scaling 442
5.11 Concluding Remarks 444
5.12 Historical Perspectives and References 445
Case Studies and Exercises by Amr Zaky and David A. Wood 446
Chapter 6 Warehouse-Scale Computers to Exploit Request-Level
and Data-Level Parallelism
6.1 Introduction 466
6.2 Programming Models and Workloads for Warehouse-Scale
Computers 471
6.3 Computer Architecture of Warehouse-Scale Computers 477
6.4 The Efficiency and Cost of Warehouse-Scale Computers 482
6.5 Cloud Computing: The Return of Utility Computing 490
6.6 Cross-Cutting Issues 501
6.7 Putting It All Together: A Google Warehouse-Scale Computer 503
6.8 Fallacies and Pitfalls 514
6.9 Concluding Remarks 518
6.10 Historical Perspectives and References 519
Case Studies and Exercises by Parthasarathy Ranganathan 519
Chapter 7 Domain-Specific Architectures
7.1 Introduction 540
7.2 Guidelines for DSAs 543
7.3 Example Domain: Deep Neural Networks 544
7.4 Google’s Tensor Processing Unit, an Inference Data
Center Accelerator 557
7.5 Microsoft Catapult, a Flexible Data Center Accelerator 567
7.6 Intel Crest, a Data Center Accelerator for Training 579
7.7 Pixel Visual Core, a Personal Mobile Device Image Processing Unit 579
7.8 Cross-Cutting Issues 592
7.9 Putting It All Together: CPUs Versus GPUs Versus DNN Accelerators 595
7.10 Fallacies and Pitfalls 602
7.11 Concluding Remarks 604
7.12 Historical Perspectives and References 606
Case Studies and Exercises by Cliff Young 606
Appendix A Instruction Set Principles
A.1 Introduction A-2
A.2 Classifying Instruction Set Architectures A-3
A.3 Memory Addressing A-7
A.4 Type and Size of Operands A-13
A.5 Operations in the Instruction Set A-15
A.6 Instructions for Control Flow A-16
A.7 Encoding an Instruction Set A-21
A.8 Cross-Cutting Issues: The Role of Compilers A-24
A.9 Putting It All Together: The RISC-V Architecture A-33
A.10 Fallacies and Pitfalls A-42
A.11 Concluding Remarks A-46
A.12 Historical Perspective and References A-47
Exercises by Gregory D. Peterson A-47
Appendix B Review of Memory Hierarchy
B.1 Introduction B-2
B.2 Cache Performance B-15
B.3 Six Basic Cache Optimizations B-22
B.4 Virtual Memory B-40
B.5 Protection and Examples of Virtual Memory B-49
B.6 Fallacies and Pitfalls B-57
B.7 Concluding Remarks B-59
B.8 Historical Perspective and References B-59
Exercises by Amr Zaky B-60
Appendix C Pipelining: Basic and Intermediate Concepts
C.1 Introduction C-2
C.2 The Major Hurdle of Pipelining—Pipeline Hazards C-10
C.3 How Is Pipelining Implemented? C-26
C.4 What Makes Pipelining Hard to Implement? C-37
C.5 Extending the RISC V Integer Pipeline to Handle Multicycle
Operations C-45
C.6 Putting It All Together: The MIPS R4000 Pipeline C-55
C.7 Cross-Cutting Issues C-65
C.8 Fallacies and Pitfalls C-70
C.9 Concluding Remarks C-71
C.10 Historical Perspective and References C-71
Updated Exercises by Diana Franklin C-71
Online Appendices
Appendix D Storage Systems
Appendix E Embedded Systems
by Thomas M. Conte
Appendix F Interconnection Networks
by Timothy M. Pinkston and Jose Duato
Appendix G Vector Processors in More Depth
by Krste Asanovic
Appendix H Hardware and Software for VLIW and EPIC
Appendix I Large-Scale Multiprocessors and Scientific Applications
Appendix J Computer Arithmetic
by David Goldberg
Appendix K Survey of Instruction Set Architectures
Appendix L Advanced Concepts on Address Translation
by Abhishek Bhattacharjee
Appendix M Historical Perspectives and References
References R-1
Index I-1

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.

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.

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