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What You Need to Know About Computer Architecture and Parallel Processing by Kai Hwang

# Computer Architecture and Parallel Processing by Kai Hwang: A Comprehensive Review ## Introduction - What is computer architecture and parallel processing? - Why are they important for modern computing? - Who is Kai Hwang and what is his contribution to the field? - What is the main objective and scope of his book? ## Overview of the Book - How is the book organized and structured? - What are the main topics and concepts covered in each chapter? - How does the book balance theory and practice? - What are the features and benefits of the book for readers? ## Chapter-by-Chapter Summary - Chapter 1: Introduction to Parallel Processing - Definition and classification of parallel processing systems - Performance measures and speedup laws - Parallel processing applications and challenges - Chapter 2: Memory and Input-Output Subsystems - Memory hierarchy and cache organization - Virtual memory and paging techniques - Input-output devices and interfaces - Bus arbitration and DMA transfer - Chapter 3: Principles of Pipelining and Vector Processing - Basic concepts and terminology of pipelining - Pipeline hazards and solutions - Instruction-level parallelism and superscalar processors - Vector processing and array processors - Chapter 4: Pipeline Computers and Vectorization Methods - Design principles and examples of pipeline computers - Vectorization methods and compiler techniques - Performance evaluation and optimization of pipeline computers - Case studies: CDC STAR-100, TI ASC, CRAY-1 - Chapter 5: Multiprocessors: Interconnection Networks and Clusters - Classification and properties of interconnection networks - Routing algorithms and performance analysis - Cluster computing and distributed systems - Case studies: IBM RP3, Intel iPSC, CMU C.mmp - Chapter 6: Multiprocessors: Architectures, Algorithms, and Programming - Taxonomy and examples of multiprocessor architectures - Parallel algorithms and complexity analysis - Parallel programming models and languages - Case studies: IBM System/370, Sequent Balance, Encore Multimax - Chapter 7: Dataflow Computers: Models, Languages, and Architectures - Dataflow computation model and semantics - Dataflow languages and compilers - Dataflow architectures and implementations - Case studies: MIT Static Dataflow Machine, Manchester Dataflow Machine, J-Machine ## Critical Evaluation of the Book - What are the strengths and weaknesses of the book? - How does the book compare with other books on the same topic? - How relevant and up-to-date is the book for current research and practice? - What are some suggestions for improvement or future editions? ## Conclusion - Summarize the main points and findings of the article - Highlight the key takeaways and implications for readers - Provide some recommendations or resources for further reading or learning ## FAQs ### Q1: Who is the target audience of the book? ### A1: The book is intended for advanced undergraduate and graduate students, researchers, and practitioners who are interested in learning about computer architecture and parallel processing. ### Q2: What are the prerequisites for reading the book? ### A2: The book assumes that readers have a basic background in computer organization, assembly language programming, data structures, algorithms, operating systems, and mathematics. ### Q3: How can I access or download the book for free? ### A3: The book is available online for free at , where you can read it online or download it as a PDF file. ### Q4: What are some other books on computer architecture and parallel processing that I can read? ### A4: Some other books that you can read are: - Advanced Computer Architecture: Parallelism, Scalability, Programmability by Kai Hwang (McGraw-Hill, 1993) - Computer Architecture: A Quantitative Approach by John L. Hennessy and David A. Patterson (Morgan Kaufmann, 2019) - Parallel Computer Architecture: A Hardware/Software Approach by David Culler, Jaswinder Pal Singh, Anoop Gupta (Morgan Kaufmann, 1999) - Introduction to Parallel Computing by Ananth Grama, Anshul Gupta, George Karypis, Vipin Kumar (Pearson, 2003) ### Q5: How can I learn more about Kai Hwang and his research? ### A5: You can visit his personal website at , where you can find his biography, publications, projects, awards, and contact information.


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