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.

• 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.

• Artificial Intelligence: A Modern Approach (2nd Edition)
• Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
• Computer Organization and Design: The Hardware/Software Interface (The Morgan Kaufmann Series in Computer Architecture and Design)
• Computer Organization and Design: The Hardware/Software Interface (The Morgan Kaufmann Series in Computer Architecture and Design)
• Making Embedded Systems: Design Patterns for Great Software
• Computer Networking: A Top-Down Approach (5th Edition)
• Computer Architecture: A Quantitative Approach
• Parallel Computer Architecture: A Hardware/Software Approach (The Morgan Kaufmann Series in Computer Architecture and Design)
• Introduction to Algorithms, Second Edition
• Deep Learning (Adaptive Computation and Machine Learning)