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Matrix Computations on Systolic-Type Arrays provides a framework which permits a good understanding of the features and limitations of processor arrays for matrix algorithms. It describes the tradeoffs among the characteristics of these systems, such as internal storage and communication bandwidth, and the impact on overall performance and cost. A system which allows for the analysis of methods for the design/mapping of matrix algorithms is also presented. This method identifies stages in the design/mapping process and the capabilities required at each stage. Matrix Computations on Systolic-Type Arrays provides a much needed description of the area of processor arrays for matrix algorithms and of the methods used to derive those arrays. The ideas developed here reduce the space of solutions in the design/mapping process by establishing clear criteria to select among possible options as well as by a-priori rejection of alternatives which are not adequate (but which are considered in other approaches). The end result is a method which is more specific than other techniques previously available (suitable for a class of matrix algorithms) but which is more systematic, better defined and more effective in reaching the desired objectives. Matrix Computations on Systolic-Type Arrays will interest researchers and professionals who are looking for systematic mechanisms to implement matrix algorithms either as algorithm-specific structures or using specialized architectures. It provides tools that simplify the design/mapping process without introducing degradation, and that permit tradeoffs between performance/cost measures selected by the designer.
This book constitutes the refereed proceedings of the Third International Euro-Par Conference, held in Passau, Germany, in August 1997. The 178 revised papers presented were selected from more than 300 submissions on the basis of 1101 reviews. The papers are organized in accordance with the conference workshop structure in tracks on support tools and environments, routing and communication, automatic parallelization, parallel and distributed algorithms, programming languages, programming models and methods, numerical algorithms, parallel architectures, HPC applications, scheduling and load balancing, performance evaluation, instruction-level parallelism, database systems, symbolic computation, real-time systems, and an ESPRIT workshop.
This concise text is designed to present the recent advances in parallel and distributed architectures and algorithms within an integrated framework. Beginning with an introduction to the basic concepts, the book goes on discussing the basic methods of parallelism exploitation in computation through vector processing, super scalar and VLIW processing, array processing, associative processing, systolic algorithms, and dataflow computation. After introducing interconnection networks, it discusses parallel algorithms for sorting, Fourier transform, matrix algebra, and graph theory. The second part focuses on basics and selected theoretical issues of distributed processing. Architectures and algorithms have been dealt in an integrated way throughout the book. The last chapter focuses on the different paradigms and issues of high performance computing making the reading more interesting. This book is meant for the senior level undergraduate and postgraduate students of computer science and engineering, and information technology. The book is also useful for the postgraduate students of computer science and computer application.
Electrical Engineering/Signal Processing High—Performance VLSI Signal Processing Innovative Architectures and Algorithms Volume 1 Algorithms and Architectures The first volume in a two-volume set, High-Performance VLSI Signal Processing: Innovative Architectures and Algorithms brings together the most innovative papers in the field, focused introductory material, and extensive references. The editors present timely coverage of algorithm and design methodologies with an emphasis on today’s rapidly-evolving high-speed architectures for VLSI implementations. These volumes will serve as vital resources for engineers who want a comprehensive knowledge of the extremely interdisciplinary field of high-performance VLSI processing. The editors provide a practical understanding of the merits of total system design through an insightful, synergistic presentation of methodology, architecture, and infrastructure. Each volume features: Major papers that span the wide range of research areas in the field Chapter introductions, including historical perspectives Numerous applications-oriented design examples Coverage of current and future technological trends Thorough treatment of high-speed architectures
The purpose of this annual series, Applied and Computational Control, Signals, and Circuits, is to keep abreast of the fast-paced developments in computational mathematics and scientific computing and their increasing use by researchers and engineers in control, signals, and circuits. The series is dedicated to fostering effective communication between mathematicians, computer scientists, computational scientists, software engineers, theorists, and practicing engineers. This interdisciplinary scope is meant to blend areas of mathematics (such as linear algebra, operator theory, and certain branches of analysis) and computational mathematics (numerical linear algebra, numerical differential equations, large scale and parallel matrix computations, numerical optimization) with control and systems theory, signal and image processing, and circuit analysis and design. The disciplines mentioned above have long enjoyed a natural synergy. There are distinguished journals in the fields of control and systems the ory, as well as signal processing and circuit theory, which publish high quality papers on mathematical and engineering aspects of these areas; however, articles on their computational and applications aspects appear only sporadically. At the same time, there has been tremendous recent growth and development of computational mathematics, scientific comput ing, and mathematical software, and the resulting sophisticated techniques are being gradually adapted by engineers, software designers, and other scientists to the needs of those applied disciplines.
This book constitutes the thoroughly refereed post-conference proceedings of the 7th International Conference on Parallel Processing and Applied Mathematics, PPAM 2007, held in Gdansk, Poland, in September 2007. The 63 revised full papers of the main conference presented together with 85 revised workshop papers were carefully reviewed and selected from over 250 initial submissions. The papers are organized in topical sections on parallel/distributed architectures and mobile computing, numerical algorithms and parallel numerics, parallel and distributed non-numerical algorithms, environments and tools for as well as applications of parallel/distributed/grid computing, evolutionary computing, meta-heuristics and neural networks. The volume proceeds with the outcome of 11 workshops and minisymposia dealing with novel data formats and algorithms for dense linear algebra computations, combinatorial tools for parallel sparse matrix computations, grid applications and middleware, large scale computations on grids, models, algorithms and methodologies for grid-enabled computing environments, scheduling for parallel computing, language-based parallel programming models, performance evaluation of parallel applications on large-scale systems, parallel computational biology, high performance computing for engineering applications, and the minisymposium on interval analysis.
Grid computing denotes an approach to utilize distributed resources that are not subject to centralized control. This approach fulfils computing requirements arising within the context of current high-performance computing applications, especially in the field of computational science and engineering.This idea is analogous to an electric power network (grid), where power generators are distributed, but the users are able to access electric power without bothering about the source of energy and its location.Current grid enabling technologies consist of stand-alone architectures. A typical architecture provides middleware access to various services at different hierarchical levels. Computational grids enable the sharing, selection and aggregation of a wide variety of geographically distributed computational resources (such as supercomputers, clusters of computers, storage systems, data sources, instruments, people, etc.) and present them as a single, unified resource for solving large-scale computations and data intensive computing applications (e.g., molecular modeling for drug design, brain activity analysis, high energy physics, etc.).Grid computing is a new emerging research area aiming to promote the development and advancement of technologies that provide seamless and scalable access to wide-area distributed resources.
Application-Driven Architecture Synthesis describes the state of the art of architectural synthesis for complex real-time processing. In order to deal with the stringent timing requirements and the intricacies of complex real-time signal and data processing, target architecture styles and target application domains have been adopted to make the synthesis approach feasible. These approaches are also heavily application-driven, which is illustrated by many realistic demonstrations, used as examples in the book. The focus is on domains where application-specific solutions are attractive, such as significant parts of audio, telecom, instrumentation, speech, robotics, medical and automotive processing, image and video processing, TV, multi-media, radar, sonar. Application-Driven Architecture Synthesis is of interest to both academics and senior design engineers and CAD managers in industry. It provides an excellent overview of what capabilities to expect from future practical design tools, and includes an extensive bibliography.
This monograph presents a unified mathematical framework for a wide range of problems in estimation and control. The authors discuss the two most commonly used methodologies: the stochastic H² approach and the deterministic (worst-case) H [infinity] approach. Despite the fundamental differences in the philosophies of these two approaches, the authors have discovered that, if indefinite metric spaces are considered, they can be treated in the same way and are essentially the same. The benefits and consequences of this unification are pursued in detail, with discussions of how to generalize well-known results from H² theory to H [infinity] setting, as well as new results and insight, the development of new algorithms, and applications to adaptive signal processing. The authors deliberately have placed primary emphasis on estimation problems which enable one to solve all the relevant control problems in detail. They also deal mostly with discrete-time systems, since these are the ones most important in current applications.
This three-volume work presents a compendium of current and seminal papers on parallel/distributed processing offered at the 22nd International Conference on Parallel Processing, held August 16-20, 1993 in Chicago, Illinois. Topics include processor architectures; mapping algorithms to parallel systems, performance evaluations; fault diagnosis, recovery, and tolerance; cube networks; portable software; synchronization; compilers; hypercube computing; and image processing and graphics. Computer professionals in parallel processing, distributed systems, and software engineering will find this book essential to complete their computer reference library.