Computer Architecture: A Quantitative Approach, 4th Edition
The era of seemingly unlimited growth in processor performance is over: single chip architectures can no longer overcome the performance limitations imposed by the power they consume and the heat they generate. Today, Intel and other semiconductor firms are abandoning the single fast processor model in favor of multi-core microprocessors--chips that combine two or more processors in a single package. In the fourth edition of Computer Architecture, the authors focus on this historic shift, increasing their coverage of multiprocessors and exploring the most effective ways of achieving parallelism as the key to unlocking the power of multiple processor architectures. Additionally, the new edition has expanded and updated coverage of design topics beyond processor performance, including power, reliability, availability, and dependability.
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Increased coverage on achieving parallelism with multiprocessors.
Case studies of latest technology from industry including the Sun Niagara Multiprocessor, AMD Opteron, and Pentium 4.
Three review appendices, included in the printed volume, review the basic and intermediate principles the main text relies upon.
Eight reference appendices, collected on the CD, cover a range of topics including specific architectures, embedded systems, application specific processors--some guest authored by subject experts.
Why Read This Book
You should read this book if you want a rigorous, numbers-driven foundation for designing and evaluating modern processors and multicore systems; you will learn how to quantify trade-offs in performance, power, and cost so design decisions are evidence-based. The book emphasizes parallelism, memory hierarchy, and real-world case studies that translate directly to better firmware, SoC, and system-level engineering choices.
Who Will Benefit
Advanced undergraduates, graduate students, and practicing architects or embedded engineers who need a quantitative framework for designing or evaluating CPUs, multicore systems, memory systems, and performance-critical embedded platforms.
Level: Advanced — Prerequisites: Solid grounding in digital logic and computer organization, familiarity with C and assembly-level programming, basic probability and discrete math, and exposure to operating-system and performance concepts.
Key Takeaways
- Analyze processor and system performance quantitatively using metrics, Amdahl's law, and benchmarking methodology
- Design and evaluate pipelined and superscalar processors to exploit instruction-level parallelism
- Architect memory hierarchies and caches to balance latency, bandwidth, and cost for embedded and server workloads
- Assess and design multicore and multiprocessor systems, including interconnects and cache-coherence implications
- Quantify power, energy, and reliability trade-offs and incorporate them into architectural decisions
- Apply case-study lessons to map real-world workloads onto efficient hardware and to interpret hardware performance counters
Topics Covered
- Principles of Computer Design: Cost, Performance, and Amdahl's Law
- Instruction Set Principles and Examples (RISC principles, MIPS examples)
- Instruction-Level Parallelism: Pipelining and Hazards
- Exploiting ILP: Dynamic Scheduling and Out-of-Order Execution
- Data-Level Parallelism: SIMD and Vector Architectures
- Memory Hierarchy Design: Caches, Virtual Memory, and Locality
- Storage, I/O, and Interfacing
- Multiprocessors and Multicore Architectures: Models and Scalability
- Interconnection Networks and Cache Coherence
- Dependability, Reliability, and Security Considerations
- Power, Energy, and Thermal Constraints in Architecture
- Quantitative Case Studies and Emerging Trends (multicore, warehouse-scale computing)
Languages, Platforms & Tools
How It Compares
More quantitative and architecture-focused than Patterson & Hennessy's Computer Organization and Design (which is more introductory), and broader in hardware scope than Bryant & O'Hallaron's Computer Systems: A Programmer's Perspective, which focuses on the software view of systems.
