Foreword
Preface
Acknowledgments
Chapter 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
Chapter 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-AS 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
Chapter 3 nstruction-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 oflLP
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-AS
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 R Colwell
Chapter4 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
Chapter 5 Thread-Level Parallelism
5.1 Introduction
5.2 Centralized Shared-Memory Architectures
5.3 Performance of Symmetric Shared-Memory Multiprocessors
……
Chapter6 Warehouse-Scale Computers to Exploit Request-Level and Data-Level Parallelism
Appendix A Instruction Set Principles
Appendix B Review of Memory Hierarchy
Appendix C Pipelining: Basic and Intermediate Concepts
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