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Parallel Processing For Artificial Intelligence
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Book Synopsis Parallel Processing for Artificial Intelligence 1 by : L.N. Kanal
Download or read book Parallel Processing for Artificial Intelligence 1 written by L.N. Kanal and published by Elsevier. This book was released on 2014-06-28 with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt: Parallel processing for AI problems is of great current interest because of its potential for alleviating the computational demands of AI procedures. The articles in this book consider parallel processing for problems in several areas of artificial intelligence: image processing, knowledge representation in semantic networks, production rules, mechanization of logic, constraint satisfaction, parsing of natural language, data filtering and data mining. The publication is divided into six sections. The first addresses parallel computing for processing and understanding images. The second discusses parallel processing for semantic networks, which are widely used means for representing knowledge - methods which enable efficient and flexible processing of semantic networks are expected to have high utility for building large-scale knowledge-based systems. The third section explores the automatic parallel execution of production systems, which are used extensively in building rule-based expert systems - systems containing large numbers of rules are slow to execute and can significantly benefit from automatic parallel execution. The exploitation of parallelism for the mechanization of logic is dealt with in the fourth section. While sequential control aspects pose problems for the parallelization of production systems, logic has a purely declarative interpretation which does not demand a particular evaluation strategy. In this area, therefore, very large search spaces provide significant potential for parallelism. In particular, this is true for automated theorem proving. The fifth section considers the problem of constraint satisfaction, which is a useful abstraction of a number of important problems in AI and other fields of computer science. It also discusses the technique of consistent labeling as a preprocessing step in the constraint satisfaction problem. Section VI consists of two articles, each on a different, important topic. The first discusses parallel formulation for the Tree Adjoining Grammar (TAG), which is a powerful formalism for describing natural languages. The second examines the suitability of a parallel programming paradigm called Linda, for solving problems in artificial intelligence. Each of the areas discussed in the book holds many open problems, but it is believed that parallel processing will form a key ingredient in achieving at least partial solutions. It is hoped that the contributions, sourced from experts around the world, will inspire readers to take on these challenging areas of inquiry.
Book Synopsis Parallel Processing for Artificial Intelligence 3 by : J. Geller
Download or read book Parallel Processing for Artificial Intelligence 3 written by J. Geller and published by Elsevier. This book was released on 1997-02-10 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Parallel Processing and Parallel Algorithms by : Seyed H Roosta
Download or read book Parallel Processing and Parallel Algorithms written by Seyed H Roosta and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 579 pages. Available in PDF, EPUB and Kindle. Book excerpt: Motivation It is now possible to build powerful single-processor and multiprocessor systems and use them efficiently for data processing, which has seen an explosive ex pansion in many areas of computer science and engineering. One approach to meeting the performance requirements of the applications has been to utilize the most powerful single-processor system that is available. When such a system does not provide the performance requirements, pipelined and parallel process ing structures can be employed. The concept of parallel processing is a depar ture from sequential processing. In sequential computation one processor is in volved and performs one operation at a time. On the other hand, in parallel computation several processors cooperate to solve a problem, which reduces computing time because several operations can be carried out simultaneously. Using several processors that work together on a given computation illustrates a new paradigm in computer problem solving which is completely different from sequential processing. From the practical point of view, this provides sufficient justification to investigate the concept of parallel processing and related issues, such as parallel algorithms. Parallel processing involves utilizing several factors, such as parallel architectures, parallel algorithms, parallel programming lan guages and performance analysis, which are strongly interrelated. In general, four steps are involved in performing a computational problem in parallel. The first step is to understand the nature of computations in the specific application domain.
Book Synopsis Deep Learning and Parallel Computing Environment for Bioengineering Systems by : Arun Kumar Sangaiah
Download or read book Deep Learning and Parallel Computing Environment for Bioengineering Systems written by Arun Kumar Sangaiah and published by Academic Press. This book was released on 2019-07-26 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning and Parallel Computing Environment for Bioengineering Systems delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Managing the gathered knowledge and applying it to multiple domains including health care, social networks, mining, recommendation systems, image processing, pattern recognition and predictions using deep learning paradigms is the major strength of this book. This book integrates the core ideas of deep learning and its applications in bio engineering application domains, to be accessible to all scholars and academicians. The proposed techniques and concepts in this book can be extended in future to accommodate changing business organizations’ needs as well as practitioners’ innovative ideas. Presents novel, in-depth research contributions from a methodological/application perspective in understanding the fusion of deep machine learning paradigms and their capabilities in solving a diverse range of problems Illustrates the state-of-the-art and recent developments in the new theories and applications of deep learning approaches applied to parallel computing environment in bioengineering systems Provides concepts and technologies that are successfully used in the implementation of today's intelligent data-centric critical systems and multi-media Cloud-Big data
Book Synopsis Parallel Processing for Artificial Intelligence 2 by : V. Kumar
Download or read book Parallel Processing for Artificial Intelligence 2 written by V. Kumar and published by Elsevier. This book was released on 2014-06-28 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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!
Book Synopsis Vector Models for Data-parallel Computing by : Guy E. Blelloch
Download or read book Vector Models for Data-parallel Computing written by Guy E. Blelloch and published by MIT Press (MA). This book was released on 1990 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematics of Computing -- Parallelism.
Book Synopsis Parallel Processing and Artificial Intelligence by : Mike Reeve
Download or read book Parallel Processing and Artificial Intelligence written by Mike Reeve and published by . This book was released on 1989-09-28 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprises papers based on an international conference held at Imperial College, London, July 1989. Topics covered include neural networks, robotics, image understanding, parallel implementations of logic languages, and parallel implementation of Lisp. Many of the papers here detail use of the INMOS transputer, and the Communicating Process Architecture on which INMOS was founded. But the theme is application of parallelism in a general way, especially in artificial intelligence.
Book Synopsis Parallel Computation and Computers for Artificial Intelligence by : J.S. Kowalik
Download or read book Parallel Computation and Computers for Artificial Intelligence written by J.S. Kowalik and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Parallel Processing for Artificial Intelligence by : Laveen N. Kanal
Download or read book Parallel Processing for Artificial Intelligence written by Laveen N. Kanal and published by . This book was released on 1994 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Advances in Parallel Computing Technologies and Applications by : D.J. Hemanth
Download or read book Advances in Parallel Computing Technologies and Applications written by D.J. Hemanth and published by IOS Press. This book was released on 2021-11-25 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent developments in parallel computing mean that the use of machine learning techniques and intelligence to handle the huge volume of available data have brought the faster solutions offered by advanced technologies to various fields of application. This book presents the proceedings of the Virtual International Conference on Advances in Parallel Computing Technologies and Applications (ICAPTA 2021), hosted in Justice Basheer Ahmed Sayeed College for women (formerly "S.I.E.T Women's College"), Chennai, India, and held online as a virtual event on 15 and 16 April 2021. The aim of the conference was to provide a forum for sharing knowledge in various aspects of parallel computing in communications systems and networking, including cloud and virtualization solutions, management technologies, and vertical application areas. It also provided a platform for scientists, researchers, practitioners and academicians to present and discuss the most recent innovations and trends, as well as the concerns and practical challenges encountered in this field. Included here are 52 full length papers, selected from over 100 submissions based on the reviews and comments of subject experts. Topics covered include parallel computing in communication, machine learning intelligence for parallel computing and parallel computing for software services in theoretical and practical aspects. Providing an overview of the latest developments in the field, the book will be of interest to all those whose work involves the use of parallel computing technologies.
Book Synopsis Mining Very Large Databases with Parallel Processing by : Alex A. Freitas
Download or read book Mining Very Large Databases with Parallel Processing written by Alex A. Freitas and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mining Very Large Databases with Parallel Processing addresses the problem of large-scale data mining. It is an interdisciplinary text, describing advances in the integration of three computer science areas, namely `intelligent' (machine learning-based) data mining techniques, relational databases and parallel processing. The basic idea is to use concepts and techniques of the latter two areas - particularly parallel processing - to speed up and scale up data mining algorithms. The book is divided into three parts. The first part presents a comprehensive review of intelligent data mining techniques such as rule induction, instance-based learning, neural networks and genetic algorithms. Likewise, the second part presents a comprehensive review of parallel processing and parallel databases. Each of these parts includes an overview of commercially-available, state-of-the-art tools. The third part deals with the application of parallel processing to data mining. The emphasis is on finding generic, cost-effective solutions for realistic data volumes. Two parallel computational environments are discussed, the first excluding the use of commercial-strength DBMS, and the second using parallel DBMS servers. It is assumed that the reader has a knowledge roughly equivalent to a first degree (BSc) in accurate sciences, so that (s)he is reasonably familiar with basic concepts of statistics and computer science. The primary audience for Mining Very Large Databases with Parallel Processing is industry data miners and practitioners in general, who would like to apply intelligent data mining techniques to large amounts of data. The book will also be of interest to academic researchers and postgraduate students, particularly database researchers, interested in advanced, intelligent database applications, and artificial intelligence researchers interested in industrial, real-world applications of machine learning.
Book Synopsis Parallel Processing for Artificial Intelligence by : V. Kumar
Download or read book Parallel Processing for Artificial Intelligence written by V. Kumar and published by . This book was released on 1985 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis GPU Parallel Computing for Machine Learning in Python by : Yoshiyasu Takefuji
Download or read book GPU Parallel Computing for Machine Learning in Python written by Yoshiyasu Takefuji and published by . This book was released on 2017-06-17 with total page 51 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book illustrates how to build a GPU parallel computer. If you don't want to waste your time for building, you can buy a built-in-GPU desktop/laptop machine. All you need to do is to install GPU-enabled software for parallel computing. Imagine that we are in the midst of a parallel computing era. The GPU parallel computer is suitable for machine learning, deep (neural network) learning. For example, GeForce GTX1080 Ti is a GPU board with 3584 CUDA cores. Using the GeForce GTX1080 Ti, the performance is roughly 20 times faster than that of an INTEL i7 quad-core CPU. We have benchmarked the MNIST hand-written digits recognition problem (60,000 persons: hand-written digits from 0 to 9). The result of MNIST benchmark for machine learning shows that GPU of a single GeForce GTX1080 Ti board takes only less than 48 seconds while the INTEL i7 quad-core CPU requires 15 minutes and 42 seconds. A CUDA core is most commonly referring to the single-precision floating point units in an SM (streaming multiprocessor). A CUDA core can initiate one single precision floating point instruction per clock cycle. CUDA is a parallel computing platform and application programming interface (API) model created by Nvidia. It allows software developers and software engineers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing. The GPU parallel computer is based on SIMD ( single instruction, multiple data) computing.The first GPU for neural networks was used by Kyoung-Su Oh, et al. for image processing published in 2004 (1). A minimum GPU parallel computer is composed of a CPU board and a GPU board. This book contains the important issue on which CPU/GPU board you should buy and also illustrates how to integrate them in a single box by considering the heat problem. The power consumption of GPU is so large that we should take care of the temperature and heat from the GPU board in the single box. Our goal is to have the faster parallel computer with lower power dissipation. Software installation is another critical issue for machine learning in Python. Two operating system examples including Ubuntu16.04 and Windows 10 system will be described. This book shows how to install CUDA and cudnnlib in two operating systems. Three frameworks including pytorch, keras, and chainer for machine learning on CUDA and cudnnlib will be introduced. Matching problems between operating system (Ubuntu, Windows 10), library (CUDA, cudnnlib), and machine learning framework (pytorch, keras, chainer) are discussed. The paper entitled "GPU" and "open source software" play a key role for advancing deep learning was published in Science (eLetter, July 20 2017)http://science.sciencemag.org/content/357/6346/16/tab-e-letters
Book Synopsis Parallel Computing: Technology Trends by : I. Foster
Download or read book Parallel Computing: Technology Trends written by I. Foster and published by IOS Press. This book was released on 2020-03-25 with total page 806 pages. Available in PDF, EPUB and Kindle. Book excerpt: The year 2019 marked four decades of cluster computing, a history that began in 1979 when the first cluster systems using Components Off The Shelf (COTS) became operational. This achievement resulted in a rapidly growing interest in affordable parallel computing for solving compute intensive and large scale problems. It also directly lead to the founding of the Parco conference series. Starting in 1983, the International Conference on Parallel Computing, ParCo, has long been a leading venue for discussions of important developments, applications, and future trends in cluster computing, parallel computing, and high-performance computing. ParCo2019, held in Prague, Czech Republic, from 10 – 13 September 2019, was no exception. Its papers, invited talks, and specialized mini-symposia addressed cutting-edge topics in computer architectures, programming methods for specialized devices such as field programmable gate arrays (FPGAs) and graphical processing units (GPUs), innovative applications of parallel computers, approaches to reproducibility in parallel computations, and other relevant areas. This book presents the proceedings of ParCo2019, with the goal of making the many fascinating topics discussed at the meeting accessible to a broader audience. The proceedings contains 57 contributions in total, all of which have been peer-reviewed after their presentation. These papers give a wide ranging overview of the current status of research, developments, and applications in parallel computing.
Book Synopsis Neural Network Parallel Computing by : Yoshiyasu Takefuji
Download or read book Neural Network Parallel Computing written by Yoshiyasu Takefuji and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural Network Parallel Computing is the first book available to the professional market on neural network computing for optimization problems. This introductory book is not only for the novice reader, but for experts in a variety of areas including parallel computing, neural network computing, computer science, communications, graph theory, computer aided design for VLSI circuits, molecular biology, management science, and operations research. The goal of the book is to facilitate an understanding as to the uses of neural network models in real-world applications. Neural Network Parallel Computing presents a major breakthrough in science and a variety of engineering fields. The computational power of neural network computing is demonstrated by solving numerous problems such as N-queen, crossbar switch scheduling, four-coloring and k-colorability, graph planarization and channel routing, RNA secondary structure prediction, knight's tour, spare allocation, sorting and searching, and tiling. Neural Network Parallel Computing is an excellent reference for researchers in all areas covered by the book. Furthermore, the text may be used in a senior or graduate level course on the topic.
Book Synopsis Advances in Edge Computing: Massive Parallel Processing and Applications by : F. Xhafa
Download or read book Advances in Edge Computing: Massive Parallel Processing and Applications written by F. Xhafa and published by IOS Press. This book was released on 2020-03-10 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: The rapid advance of Internet of Things (IoT) technologies has resulted in the number of IoT-connected devices growing exponentially, with billions of connected devices worldwide. While this development brings with it great opportunities for many fields of science, engineering, business and everyday life, it also presents challenges such as an architectural bottleneck – with a very large number of IoT devices connected to a rather small number of servers in Cloud data centers – and the problem of data deluge. Edge computing aims to alleviate the computational burden of the IoT for the Cloud by pushing some of the computations and logics of processing from the Cloud to the Edge of the Internet. It is becoming commonplace to allocate tasks and applications such as data filtering, classification, semantic enrichment and data aggregation to this layer, but to prevent this new layer from itself becoming another bottleneck for the whole computing stack from IoT to the Cloud, the Edge computing layer needs to be capable of implementing massively parallel and distributed algorithms efficiently. This book, Advances in Edge Computing: Massive Parallel Processing and Applications, addresses these challenges in 11 chapters. Subjects covered include: Fog storage software architecture; IoT-based crowdsourcing; the industrial Internet of Things; privacy issues; smart home management in the Cloud and the Fog; and a cloud robotic solution to assist medical applications. Providing an overview of developments in the field, the book will be of interest to all those working with the Internet of Things and Edge computing.
Book Synopsis Scaling Up Machine Learning by : Ron Bekkerman
Download or read book Scaling Up Machine Learning written by Ron Bekkerman and published by Cambridge University Press. This book was released on 2012 with total page 493 pages. Available in PDF, EPUB and Kindle. Book excerpt: This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.
