Despite the ample number of articles on parallel-vector computational algorithms published over the last 20 years, there is a lack of texts in the field customized for senior undergraduate and graduate engineering research. Parallel-Vector Equation Solvers for Finite Element Engineering Applications aims to fill this gap, detailing both the theoretical development and important implementations of equation-solution algorithms. The mathematical background necessary to understand their inception balances well with descriptions of their practical uses. Illustrated with a number of state-of-the-art FORTRAN codes developed as examples for the book, Dr. Nguyen's text is a perfect choice for instructors and researchers alike.

In this volume authors of academia and practice provide practitioners, scientists and graduate students with a good overview of basic methods and paradigms, as well as important issues and trends across the broad spectrum of parallel and distributed processing. In particular, the book covers fundamental topics such as efficient parallel algorithms, languages for parallel processing, parallel operating systems, architecture of parallel and distributed systems, management of resources, tools for parallel computing, parallel database systems and multimedia object servers, and networking aspects of distributed and parallel computing. Three chapters are dedicated to applications: parallel and distributed scientific computing, high-performance computing in molecular sciences, and multimedia applications for parallel and distributed systems. Summing up, the Handbook is indispensable for academics and professionals who are interested in learning the leading expert`s view of the topic.

This book presents the state-of-the-art in simulation on supercomputers. Leading researchers present results achieved on systems of the Gauss-Allianz, the association of High-Performance Computing centers in Germany. The reports cover all fields of computational science and engineering, ranging from CFD to Computational Physics and Biology to Computer Science, with a special emphasis on industrially relevant applications. Presenting results for large-scale parallel microprocessor-based systems and GPU and FPGA-supported systems, the book makes it possible to compare the performance levels and usability of various architectures. Its outstanding results in achieving the highest performance for production codes are of particular interest for both scientists and engineers. The book includes a wealth of color illustrations and tables.

This textbook presents a survey of research on boolean functions, circuits, parallel computation models, function algebras, and proof systems. Its main aim is to elucidate the structure of "fast" parallel computation. The complexity of parallel computation is emphasized through a variety of techniques ranging from finite combinatorics, probability theory and finite group theory to finite model theory and proof theory. Nonuniform computation models are studied in the form of boolean circuits; uniform ones in a variety of forms. Steps in the investigation of non-deterministic polynomial time are surveyed as is the complexity of various proof systems. The book will benefit advanced undergraduates and graduate students as well as researchers in the field of complexity theory.

A wide range of modern computer applications require the performance and flexibility of parallel and distributed systems. Better software support is required if the technical advances in these systems are to be fully exploited by commerce and industry. This involves the provision of specialised techniques and tools as well as the integration of standard software engineering methods. This book will reflect current advances in this area, and will address issues of theory and practice with contributions from academia and industry. It is the aim of the book to provide a focus for information on this developing which will be of use to both researchers and practitioners.

Loop tiling, as one of the most important compiler optimizations, is beneficial for both parallel machines and uniprocessors with a memory hierarchy. This book explores the use of loop tiling for reducing communication cost and improving parallelism for distributed memory machines. The author provides mathematical foundations, investigates loop permutability in the framework of nonsingular loop transformations, discusses the necessary machineries required, and presents state-of-the-art results for finding communication- and time-minimal tiling choices. Throughout the book, theorems and algorithms are illustrated with numerous examples and diagrams. The techniques presented in Loop Tiling for Parallelism can be adapted to work for a cluster of workstations, and are also directly applicable to shared-memory machines once the machines are modeled as BSP (Bulk Synchronous Parallel) machines. Features and key topics: Detailed review of the mathematical foundations, including convex polyhedra and cones; Self-contained treatment of nonsingular loop transformations, code generation, and full loop permutability; Tiling loop nests by rectangles and parallelepipeds, including their mathematical definition, dependence analysis, legality test, and code generation; A complete suite of techniques for generating SPMD code for a tiled loop nest; Up-to-date results on tile size and shape selection for reducing communication and improving parallelism; End-of-chapter references for further reading. Researchers and practitioners involved in optimizing compilers and students in advanced computer architecture studies will find this a lucid and well-presented reference work with numerous citations to original sources.

Scheduling in Parallel Computing Systems: Fuzzy and Annealing Techniques advocates the viability of using fuzzy and annealing methods in solving scheduling problems for parallel computing systems. The book proposes new techniques for both static and dynamic scheduling, using emerging paradigms that are inspired by natural phenomena such as fuzzy logic, mean-field annealing, and simulated annealing. Systems that are designed using such techniques are often referred to in the literature as intelligent' because of their capability to adapt to sudden changes in their environments. Moreover, most of these changes cannot be anticipated in advance or included in the original design of the system. Scheduling in Parallel Computing Systems: Fuzzy and Annealing Techniques provides results that prove such approaches can become viable alternatives to orthodox solutions to the scheduling problem, which are mostly based on heuristics. Although heuristics are robust and reliable when solving certain instances of the scheduling problem, they do not perform well when one needs to obtain solutions to general forms of the scheduling problem. On the other hand, techniques inspired by natural phenomena have been successfully applied for solving a wide range of combinatorial optimization problems (e.g. traveling salesman, graph partitioning). The success of these methods motivated their use in this book to solve scheduling problems that are known to be formidable combinatorial problems. Scheduling in Parallel Computing Systems: Fuzzy and Annealing Techniques is an excellent reference and may be used for advanced courses on the topic.

Input/Output in Parallel and Distributed Computer Systems has attracted increasing attention over the last few years, as it has become apparent that input/output performance, rather than CPU performance, may be the key limiting factor in the performance of future systems. This I/O bottleneck is caused by the increasing speed mismatch between processing units and storage devices, the use of multiple processors operating simultaneously in parallel and distributed systems, and by the increasing I/O demands of new classes of applications, like multimedia. It is also important to note that, to varying degrees, the I/O bottleneck exists at multiple levels of the memory hierarchy. All indications are that the I/O bottleneck will be with us for some time to come, and is likely to increase in importance. Input/Output in Parallel and Distributed Computer Systems is based on papers presented at the 1994 and 1995 IOPADS workshops held in conjunction with the International Parallel Processing Symposium. This book is divided into three parts. Part I, the Introduction, contains four invited chapters which provide a tutorial survey of I/O issues in parallel and distributed systems. The chapters in Parts II and III contain selected research papers from the 1994 and 1995 IOPADS workshops; many of these papers have been substantially revised and updated for inclusion in this volume. Part II collects the papers from both years which deal with various aspects of system software, and Part III addresses architectural issues. Input/Output in Parallel and Distributed Computer Systems is suitable as a secondary text for graduate level courses in computer architecture, software engineering, and multimedia systems, and as a reference for researchers and practitioners in industry.

Language, Compilers and Run-time Systems for Scalable Computers contains 20 articles based on presentations given at the third workshop of the same title, and 13 extended abstracts from the poster session. Starting with new developments in classical problems of parallel compiler design, such as dependence analysis and an exploration of loop parallelism, the book goes on to address the issues of compiler strategy for specific architectures and programming environments. Several chapters investigate support for multi-threading, object orientation, irregular computation, locality enhancement, and communication optimization. Issues of the interface between language and operating system support are also discussed. Finally, the load balance issues are discussed in different contexts, including sparse matrix computation and iteratively balanced adaptive solvers for partial differential equations. Some additional topics are also discussed in the extended abstracts. Each chapter provides a bibliography of relevant papers and the book can thus be used as a reference to the most up-to-date research in parallel software engineering.

Over the last fifteen years GIS has become a fully-fledged technology, deployed across a range of application areas. However, although computer advances in performance appear to continue unhindered, data volumes and the growing sophistication of analysis procedures mean that performance will increasingly become a serious concern in GIS. Parallel computing offers a potential solution. However, traditional algorithms may not run effectively in a parallel environment, so utilization of parallel technology is not entirely straightforward. This groundbreaking book examines some of the current strategies facing scientists and engineers at this crucial interface of parallel computing and GIS.; The book begins with an introduction to the concepts, terminology and techniques of parallel processing, with particular reference to GIS. High level programming paradigms and software engineering issues underlying parallel software developments are considered and emphasis is given to designing modular reusable software libraries. The book continues with problems in designing parallel software for GIS applications, potential vector and raster data structures and details the algorithmic design for some major GIS operations. An implementation case study is included, based around a raster generalization problem, which illustrates some of the principles involved. Subsequent chapters review progress in parallel database technology in a GIS environment and the use of parallel techniques in various application areas, dealing with both algorithmic and implementation issues.; "Parallel Processing Algorithms for GIS" should be a useful text for a new generation of GIS professionals whose principal concern is the challenge of embracing major computer performance enhancements via parallel computing. Similarly, it should be an important volume for parallel computing professionals who are increasingly aware that GIS offers a major application domain for their technology.

Concurrent data structures simplify the development of concurrent programs by encapsulating commonly used mechanisms for synchronization and commu nication into data structures. This thesis develops a notation for describing concurrent data structures, presents examples of concurrent data structures, and describes an architecture to support concurrent data structures. Concurrent Smalltalk (CST), a derivative of Smalltalk-80 with extensions for concurrency, is developed to describe concurrent data structures. CST allows the programmer to specify objects that are distributed over the nodes of a concurrent computer. These distributed objects have many constituent objects and thus can process many messages simultaneously. They are the foundation upon which concurrent data structures are built. The balanced cube is a concurrent data structure for ordered sets. The set is distributed by a balanced recursive partition that maps to the subcubes of a binary 7lrcube using a Gray code. A search algorithm, VW search, based on the distance properties of the Gray code, searches a balanced cube in O(log N) time. Because it does not have the root bottleneck that limits all tree-based data structures to 0(1) concurrency, the balanced cube achieves 0C.: N) con currency. Considering graphs as concurrent data structures, graph algorithms are pre sented for the shortest path problem, the max-flow problem, and graph parti tioning. These algorithms introduce new synchronization techniques to achieve better performance than existing algorithms."

Parallel and distributed computation has been gaining a great lot of attention in the last decades. During this period, the advances attained in computing and communication technologies, and the reduction in the costs of those technolo gies, played a central role in the rapid growth of the interest in the use of parallel and distributed computation in a number of areas of engineering and sciences. Many actual applications have been successfully implemented in various plat forms varying from pure shared-memory to totally distributed models, passing through hybrid approaches such as distributed-shared memory architectures. Parallel and distributed computation differs from dassical sequential compu tation in some of the following major aspects: the number of processing units, independent local dock for each unit, the number of memory units, and the programming model. For representing this diversity, and depending on what level we are looking at the problem, researchers have proposed some models to abstract the main characteristics or parameters (physical components or logical mechanisms) of parallel computers. The problem of establishing a suitable model is to find a reasonable trade-off among simplicity, power of expression and universality. Then, be able to study and analyze more precisely the behavior of parallel applications."

High Performance Computational Methods for Biological Sequence Analysis presents biological sequence analysis using an interdisciplinary approach that integrates biological, mathematical and computational concepts. These concepts are presented so that computer scientists and biomedical scientists can obtain the necessary background for developing better algorithms and applying parallel computational methods. This book will enable both groups to develop the depth of knowledge needed to work in this interdisciplinary field. This work focuses on high performance computational approaches that are used to perform computationally intensive biological sequence analysis tasks: pairwise sequence comparison, multiple sequence alignment, and sequence similarity searching in large databases. These computational methods are becoming increasingly important to the molecular biology community allowing researchers to explore the increasingly large amounts of sequence data generated by the Human Genome Project and other related biological projects. The approaches presented by the authors are state-of-the-art and show how to reduce analysis times significantly, sometimes from days to minutes. High Performance Computational Methods for Biological Sequence Analysis is tremendously important to biomedical science students and researchers who are interested in applying sequence analyses to their studies, and to computational science students and researchers who are interested in applying new computational approaches to biological sequence analyses.

Fault-Tolerant Parallel Computation presents recent advances in algorithmic ways of introducing fault-tolerance in multiprocessors under the constraint of preserving efficiency. The difficulty associated with combining fault-tolerance and efficiency is that the two have conflicting means: fault-tolerance is achieved by introducing redundancy, while efficiency is achieved by removing redundancy. This monograph demonstrates how in certain models of parallel computation it is possible to combine efficiency and fault-tolerance and shows how it is possible to develop efficient algorithms without concern for fault-tolerance, and then correctly and efficiently execute these algorithms on parallel machines whose processors are subject to arbitrary dynamic fail-stop errors. The efficient algorithmic approaches to multiprocessor fault-tolerance presented in this monograph make a contribution towards bridging the gap between the abstract models of parallel computation and realizable parallel architectures. Fault-Tolerant Parallel Computation presents the state of the art in algorithmic approaches to fault-tolerance in efficient parallel algorithms. The monograph synthesizes work that was presented in recent symposia and published in refereed journals by the authors and other leading researchers. This is the first text that takes the reader on the grand tour of this new field summarizing major results and identifying hard open problems. This monograph will be of interest to academic and industrial researchers and graduate students working in the areas of fault-tolerance, algorithms and parallel computation and may also be used as a text in a graduate course on parallel algorithmic techniques and fault-tolerance.

In Symbolic Analysis for Parallelizing Compilers the author presents an excellent demonstration of the effectiveness of symbolic analysis in tackling important optimization problems, some of which inhibit loop parallelization. The framework that Haghighat presents has proved extremely successful in induction and wraparound variable analysis, strength reduction, dead code elimination and symbolic constant propagation. The approach can be applied to any program transformation or optimization problem that uses properties and value ranges of program names. Symbolic analysis can be used on any transformational system or optimization problem that relies on compile-time information about program variables. This covers the majority of, if not all optimization and parallelization techniques. The book makes a compelling case for the potential of symbolic analysis, applying it for the first time - and with remarkable results - to a number of classical optimization problems: loop scheduling, static timing or size analysis, and dependence analysis. It demonstrates how symbolic analysis can solve these problems faster and more accurately than existing hybrid techniques.

Efficient parallel solutions have been found to many problems. Some of them can be obtained automatically from sequential programs, using compilers. However, there is a large class of problems - irregular problems - that lack efficient solutions. IRREGULAR 94 - a workshop and summer school organized in Geneva - addressed the problems associated with the derivation of efficient solutions to irregular problems. This book, which is based on the workshop, draws on the contributions of outstanding scientists to present the state of the art in irregular problems, covering aspects ranging from scientific computing, discrete optimization, and automatic extraction of parallelism. Audience: This first book on parallel algorithms for irregular problems is of interest to advanced graduate students and researchers in parallel computer science.

Based on the Lectures given during the Eurocourse on 'Computing with Parallel Architectures' held at the Joint Research Centre Ispra, Italy, September 10-14, 1990

The one instruction set computer (OISC) is the ultimate reduced instruction set computer (RISC). In OISC, the instruction set consists of only one instruction, and then by composition, all other necessary instructions are synthesized. This is an approach completely opposite to that of a complex instruction set computer (CISC), which incorporates complex instructions as microprograms within the processor.

Computer Architecture: A Minimalist Perspective examines computer architecture, computability theory, and the history of computers from the perspective of one instruction set computing - a novel approach in which the computer supports only one, simple instruction. This bold, new paradigm offers significant promise in biological, chemical, optical, and molecular scale computers.
Features include:

- Provides a comprehensive study of computer architecture using computability theory as a base.
- Provides a fresh perspective on computer architecture not found in any other text.
- Covers history, theory, and practice of computer architecture from a minimalist perspective. Includes a complete implementation of a one instruction computer.
- Includes exercises and programming assignments. Computer Architecture: A Minimalist Perspective is designed to meet the needs of a professional audience composed of researchers, computer hardware engineers, software engineers computational theorists, and systems engineers. The book is also intended for use in upper division undergraduate students and early graduate students studying computer architecture or embedded systems. It is an excellent text for use as a supplement or alternative in traditional Computer Architecture Courses, orin courses entitled "Special Topics in Computer Architecture."

The Rust programming language is extremely well-suited for concurrency, and its ecosystem has many libraries that include lots of concurrent data structures, locks, and more. But implementing those structures correctly can be very difficult. Even in the most well-used libraries, memory ordering bugs are not uncommon. In this practical book, Mara Bos, leader of the Rust library team, helps Rust programmers of all levels gain a clear understanding of low-level concurrency. You'll learn everything about atomics and memory ordering and how they're combined with basic operating system APIs to build common primitives like mutexes and condition variables. Once you're done, you'll have a firm grasp of how Rust's memory model, the processor, and the roles of the operating system all fit together. With this guide, you'll learn: How Rust's type system works exceptionally well for programming concurrency correctly All about mutexes, condition variables, atomics, and memory ordering What happens in practice with atomic operations on Intel and ARM processors How locks are implemented with support from the operating system How to write correct code that includes concurrency, atomics, and locks How to build your own locking and synchronization primitives correctly

Load Balancing in Parallel Computers: Theory and Practice is about the essential software technique of load balancing in distributed memory message-passing parallel computers, also called multicomputers. Each processor has its own address space and has to communicate with other processors by message passing. In general, a direct, point-to-point interconnection network is used for the communications. Many commercial parallel computers are of this class, including the Intel Paragon, the Thinking Machine CM-5, and the IBM SP2. Load Balancing in Parallel Computers: Theory and Practice presents a comprehensive treatment of the subject using rigorous mathematical analyses and practical implementations. The focus is on nearest-neighbor load balancing methods in which every processor at every step is restricted to balancing its workload with its direct neighbours only. Nearest-neighbor methods are iterative in nature because a global balanced state can be reached through processors' successive local operations. Since nearest-neighbor methods have a relatively relaxed requirement for the spread of local load information across the system, they are flexible in terms of allowing one to control the balancing quality, effective for preserving communication locality, and can be easily scaled in parallel computers with a direct communication network. Load Balancing in Parallel Computers: Theory and Practice serves as an excellent reference source and may be used as a text for advanced courses on the subject.

This book covers the essential elements of parallel processing and parallel algorithms. It is unique in that it is a self-contained book covering everything fundamental of parallel processing from computer architecture to parallel programming and parallel algorithms. It is designed to function as a text for an undergraduate course in parallel processing, but also works well as a comprehensive reference for professionals interested in all phases of parallel processing and parallel programming.

Computer vision falls short of human vision in two respects: execution time and intelligent interpretation. This book addresses the question of execution time. It is based on a workshop on specialized processors for real-time image analysis, held as part of the activities of an ESPRIT Basic Research Action, the Working Group on Vision. The aim of the book is to examine the state of the art in vision-oriented computers. Two approaches are distinguished: multiprocessor systems and fine-grain massively parallel computers. The development of fine-grain machines has become more important over the last decade, but one of the main conclusions of the workshop is that this does not imply the replacement of multiprocessor machines. The book is divided into four parts. Part 1 introduces different architectures for vision: associative and pyramid processors as examples of fine-grain machines and a workstation with bus-oriented network topology as an example of a multiprocessor system. Parts 2 and 3 deal with the design and development of dedicated and specialized architectures. Part 4 is mainly devoted to applications, including road segmentation, mobile robot guidance and navigation, reconstruction and identification of 3D objects, and motion estimation.

This book provides an introduction to decision making in a distributed computational framework. Classical detection theory assumes a centralized configuration. All observations are processed by a central processor to produce the decision. In the decentralized detection system, distributed detectors generate decisions based on locally available observations; these decisions are then conveyed to the fusion center that makes the global decision. Using numerous examples throughout the book, the author discusses such distributed detection processes under several different formulations and in a wide variety of network topologies.

Topics covered include: elements of detection theory; distributed Bayesian detection in parallel fusion networks and in other network topologies; distributed detection with false-alarm rate constraints; distributed sequential detection; and information theory and distributed hypotheses testing.

By providing a unified treatment of the recent advances, this book should prove valuable not only to researchers active in the field, but also to graduate students and others embarking on research in detection, signal processing, and statistical decision theory.

Distributed and Parallel Systems: Cluster and Grid Computing is the proceedings of the fourth Austrian-Hungarian Workshop on Distributed and Parallel Systems organized jointly by Johannes Kepler University, Linz, Austria and the MTA SZTAKI Computer and Automation Research Institute.

The papers in this volume cover a broad range of research topics presented in four groups. The first one introduces cluster tools and techniques, especially the issues of load balancing and migration. Another six papers deal with grid and global computing including grid infrastructure, tools, applications and mobile computing. The next nine papers present general questions of distributed development and applications. The last four papers address a crucial issue in distributed computing: fault tolerance and dependable systems.

This volume will be useful to researchers and scholars interested in all areas related to parallel and distributed computing systems.