Computer architecture:a quantitative approach / John L. Hennessy, David A. Patterson

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 lssues; The Design of Memory Hierarchies
2.6 Putting It All Together; Memory Hierachies in the
ARM Cortex-A8 and Intel Core 17
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 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 17 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
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 CPUs
and Tesia versus Core 17
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
Centralized Shared-Memory Architectures
Performance of Symmetric Shared-Memory Multiprocessors
5.2
5.3
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
Chapter 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