Parallel and distributed computing are becoming increasingly important as cost-effective ways to achieve high computational performance. Symbolic computations are notable for their use of irregular data structures and hence parallel symbolic computing has its own distinctive set of technical challenges. The papers in this book are based on presentations made at a workshop at MIT in October 1992. They present results in a wide range of areas including: speculative computation, scheduling techniques, program development tools and environments, programming languages and systems, models of concurrency and distribution, parallel computer architecture, and symbolic applications.
These proceedings are devoted to communicating significant developments in all areas pertinent to Parallel Symbolic Computation.The scope includes algorithms, languages, software systems and application in any area of parallel symbolic computation, where parallelism is interpreted broadly to include concurrent, distributive, cooperative schemes, and so forth.
Focuses on the design and implementation of two classes of non-von Neumann computer architecture: those designed for functional and logical language computing.
Mathematics for Physical Science and Engineering is a complete text in mathematics for physical science that includes the use of symbolic computation to illustrate the mathematical concepts and enable the solution of a broader range of practical problems. This book enables professionals to connect their knowledge of mathematics to either or both of the symbolic languages Maple and Mathematica. The book begins by introducing the reader to symbolic computation and how it can be applied to solve a broad range of practical problems. Chapters cover topics that include: infinite series; complex numbers and functions; vectors and matrices; vector analysis; tensor analysis; ordinary differential equations; general vector spaces; Fourier series; partial differential equations; complex variable theory; and probability and statistics. Each important concept is clarified to students through the use of a simple example and often an illustration. This book is an ideal reference for upper level undergraduates in physical chemistry, physics, engineering, and advanced/applied mathematics courses. It will also appeal to graduate physicists, engineers and related specialties seeking to address practical problems in physical science. - Clarifies each important concept to students through the use of a simple example and often an illustration - Provides quick-reference for students through multiple appendices, including an overview of terms in most commonly used applications (Mathematica, Maple) - Shows how symbolic computing enables solving a broad range of practical problems
Parallel Computing: Methods, Algorithms and Applications presents a collection of original papers presented at the international meeting on parallel processing, methods, algorithms, and applications at Verona, Italy in September 1989.
This book contains papers presented at a workshop on the use of parallel techniques in symbolic and algebraic computation held at Cornell University in May 1990. The eight papers in the book fall into three groups. The first three papers discuss particular programming substrates for parallel symbolic computation, especially for distributed memory machines. The next three papers discuss novel ways of computing with elements of finite fields and with algebraic numbers. The finite field technique is especially interesting since it uses the Connection Machine, a SIMD machine, to achievesurprising amounts of parallelism. One of the parallel computing substrates is also used to implement a real root isolation technique. One of the crucial algorithms in modern algebraic computation is computing the standard, or Gr|bner, basis of an ideal. The final two papers discuss two different approaches to speeding their computation. One uses vector processing on the Cray and achieves significant speed-ups. The other uses a distributed memory multiprocessor and effectively explores the trade-offs involved with different interconnect topologies of the multiprocessors.
It has been widely recognized that artificial intelligence computations offer large potential for distributed and parallel processing. Unfortunately, not much is known about designing parallel AI algorithms and efficient, easy-to-use parallel computer architectures for AI applications. The field of parallel computation and computers for AI is in its infancy, but some significant ideas have appeared and initial practical experience has become available. The purpose of this book has been to collect in one volume contributions from several leading researchers and pioneers of AI that represent a sample of these ideas and experiences. This sample does not include all schools of thought nor contributions from all leading researchers, but it covers a relatively wide variety of views and topics and in this sense can be helpful in assessing the state ofthe art. We hope that the book will serve, at least, as a pointer to more specialized literature and that it will stimulate interest in the area of parallel AI processing. It has been a great pleasure and a privilege to cooperate with all contributors to this volume. They have my warmest thanks and gratitude. Mrs. Birgitta Knapp has assisted me in the editorial task and demonstrated a great deal of skill and patience. Janusz S. Kowalik vii INTRODUCTION Artificial intelligence (AI) computer programs can be very time-consuming.
Containing over 300 entries in an A-Z format, the Encyclopedia of Parallel Computing provides easy, intuitive access to relevant information for professionals and researchers seeking access to any aspect within the broad field of parallel computing. Topics for this comprehensive reference were selected, written, and peer-reviewed by an international pool of distinguished researchers in the field. The Encyclopedia is broad in scope, covering machine organization, programming languages, algorithms, and applications. Within each area, concepts, designs, and specific implementations are presented. The highly-structured essays in this work comprise synonyms, a definition and discussion of the topic, bibliographies, and links to related literature. Extensive cross-references to other entries within the Encyclopedia support efficient, user-friendly searchers for immediate access to useful information. Key concepts presented in the Encyclopedia of Parallel Computing include; laws and metrics; specific numerical and non-numerical algorithms; asynchronous algorithms; libraries of subroutines; benchmark suites; applications; sequential consistency and cache coherency; machine classes such as clusters, shared-memory multiprocessors, special-purpose machines and dataflow machines; specific machines such as Cray supercomputers, IBM’s cell processor and Intel’s multicore machines; race detection and auto parallelization; parallel programming languages, synchronization primitives, collective operations, message passing libraries, checkpointing, and operating systems. Topics covered: Speedup, Efficiency, Isoefficiency, Redundancy, Amdahls law, Computer Architecture Concepts, Parallel Machine Designs, Benmarks, Parallel Programming concepts & design, Algorithms, Parallel applications. This authoritative reference will be published in two formats: print and online. The online edition features hyperlinks to cross-references and to additional significant research. Related Subjects: supercomputing, high-performance computing, distributed computing
This book constitutes the refereed proceedings of the 6th International Conference on Parallel Computing Technologies, PaCT 2001, held in Novosibirsk, Russia in September 2001. The 36 revised full papers and 13 posters presented together with 4 invited papers were carefully reviewed and selected from 81 submissions. The papers presented span the whole range of parallel processing from theory and software through architecture and applications. Among the topics addressed are shared memory systems, formal methods, networks of processes, cellular automata, mobile data access systems, Java programming, neuro-cluster computing, network clusters, load balancing, etc.
This first volume in the series "Algorithms and Computation in Mathematics", is destined to become the standard reference work in the field. Manuel Bronstein is the number-one expert on this topic and his book is the first to treat the subject both comprehensively and in sufficient detail - incorporating new results along the way. The book addresses mathematicians and computer scientists interested in symbolic computation, developers and programmers of computer algebra systems as well as users of symbolic integration methods. Many algorithms are given in pseudocode ready for immediate implementation, making the book equally suitable as a textbook for lecture courses on symbolic integration.
