Advanced Computer Architecture and Parallel Processing

Wiley Series on Parallel and Distributed Computing

Book 42
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Computer architecture deals with the physical configuration, logical structure, formats, protocols, and operational sequences for processing data, controlling the configuration, and controlling the operations over a computer. It also encompasses word lengths, instruction codes, and the interrelationships among the main parts of a computer or group of computers. This two-volume set offers a comprehensive coverage of the field of computer organization and architecture.
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About the author

HESHAM EL-REWINI, PHD, PE, is a full professor and chairman of the Department of Computer Sciences and Engineering at Southern Methodist University (SMU). He has co-authored several books, published numerous research papers in journals and conference proceedings, and chaired many international conferences.

MOSTAFA ABD-EL-BARR, PHD, PEnG, is a professor and chairman of the Department of Information Science at Kuwait University. He has co-authored two other books, published more than 120 research papers in journals and conference proceedings, and served as chair for a number of international conferences and symposia.

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Additional Information

Publisher
John Wiley & Sons
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Published on
Apr 8, 2005
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Pages
288
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ISBN
9780471478393
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Language
English
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Genres
Computers / Information Technology
Computers / Systems Architecture / General
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Content Protection
This content is DRM protected.
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The state of the art of high-performance computing

Prominent researchers from around the world have gathered to present the state-of-the-art techniques and innovations in high-performance computing (HPC), including:
* Programming models for parallel computing: graph-oriented programming (GOP), OpenMP, the stages and transformation (SAT) approach, the bulk-synchronous parallel (BSP) model, Message Passing Interface (MPI), and Cilk
* Architectural and system support, featuring the code tiling compiler technique, the MigThread application-level migration and checkpointing package, the new prefetching scheme of atomicity, a new "receiver makes right" data conversion method, and lessons learned from applying reconfigurable computing to HPC
* Scheduling and resource management issues with heterogeneous systems, bus saturation effects on SMPs, genetic algorithms for distributed computing, and novel task-scheduling algorithms
* Clusters and grid computing: design requirements, grid middleware, distributed virtual machines, data grid services and performance-boosting techniques, security issues, and open issues
* Peer-to-peer computing (P2P) including the proposed search mechanism of hybrid periodical flooding (HPF) and routing protocols for improved routing performance
* Wireless and mobile computing, featuring discussions of implementing the Gateway Location Register (GLR) concept in 3G cellular networks, maximizing network longevity, and comparisons of QoS-aware scatternet scheduling algorithms
* High-performance applications including partitioners, running Bag-of-Tasks applications on grids, using low-cost clusters to meet high-demand applications, and advanced convergent architectures and protocols

High-Performance Computing: Paradigm and Infrastructure is an invaluable compendium for engineers, IT professionals, and researchers and students of computer science and applied mathematics.
Solving complex optimization problems with parallel metaheuristics

Parallel Metaheuristics brings together an international group of experts in parallelism and metaheuristics to provide a much-needed synthesis of these two fields. Readers discover how metaheuristic techniques can provide useful and practical solutions for a wide range of problems and application domains, with an emphasis on the fields of telecommunications and bioinformatics. This volume fills a long-existing gap, allowing researchers and practitioners to develop efficient metaheuristic algorithms to find solutions.

The book is divided into three parts:
* Part One: Introduction to Metaheuristics and Parallelism, including an Introduction to Metaheuristic Techniques, Measuring the Performance of Parallel Metaheuristics, New Technologies in Parallelism, and a head-to-head discussion on Metaheuristics and Parallelism
* Part Two: Parallel Metaheuristic Models, including Parallel Genetic Algorithms, Parallel Genetic Programming, Parallel Evolution Strategies, Parallel Ant Colony Algorithms, Parallel Estimation of Distribution Algorithms, Parallel Scatter Search, Parallel Variable Neighborhood Search, Parallel Simulated Annealing, Parallel Tabu Search, Parallel GRASP, Parallel Hybrid Metaheuristics, Parallel Multi-Objective Optimization, and Parallel Heterogeneous Metaheuristics
* Part Three: Theory and Applications, including Theory of Parallel Genetic Algorithms, Parallel Metaheuristics Applications, Parallel Metaheuristics in Telecommunications, and a final chapter on Bioinformatics and Parallel Metaheuristics

Each self-contained chapter begins with clear overviews and introductions that bring the reader up to speed, describes basic techniques, and ends with a reference list for further study. Packed with numerous tables and figures to illustrate the complex theory and processes, this comprehensive volume also includes numerous practical real-world optimization problems and their solutions.

This is essential reading for students and researchers in computer science, mathematics, and engineering who deal with parallelism, metaheuristics, and optimization in general.
Discover how to streamline complex bioinformatics applications with parallel computing


This publication enables readers to handle more complex bioinformatics applications and larger and richer data sets. As the editor clearly shows, using powerful parallel computing tools can lead to significant breakthroughs in deciphering genomes, understanding genetic disease, designing customized drug therapies, and understanding evolution.

A broad range of bioinformatics applications is covered with demonstrations on how each one can be parallelized to improve performance and gain faster rates of computation. Current parallel computing techniques and technologies are examined, including distributed computing and grid computing. Readers are provided with a mixture of algorithms, experiments, and simulations that provide not only qualitative but also quantitative insights into the dynamic field of bioinformatics.

Parallel Computing for Bioinformatics and Computational Biology is a contributed work that serves as a repository of case studies, collectively demonstrating how parallel computing streamlines difficult problems in bioinformatics and produces better results. Each of the chapters is authored by an established expert in the field and carefully edited to ensure a consistent approach and high standard throughout the publication.

The work is organized into five parts:
* Algorithms and models
* Sequence analysis and microarrays
* Phylogenetics
* Protein folding
* Platforms and enabling technologies

Researchers, educators, and students in the field of bioinformatics will discover how high-performance computing can enable them to handle more complex data sets, gain deeper insights, and make new discoveries.
The most important use of computing in the future will be in the context of the global "digital convergence" where everything becomes digital and every thing is inter-networked. The application will be dominated by storage, search, retrieval, analysis, exchange and updating of information in a wide variety of forms. Heavy demands will be placed on systems by many simultaneous re quests. And, fundamentally, all this shall be delivered at much higher levels of dependability, integrity and security. Increasingly, large parallel computing systems and networks are providing unique challenges to industry and academia in dependable computing, espe cially because of the higher failure rates intrinsic to these systems. The chal lenge in the last part of this decade is to build a systems that is both inexpensive and highly available. A machine cluster built of commodity hardware parts, with each node run ning an OS instance and a set of applications extended to be fault resilient can satisfy the new stringent high-availability requirements. The focus of this book is to present recent techniques and methods for im plementing fault-tolerant parallel and distributed computing systems. Section I, Fault-Tolerant Protocols, considers basic techniques for achieving fault-tolerance in communication protocols for distributed systems, including synchronous and asynchronous group communication, static total causal order ing protocols, and fail-aware datagram service that supports communications by time.
This monograph evolved from my Ph. D dissertation completed at the Laboratory of Computer Science, MIT, during the Summer of 1986. In my dissertation I proposed a pipelined code mapping scheme for array operations on static dataflow architectures. The main addition to this work is found in Chapter 12, reflecting new research results developed during the last three years since I joined McGill University-results based upon the principles in my dissertation. The terminology dataflow soft ware pipelining has been consistently used since publication of our 1988 paper on the argument-fetching dataflow architecture model at McGill University [43]. In the first part of this book we describe the static data flow graph model as an operational model for concurrent computation. We look at timing considerations for program graph execution on an ideal static dataflow computer, examine the notion of pipe lining, and characterize its performance. We discuss balancing techniques used to transform certain graphs into fully pipelined data flow graphs. In particular, we show how optimal balancing of an acyclic data flow graph can be formulated as a linear programming problem for which an optimal solution exists. As a major result, we show the optimal balancing problem of acyclic data flow graphs is reduceable to a class of linear programming problem, the net work flow problem, for which well-known efficient algorithms exist. This result disproves the conjecture that such problems are computationally hard.
Supercomputers are the largest and fastest computers available at any point in time. The term was used for the first time in the New York World, March 1920, to describe "new statistical machines with the mental power of 100 skilled mathematicians in solving even highly complex algebraic problems. " Invented by Mendenhall and Warren, these machines were used at Columbia University'S Statistical Bureau. Recently, supercomputers have been used primarily to solve large-scale prob lems in science and engineering. Solutions of systems of partial differential equa tions, such as those found in nuclear physics, meteorology, and computational fluid dynamics, account for the majority of supercomputer use today. The early computers, such as EDVAC, SSEC, 701, and UNIVAC, demonstrated the feasibility of building fast electronic computing machines which could become commercial products. The next generation of computers focused on attaining the highest possible computational speeds. This book discusses the architectural approaches used to yield significantly higher computing speeds while preserving the conventional, von Neumann, machine organization (Chapters 2-4). Subsequent improvements depended on developing a new generation of computers employing a new model of computation: single-instruction multiple data (SIMD) processors (Chapters 5-7). Later machines refmed SIMD architec ture and technology (Chapters 8-9). SUPERCOMPUTER ARCHITECI'URE CHAPTER! INTRODUCTION THREE ERAS OF SUPERCOMPUTERS Supercomputers -- the largest and fastest computers available at any point in time -- have been the products of complex interplay among technological, architectural, and algorithmic developments.
 The salient features of the book are as follows:

• Hybrid Elements including topics like Memory organization, Binary representation of data, Computer arithmetic Software for parallel programming, tagged across some chapters through Quick Response (QR) Codes

• Learning objectives tagged across chapters:

• Emphasis on parallelism, scalability and programmability aspects of computer architecture. It presents the analysis of scalability

• Issues related to instruction level parallelism, processor clock speed, and power consumption defined according to the recent developments in processor design

• Inclusion of important topics like processor design, control unit, input and output, parallelis

• erial Bus, Real systems– IBM, Hitachi, Cray, Intel, UltraSparc, Blue Gene (from IBM), Cray XT series, XT5 and XMT, Fujitsu, DEC, MasPar, Tera, Stardent


Topical inclusions include:

• Pipelining hazards, data hazards and control hazards

• PCI Bus and PCI Express

• Interconnection networks and cluster computers

• MPI, openMP, PVM, Pthreads

• Multicore processors

• Impact of technology

• Stream processing

• Programming language Chapel

• Updated coverage of recent processors and systems: Intel Pentium IV, Sun UltraSparc, Blue Gene (from IBM), Cray XT Series, XT5 and XMT


Useful pedagogical features include the following:

• Plenty of background material on OLC

• Diagrams illustrating the basic concepts: 320

• A good number of case studies and: 6

• Solved problems: 114

• Exercise and review problems at the end of chapters: 251

• Tables: 40

• Solved Examples: 114

• Exercise Problems: 251


Computer Architecture: A Quantitative Approach, Fifth Edition, explores the ways that software and technology in the cloud are accessed by digital media, such as cell phones, computers, tablets, and other mobile devices. The book, which became a part of Intel's 2012 recommended reading list for developers, covers the revolution of mobile computing. It also highlights the two most important factors in architecture today: parallelism and memory hierarchy.

This fully updated edition is comprised of six chapters that follow 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. Formulas for energy, static and dynamic power, integrated circuit costs, reliability, and availability are included. The book also covers virtual machines, SRAM and DRAM technologies, and new material on Flash memory. Other topics include the exploitation of instruction-level parallelism in high-performance processors, superscalar execution, dynamic scheduling and multithreading, vector architectures, multicore processors, and warehouse-scale computers (WSCs). There are updated case studies and completely new exercises. Additional reference appendices are available online.

This book will be a valuable reference for computer architects, programmers, application developers, compiler and system software developers, computer system designers and application developers.

Part of Intel's 2012 Recommended Reading List for DevelopersUpdated to cover the mobile computing revolutionEmphasizes the two most important topics in architecture today: memory hierarchy and parallelism in all its forms.Develops common themes throughout each chapter: power, performance, cost, dependability, protection, programming models, and emerging trends ("What's Next")Includes three review appendices in the printed text. Additional reference appendices are available online.Includes updated Case Studies and completely new exercises.
The state of the art of high-performance computing

Prominent researchers from around the world have gathered to present the state-of-the-art techniques and innovations in high-performance computing (HPC), including:
* Programming models for parallel computing: graph-oriented programming (GOP), OpenMP, the stages and transformation (SAT) approach, the bulk-synchronous parallel (BSP) model, Message Passing Interface (MPI), and Cilk
* Architectural and system support, featuring the code tiling compiler technique, the MigThread application-level migration and checkpointing package, the new prefetching scheme of atomicity, a new "receiver makes right" data conversion method, and lessons learned from applying reconfigurable computing to HPC
* Scheduling and resource management issues with heterogeneous systems, bus saturation effects on SMPs, genetic algorithms for distributed computing, and novel task-scheduling algorithms
* Clusters and grid computing: design requirements, grid middleware, distributed virtual machines, data grid services and performance-boosting techniques, security issues, and open issues
* Peer-to-peer computing (P2P) including the proposed search mechanism of hybrid periodical flooding (HPF) and routing protocols for improved routing performance
* Wireless and mobile computing, featuring discussions of implementing the Gateway Location Register (GLR) concept in 3G cellular networks, maximizing network longevity, and comparisons of QoS-aware scatternet scheduling algorithms
* High-performance applications including partitioners, running Bag-of-Tasks applications on grids, using low-cost clusters to meet high-demand applications, and advanced convergent architectures and protocols

High-Performance Computing: Paradigm and Infrastructure is an invaluable compendium for engineers, IT professionals, and researchers and students of computer science and applied mathematics.
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