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Since the advent of the Inteet in theThis has been enabled by advances inThese factors have contributed toAround the tu of the millenium,Advances in hardware technology have suddenly made sensorIn the emerging globalMoreover, thisWriting suchThe book will focus on the fundamental principles and modelsIt will addressThe manner of presentation of theIntroduction A model of distributed computations Logical time Global state and snapshot recording algorithms Terminology and basic algorithms Message ordering and group communication Termination detection Reasoning with knowledge Distributed mutual exclusion algorithms Deadlock detection in distributed systems Global predicate detection Distributed shared memory Checkpointing and rollback recovery Consensus and agreement algorithms Failure detectors Authentication in distributed systems Self-stabilization Peer-to-peer computing and overlay graphsIn particular, the lack of a broadly supported standard for programming shared-memory multiprocessors has been a chasm both for users and for software vendors interested in porting their software to these multiprocessors. OpenMP represents the first vendor.As we become able to do more and more with our advanced technologies, and as we hide them and their complexities completely from their users, we will have accomplished the envisioned magic desideratum that a.The era of sequential computing must give way to a new era in which parallelism is at the forefront. Although important scientific and engineering challenges lie ahead, this is an opportune time for innovation in programming systems and computi.The tools need manual intervention by the programmer to parallelize the code. This book is intended to give the programmer the techniques necessary to explore parallelism in algorithms, serial as well as.From working in a scientific computing center, the authors gained a unique perspective on the requirements and attitudes of users.
Although important improvements have been achieved in this field in the last 30 years, there are still many unresolved issues. These issues arise from several broad areas, such as the design of parallel systems and scalable inte.Numerics, Applications, and Trends There has been rapid progress in microprocessor architecture, interconnection technology and software development, which are influencing directly the rapid growth of parallel and distributed computing. However, in order to make these benefits usable in practice, this developmen. This comprehensive textbook covers the fundamental principles and models underlying the theory, algorithms and systems aspects of distributed computing. Broad and detailed coverage of the theory is balanced with practical systems-related issues such as mutual exclusion, deadlock detection, authentication, and failure recovery. Algorithms are carefully selected, lucidly presented, and described without complex proofs. Simple explanations and illustrations are used to elucidate the algorithms. Important emerging topics such as peer-to-peer networks and network security are also considered. With vital algorithms, numerous illustrations, examples and homework problems, this textbook is suitable for advanced undergraduate and graduate students of electrical and computer engineering and computer science. Practitioners in data networking and sensor networks will also find this a valuable resource.Deadlock detection in distributed systems 11. Global predicate detection 12. Distributed shared memory 13. Checkpointing and rollback recovery 14. Consensus and agreement algorithms 15. Failure detectors 16. Authentication in distributed system 17. Self-stabilization 18. Peer-to-peer computing and overlay graphs Index.
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Look Inside Copyright Information Page (77 KB) Front Matter (198 KB) Index (113 KB) Table of Contents (113 KB) Marketing Excerpt (205 KB) Access to locked resources is granted exclusively by Cambridge University Press to lecturers whose faculty status has been verified. To gain access to locked resources, lecturers shouldOther lecturers may wish to use locked resources for assessment purposes and their usefulness is undermined when the source files (for example, solution manuals or test banks) are shared online or via social networks.Lecturers are permitted to view, print or download these resources for use in their teaching, but may not change them or use them for commercial gain.Create an account now. If you are having problems accessing these resources please emailYour eBook purchase and download will be. ISBN: 9780521189842, paperback edition. Cambridge University Press, March 2011. 756 pages.Jan 2011 edition and the South Asia Edition (2010). Create one here. Creators are allowed to post content they produce to the platform, so long as they comply with our policies. United Kingdom. Company number 10637289. Please try again.Please try your request again later. This comprehensive textbook covers the fundamental principles and models underlying the theory, algorithms and systems aspects of distributed computing. Broad and detailed coverage of the theory is balanced with practical systems-related issues such as mutual exclusion, deadlock detection, authentication, and failure recovery. Algorithms are carefully selected, lucidly presented, and described without complex proofs. Simple explanations and illustrations are used to elucidate the algorithms. Important emerging topics such as peer-to-peer networks and network security are also considered. With vital algorithms, numerous illustrations, examples and homework problems, this textbook is suitable for advanced undergraduate and graduate students of electrical and computer engineering and computer science.
Practitioners in data networking and sensor networks will also find this a valuable resource.In order to navigate out of this carousel please use your heading shortcut key to navigate to the next or previous heading. Page 1 of 1 Start over Page 1 of 1 In order to navigate out of this carousel please use your heading shortcut key to navigate to the next or previous heading. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required. Get your Kindle here, or download a FREE Kindle Reading App. In order to navigate out of this carousel please use your heading shortcut key to navigate to the next or previous heading. Please try again.Please try your request again later. This comprehensive textbook covers the fundamental principles and models underlying the theory, algorithms and systems aspects of distributed computing. Broad and detailed coverage of the theory is balanced with practical systems-related issues such as mutual exclusion, deadlock detection, authentication, and failure recovery. Algorithms are carefully selected, lucidly presented, and described without complex proofs. Simple explanations and illustrations are used to elucidate the algorithms. Important emerging topics such as peer-to-peer networks and network security are also considered. With vital algorithms, numerous illustrations, examples and homework problems, this textbook is suitable for advanced undergraduate and graduate students of electrical and computer engineering and computer science. Practitioners in data networking and sensor networks will also find this a valuable resource.Page 1 of 1 Start over Page 1 of 1 In order to navigate out of this carousel please use your heading shortcut key to navigate to the next or previous heading. In order to navigate out of this carousel please use your heading shortcut key to navigate to the next or previous heading.
Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required. Get your Kindle here, or download a FREE Kindle Reading App. Instructor's solutions manual is provided gratis by Springer to instructors who adopt the textbook.These presentation files were originally prepared in 2005 and were last updated on the dates shown.Through a steady stream of experimental research, tool-building efforts, and theoretical studies, the design of an instruction-set architecture, once considered an art, has been transformed into one of the most quantitative branches of computer technology. At the same time, better understanding of various forms of concurrency, from standard pipelining to massive parallelism, and invention of architectural structures to support a reasonably efficient and user-friendly programming model for such systems, has allowed hardware performance to continue its exponential growth. This trend is expected to continue in the near future. It has led to unprecedented hardware complexity and almost intolerable development costs. The challenge facing current and future computer designers is to institute simplicity where we now have complexity; to use fundamental theories being developed in this area to gain performance and ease-of-use benefits from simpler circuits; to understand the interplay between technological capabilities and limitations, on the one hand, and design decisions based on user and application requirements on the other. High-performance uniprocessors are becoming increasingly complex, expensive, and power-hungry. A basic tradeoff thus exists between the use of one or a small number of such complex processors, at one extreme, and a moderate to very large number of simpler processors, at the other. When combined with a high-bandwidth, but logically simple, interprocessor communication facility, the latter approach leads to significant simplification of the design process.
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First, at very high clock rates, the link between the processor and memory becomes very critical. CPUs can no longer be designed and verified in isolation. VLSI technology now allows us to put more transistors on a chip than required by even the most advanced superscalar processor. The bulk of these transistors are now being used to provide additional on-chip memory. However, they can just as easily be used to build multiple processors on a single chip. Emergence of multiple-processor microchips, along with currently available methods for glueless combination of several chips into a larger system and maturing standards for parallel machine models, holds the promise for making parallel processing more practical. New parallel architectures appear with amazing regularity in technical publications, while older architectures are studied and analyzed in novel and insightful ways. The wealth of published theoretical and practical results on parallel architectures and algorithms is truly awe-inspiring. The emergence of standard programming and communication models has removed some of the concerns with compatibility and software design issues in parallel processing, thus resulting in new designs and products with mass-market appeal. Given the computation-intensive nature of many application areas (such as encryption, physical modeling, and multimedia), parallel processing will continue to thrive for years to come. Packing many processors in a computer might constitute as much a part of a future computer architect's toolbox as pipelining, cache memories, and multiple instruction issue do today. In this scenario, even though the multiplicity of processors will not affect the end user or even the professional programmer (other than of course boosting the system performance), the number might be mentioned in sales literature to lure customers, in the same way that clock frequency and cache size are now used.
The challenge will then shift from making parallel processing work to incorporating a larger number of processors, more economically and in a truly seamless fashion.Each of these books has its unique strengths and has contributed to the formation and fruition of the field. The current text, Introduction to Parallel Processing: Algorithms and Architectures, is an outgrowth of lecture notes that the author has developed and refined over many years, beginning in the mid-1980s. Here are the most important features of this text in comparison to the listed books: In my approach to teaching, a lecture is a more or less self-contained module with links to past lectures and pointers to what will transpire in the future. Each lecture must have a theme or title and must proceed from motivation, to details, to conclusion. There must be smooth transitions between lectures and a clear enunciation of how each lecture fits into the overall plan. In designing the text, I have strived to divide the material into chapters, each of which is suitable for one lecture (1-2 hours). A short lecture can cover the first few subsections while a longer lecture might deal with more advanced material near the end. To make the structure hierarchical, as opposed to flat or linear, chapters have been grouped into six parts, each composed of four closely related chapters (see the diagram above). These are well-thought-out problems, many of them class-tested, that complement the material in the chapter, introduce new viewing angles, and link the chapter material to topics in other chapters. The ability to cope with complexity requires both a deep knowledge of the theoretical underpinnings of parallel processing and examples of designs that help us understand the theory. This viewpoint is reflected, e.g., in the coverage of problem-driven parallel machine designs (Chapter 8) that point to the origins of the butterfly and binary-tree architectures.
Other examples are found in Chapter 16 where a variety of composite and hierarchical architectures are discussed and some fundamental cost-performance trade-offs in network design are exposed. Fifteen carefully chosen case studies in Chapters 21-23 provide additional insight and motivation for the theories discussed. Parallel computing is nourished by, and in turn feeds, other subfields of computer architecture and technology. Examples of such links abound. In computer arithmetic, the design of high-speed adders and multipliers contributes to, and borrows many methods from, parallel processing.The current text covers virtually all important architectural and algorithmic topics in parallel processing, thus offering a balanced and complete view of the field. Coverage of the circuit model and problem-driven parallel machines (Chapters 7 and 8), some variants of mesh architectures (Chapter 12), composite and hierarchical systems (Chapter 16), which are becoming increasingly important for overcoming VLSI layout and packaging constraints, and the topics in Part V (Chapters 17-20) do not all appear in other textbooks. Similarly, other books that cover the foundations of parallel processing do not contain discussions on practical implementation issues and case studies of the type found in Part VI. For example, n always stands for the number of data elements (problem size) and p for the number of processors. While other authors have done this in the basic parts of their texts, there is a tendency to cover more advanced research topics by simply borrowing the notation and terminology from the reference source. Such an approach has the advantage of making the transition between reading the text and the original reference source easier, but it is utterly confusing to the majority of the students who rely on the text and do not consult the original references except, perhaps, to write a research paper.In my own teaching, I have used the chapters primarily for 1.
5-hour lectures, twice a week, in a 10-week quarter, omitting or combining some chapters to fit the material into 18-20 lectures. But the modular structure of the text lends itself to other lecture formats, self-study, or review of the field by practitioners. In the latter two cases, the readers can view each chapter as a study unit (for 1 week, say) rather than as a lecture. Ideally, all topics in each chapter should be covered before moving to the next chapter. However, if fewer lecture hours are available, then some of the subsections located at the end of chapters can be omitted or introduced only in terms of motivations and key results. These problems form an integral part of the book and have not been added as afterthoughts to make the book more attractive for use as a text. A total of 358 problems are included (13-16 per chapter).The author's detailed syllabus for the course ECE 254B at UCSB is available from the author's Web page for the course. The articles in IEEE Computer provide excellent starting points for research projects and term papers. The text has benefited greatly from keen observations, curiosity, and encouragement of my many students in these courses. A sincere thanks to all of them. Particular thanks go to Dr. Ding-Ming Kwai who read an early version of the manuscript carefully and suggested numerous corrections and improvements.Held every 1.5-2 years since 1986. The 6th FMPC was held in Annapolis, MD, October 27-31, 1996, and the 7th is planned for February 20-25, 1999. Held annually since 1972. The 27th ICPP was held in Minneapolis, MN, August 10-15, 1998, and the 28th is scheduled for September 21-24, 1999, in Aizu, Japan. Held annually since 1987. The 11th IPPS was held in Geneva, Switzerland, April 1-5, 1997. Held annually since 1989. Held annually since 1989, except for 1997. The 8th SPDP was held in New Orleans, LA, October 23-26, 1996.
While being successful in covering the main topics of parallel processing, the author has managed to arrive at an evenly divided book with fairly self-contained sections. As shown in page (xi), the book is comprised of six parts with each part containing four chapters. In the book, theoretical models of parallel processing are described and accompanied by techniques for exact analysis of parallel machines.Chapter 1 dwells on basic concepts of parallel processing. Fundamental computations including semigroup and parallel prefix computations are covered in Chapter 2. The reader here is introduced to simple parallel architectures such as linear array, binary tree, and 2D mesh. Most common communication primitives such as one to one routing, broadcasting, and sorting are described in conjunction with the said three architectures. In Chapter 3, asymptotic complexity along with complexity classes (NP-complete, NP, P, NC, etc.) is covered in detail. The author has done a good job in clarifying facts and fallacies of parallel processing. Chapter 4 concludes the first part of the book by intruding Flynn's classification of parallel machines, the PRAM model, and global versus distributed memory. It then describes, in more detail than earlier chapters, the basic parallel algorithms. Algorithms aimed at systems with shared memory are discussed in Chapter 6. Synthesis and analysis of sorting networks are presented in Chapter 7, which is followed by more coverage of architectures designed for such parallel algorithms as DFT and FFT. I think, therefore, devotion of the whole part to mesh topologies is appropriate and well justified. These networks generally have low diameter and node degree and offer the familiar trade-off between speed and cost. Emulation (embedding) among parallel architectures is also discussed in a general way, with specific problems given at the end of the chapter.
The author should be congratulated for composing a book, which appears to survive for years to come as it addresses, along with contemporary algorithms and architectures, concepts and innovations with little or no dependence on the current technology. Volume 1 presents solutions to selected problems and includes additional problems (many with solutions) that did not make the cut for inclusion in the text Introduction to Parallel Processing: Algorithms and Architectures (Plenum Press, 1999) or that were designed after the book went to print. Volume 2 contains enlarged versions of the figures and tables in the text as well as additional material, presented in a format that is suitable for use as transparency masters.Four new chapters, planned for the next revision of the textbook, are described in a separate section below.A fast general simulation would allow us to run sequential programs in parallel with good speed-up.This is quite unlikely. The networks of Dowd et al.For example, in multiplying large matrices, the speed-ups achieved are almost identical with 2 nodes at 4 threads, 4 nodes at 2 threads, and 8 nodes at a single thread. ASCI White is also described and depicted in the November 2001 issue of IEEE Spectrum, pp. 29-31. Now, it seems appropriate for shared-memory and circuit models to be described in greater detail and with more examples. New material associated with this revision and expansion will be posted here as they become available. Topics include principles of naming and location, atomicity, resource sharing, concurrency control and other synchronization, deadlock detection and avoidance, security, distributed data access and control, integration of operating systems and computer networks, distributed systems design, consistency control, and fault tolerance.