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Computer Architecture and Parallel Processing

Hwang, Kai

Briggs, Faye A.

Reference

Mcgraw-Hill College

Computers & Technology

Brigts, Faye A.

Hardware & DIY

McGraw-Hill Companies

Features detailed information on a wide range of key subjects, including vector processors and multiprocessors, concurrency control, hypercube systems and key applications, parallel architectures for implementing AI systems, and a comparison of the Cray X-MP-4, the Fujitsu VP-200, and the Hitachi S-810/20. Acidic paper. Annotation copyright Book News, Inc. Portland, Or.

Parallel Processing for Supercomputers and Artificial Intelligence (McGraw-Hill Series in Supercomputing and Parallel Processing)

Computer Science

McGraw=Hill

This book deals with advanced computer architecture and parallel programming techniques. The material is suitable for use as a textbook in a one-semester graduate or senior course, offered by Computer Science, Computer Engineering, Electrical Engineering, or Industrial Engineering programs.

Advanced Computer Architecture: Parallelism, Scalability, Programmability

McGraw-Hill Science/Engineering/Math.

This comprehensive reference from distinguished authors covers the principles, technology, architecture, and programming issues in parallel and distributed computing. From the essential enabling technologies to modern SMP and NUMA multi-processor clusters, it's all here for computer scientists and engineers.

Scalable Parallel Computing: Technology, Architecture, Programming

Xu, Zhiwei

Distributed and Cloud Computing: From Parallel Processing to the Internet of Things offers complete coverage of modern distributed computing technology including clusters, the grid, service-oriented architecture, massively parallel processors, peer-to-peer networking, and cloud computing. It is the first modern, up-to-date distributed systems textbook; it explains how to create high-performance, scalable, reliable systems, exposing the design principles, architecture, and innovative applications of parallel, distributed, and cloud computing systems. Topics covered by this book include: facilitating management, debugging, migration, and disaster recovery through virtualization; clustered systems for research or ecommerce applications; designing systems as web services; and social networking systems using peer-to-peer computing. The principles of cloud computing are discussed using examples from open-source and commercial applications, along with case studies from the leading distributed computing vendors such as Amazon, Microsoft, and Google. Each chapter includes exercises and further reading, with lecture slides and more available online. This book will be ideal for students taking a distributed systems or distributed computing class, as well as for professional system designers and engineers looking for a reference to the latest distributed technologies including cloud, P2P and grid computing.

Distributed and Cloud Computing: From Parallel Processing to the Internet of Things

Dongarra, Jack

Fox, Geoffrey C.

Morgan Kaufmann

The first textbook to teach students how to build data analytic solutions on large data sets using cloud-based technologies.
This is the first textbook to teach students how to build data analytic solutions on large data sets (specifically in Internet of Things applications) using cloud-based technologies for data storage, transmission and mashup, and AI techniques to analyze this data. 
This textbook is designed to train college students to master modern cloud computing systems in operating principles, architecture design, machine learning algorithms, programming models and software tools for big data mining, analytics, and cognitive applications. The book will be suitable for use in one-semester computer science or electrical engineering courses on cloud computing, machine learning, cloud programming, cognitive computing, or big data science. The book will also be very useful as a reference for professionals who want to work in cloud computing and data science.
Cloud and Cognitive Computing begins with two introductory chapters on fundamentals of cloud computing, data science, and adaptive computing that lay the foundation for the rest of the book. Subsequent chapters cover topics including cloud architecture, mashup services, virtual machines, Docker containers, mobile clouds, IoT and AI, inter-cloud mashups, and cloud performance and benchmarks, with a focus on Google's Brain Project, DeepMind, and X-Lab programs, IBKai HwangM SyNapse, Bluemix programs, cognitive initiatives, and neurocomputers. The book then covers machine learning algorithms and cloud programming software tools and application development, applying the tools in machine learning, social media, deep learning, and cognitive applications. All cloud systems are illustrated with big data and cognitive application examples.

Cloud Computing for Machine Learning and Cognitive Applications (Mit Press)

The MIT Press

The definitive guide to successfully integrating social, mobile, Big-Data analytics, cloud and IoT principles and technologies The main goal of this book is to spur the development of effective big-data computing operations on smart clouds that are fully supported by IoT sensing, machine learning and analytics systems. To that end, the authors draw upon their original research and proven track record in the field to describe a practical approach integrating big-data theories, cloud design principles, Internet of Things (IoT) sensing, machine learning, data analytics and Hadoop and Spark programming. Part 1 focuses on data science, the roles of clouds and IoT devices and frameworks for big-data computing. Big data analytics and cognitive machine learning, as well as cloud architecture, IoT and cognitive systems are explored, and mobile cloud-IoT-interaction frameworks are illustrated with concrete system design examples. Part 2 is devoted to the principles of and algorithms for machine learning, data analytics and deep learning in big data applications. Part 3 concentrates on cloud programming software libraries from MapReduce to Hadoop, Spark and TensorFlow and describes business, educational, healthcare and social media applications for those tools. <ul>
<li>The first book describing a practical approach to integrating social, mobile, analytics, cloud and IoT (SMACT) principles and technologies</li> <li>Covers theory and computing techniques and technologies, making it suitable for use in both computer science and electrical engineering programs</li> <li>Offers an extremely well-informed vision of future intelligent and cognitive computing environments integrating SMACT technologies</li> <li>Fully illustrated throughout with examples, figures and approximately 150 problems to support and reinforce learning</li> <li>Features a companion website with an instructor manual and PowerPoint slides www.wiley.com/go/hwangIOT</li> </ul>
Big-Data Analytics for Cloud, IoT and Cognitive Computing satisfies the demand among university faculty and students for cutting-edge information on emerging intelligent and cognitive computing systems and technologies. Professionals working in data science, cloud computing and IoT applications will also find this book to be an extremely useful working resource.

Big-Data Analytics for Cloud, IoT and Cognitive Computing

Chen, Min

Networking & Cloud Computing

wiley

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