John L. Hennessy, David A. Patterson, Christos Kozyrakis | Computer Architecture. A Quantitative Approach (The Morgan Kaufmann Series in Computer Architecture and Design). 7th Edition (2025) [PDF]
Автор: John L. Hennessy, David A. Patterson, Christos Kozyrakis
Издательство: Morgan Kaufmann
ISBN: 978-0443154065
Жанр: Computer Hardware Design & Architecture, Microprocessor Design, Computer Science
Язык: Английский
Формат: PDF
Качество: Изначально электронное (ebook)
Иллюстрации: Цветные и черно-белые
Описание:Computer Architecture: A Quantitative Approach, has been considered essential reading by instructors, students and practitioners of computer design for nearly 30 years. The seventh edition of this classic textbook from John Hennessy and David Patterson, winners of the 2017 ACM A.M. Turing Award recognizing contributions of lasting and major technical importance to the computing field, along with new author Christos Kozyrakis, is fully revised with the latest developments in processor and system architecture.
True to its original mission of demystifying computer architecture, this edition continues the longstanding tradition of focusing on areas where the most exciting computing innovation is happening, while always keeping an emphasis on good engineering design.
Читать фрагмент Winner of a 2019 Textbook Excellence Award (Texty) from the Textbook and Academic Authors Association.
Each chapter follows a consistent framework: explanation of the ideas in each chapter; a "crosscutting issues" section, which presents how the concepts covered in one chapter connect with those given in other chapters; a "putting it all together" section that links these concepts by discussing how they are applied in real machine; and detailed examples of misunderstandings and architectural traps commonly encountered by developers and architects.
Includes "Putting It All Together" sections near the end of every chapter, providing real-world technology examples that demonstrate the principles covered in each chapter.
Covers new developments in GPU and CPU architectures, as well as domain specific architectures.
Features more comprehensive coverage of systems on chip and heterogeneity.
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