Parallelization in Inference Systems


Book Description

This volume contains the proceedings of an international workshop on parallelism in inference systems held in Germany in December 1990. The topicof the workshop is still rather young and several papers in the book are overview articles intended to provide a first orientation toward some of the more intensively investigated subtopics. The main part of the book is a compilation of research papers on parallelization in special domains ofinference such as rewriting, automatic reasoning, logic programming, andconnectionist inference. Appended to the book is a collection of short project summaries received in response to a worldwide email call. The book is intended primarily for researchers working on inference systems who are interested in parallelizing their systems.




Parallel Language and Compiler Research in Japan


Book Description

Parallel Language and Compiler Research in Japan offers the international community an opportunity to learn in-depth about key Japanese research efforts in the particular software domains of parallel programming and parallelizing compilers. These are important topics that strongly bear on the effectiveness and affordability of high performance computing systems. The chapters of this book convey a comprehensive and current depiction of leading edge research efforts in Japan that focus on parallel software design, development, and optimization that could be obtained only through direct and personal interaction with the researchers themselves.




Parallel Processing for Artificial Intelligence 3


Book Description

The third in an informal series of books about parallel processing for Artificial Intelligence, this volume is based on the assumption that the computational demands of many AI tasks can be better served by parallel architectures than by the currently popular workstations. However, no assumption is made about the kind of parallelism to be used. Transputers, Connection Machines, farms of workstations, Cellular Neural Networks, Crays, and other hardware paradigms of parallelism are used by the authors of this collection.The papers arise from the areas of parallel knowledge representation, neural modeling, parallel non-monotonic reasoning, search and partitioning, constraint satisfaction, theorem proving, parallel decision trees, parallel programming languages and low-level computer vision. The final paper is an experience report about applications of massive parallelism which can be said to capture the spirit of a whole period of computing history.This volume provides the reader with a snapshot of the state of the art in Parallel Processing for Artificial Intelligence.




Parallel Processing for Artificial Intelligence 2


Book Description

With the increasing availability of parallel machines and the raising of interest in large scale and real world applications, research on parallel processing for Artificial Intelligence (AI) is gaining greater importance in the computer science environment. Many applications have been implemented and delivered but the field is still considered to be in its infancy.This book assembles diverse aspects of research in the area, providing an overview of the current state of technology. It also aims to promote further growth across the discipline. Contributions have been grouped according to their subject: architectures (3 papers), languages (4 papers), general algorithms (6 papers), and applications (5 papers). The internationally sourced papers range from purely theoretical work, simulation studies, algorithm and architecture proposals, to implemented systems and their experimental evaluation.Since the book is a second volume in the parallel processing for AI series, it provides a continued documentation of the research and advances made in the field. The editors hope that it will inspire readers to investigate the possiblities for enhancing AI systems by parallel processing and to make new discoveries of their own!




Future Parallel Computers


Book Description

Organized by the University of Pisa on behalf of the European Strategic Programme for Research and Development in Information Technology (ESPRIT)




Handbook of Parallel Constraint Reasoning


Book Description

This is the first book presenting a broad overview of parallelism in constraint-based reasoning formalisms. In recent years, an increasing number of contributions have been made on scaling constraint reasoning thanks to parallel architectures. The goal in this book is to overview these achievements in a concise way, assuming the reader is familiar with the classical, sequential background. It presents work demonstrating the use of multiple resources from single machine multi-core and GPU-based computations to very large scale distributed execution platforms up to 80,000 processing units. The contributions in the book cover the most important and recent contributions in parallel propositional satisfiability (SAT), maximum satisfiability (MaxSAT), quantified Boolean formulas (QBF), satisfiability modulo theory (SMT), theorem proving (TP), answer set programming (ASP), mixed integer linear programming (MILP), constraint programming (CP), stochastic local search (SLS), optimal path finding with A*, model checking for linear-time temporal logic (MC/LTL), binary decision diagrams (BDD), and model-based diagnosis (MBD). The book is suitable for researchers, graduate students, advanced undergraduates, and practitioners who wish to learn about the state of the art in parallel constraint reasoning.




Quantitative Methods in Parallel Systems


Book Description

It is widely recognized that the complexity of parallel and distributed systems is such that proper tools must be employed during their design stage in order to achieve the quantitative goals for which they are intended. This volume collects recent research results obtained within the Basic Research Action Qmips, which bears on the quantitative analysis of parallel and distributed architectures. Part 1 is devoted to research on the usage of general formalisms stemming from theoretical computer science in quantitative performance modeling of parallel systems. It contains research papers on process algebras, on Petri nets, and on queueing networks. The contributions in Part 2 are concerned with solution techniques. This part is expected to allow the reader to identify among the general formalisms of Part I, those that are amenable to an efficient mathematical treatment in the perspective of quantitative information. The common theme of Part 3 is the application of the analytical results of Part 2 to the performance evaluation and optimization of parallel and distributed systems. Part 1. Stochastic Process Algebras are used by N. Gotz, H. Hermanns, U. Herzog, V. Mertsiotakis and M. Rettelbach as a novel approach for the struc tured design and analysis of both the functional behaviour and performability (i.e performance and dependability) characteristics of parallel and distributed systems. This is achieved by integrating stochastic modeling and analysis into the powerful and well investigated formal description techniques of process algebras.




Parallel Inference Engine


Book Description

This text describes the machine model designed to support parallel interface, the design of the Kleng language, the design and implementation of the parallel interface engine, the programming tools, the runtime system, and some evaluation results. The architecture of the PIE 64 is tuned specially to support parallel inference. The compiler and runtime systems proposed here are designed to reduce the overhead that inevitably incurrs when using fine granularity processing.




Advanced Driver Intention Inference


Book Description

Advanced Driver Intention Inference: Theory and Design describes one of the most important function for future ADAS, namely, the driver intention inference. The book contains the state-of-art knowledge on the construction of driver intention inference system, providing a better understanding on how the human driver intention mechanism will contribute to a more naturalistic on-board decision system for automated vehicles. - Features examples of using machine learning/deep learning to build industry products - Depicts future trends for driver behavior detection and driver intention inference - Discuss traffic context perception techniques that predict driver intentions such as Lidar and GPS




FGCS '92


Book Description