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Modern Computer Architecture and Organization

Modern Computer Architecture and Organization - Third Edition

By : Jim Ledin
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Modern Computer Architecture and Organization

Modern Computer Architecture and Organization

5 (1)
By: Jim Ledin

Overview of this book

Computer architecture now extends far beyond CPUs, forming the foundation of cloud platforms, AI systems, cybersecurity solutions, and emerging computing technologies. Modern Computer Architecture and Organization, Third Edition, provides a clear, end-to-end understanding of how today’s computer systems are built and why architectural decisions matter more than ever. You’ll begin with the fundamentals of digital logic, processor elements, memory systems, and the hardware–software interface, building a strong grounding that remains essential for every architect and systems engineer. Next, the book explores modern processor and memory architectures, performance-enhancing techniques, virtualization, and mainstream instruction sets, including x86, ARM, and an in-depth treatment of RISC-V. New and significantly expanded chapters extend this foundation into modern computing domains, including AI and large language model (LLM) computing platforms, GPUs, cloud-scale systems, confidential and security-focused architectures, blockchain and Bitcoin mining systems, self-driving vehicle architectures, and future directions such as quantum computing. This edition reflects today’s AI-driven, cloud-centric, security-aware computing landscape while remaining true to the classical principles that continue to underpin modern computer systems.
Table of Contents (28 chapters)
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1
Part 1: Fundamentals of Computer Architecture
8
Part 2: Processor Architectures and Instruction Sets
15
Part 3: Computer System Architectures
23
Part 4: Appendices
27
Index

Tensor processing

The GA102 tensor cores are designed to accelerate computations of geometric transformations in graphics display and the tensor operations in artificial intelligence applications. Each tensor core is a programmable matrix multiply and accumulate unit.

Unlike the multiplication of two scalars, the order of the operands in matrix multiplication is significant, just as it is when dividing one scalar by another. Furthermore, the multiplication of two matrices is only possible if the number of columns in the first matrix is equal to the number of rows in the second. These dimensions are referred to as M (the number of rows in the first matrix), N (the number of columns in the second matrix), and K (the number of columns in the first matrix and the number of rows in the second matrix). The result of a matrix multiplication with these dimensions is a matrix with M rows and N columns.

The computational units in the GA102 tensor core support a limited number of relatively small...

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