The Parallel Process
The Parallel Process

Author: Krissy Pozatek

Publisher: Lantern Books

Published: 2010-12

Total Pages: 193

ISBN-13: 1590562364

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For many parents of troubled teenagers, a therapeutic program that takes the child from the home for a period of time offers some respite from the daily tumult of acting out, lies, and tension that has left the family under siege. However, just as the teenager is embarking on a journey of self-discovery, skill-development, and emotional maturation, so parents too need to use this time to recognize that their own patterns may have contributed to their family's downward spiral. This is The Parallel Process. Using case studies garnered from her many years as an adolescent and family therapist, Krissy Pozatek shows parents of pre-teens, adolescents, and young adults how they can help their children by attuning to emotions, setting limits, not rushing to their rescue, and allowing them to take responsibility for their actions, while recognizing their own patterns of emotional withdrawal, workaholism, and of surrendering their lives and personalities to parenting. The Parallel Process is an essential primer for all parents, whether of troubled teens or not, who are seeking to help the family stay and grow together as they negotiate the potentially difficult teenage years.

Introduction to Parallel Processing
Introduction to Parallel Processing

Author: Behrooz Parhami

Publisher: Springer Science & Business Media

Published: 2006-04-11

Total Pages: 512

ISBN-13: 0306469642

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THE CONTEXT OF PARALLEL PROCESSING The field of digital computer architecture has grown explosively in the past two decades. Through a steady stream of experimental research, tool-building efforts, and theoretical studies, the design of an instruction-set architecture, once considered an art, has been transformed into one of the most quantitative branches of computer technology. At the same time, better understanding of various forms of concurrency, from standard pipelining to massive parallelism, and invention of architectural structures to support a reasonably efficient and user-friendly programming model for such systems, has allowed hardware performance to continue its exponential growth. This trend is expected to continue in the near future. This explosive growth, linked with the expectation that performance will continue its exponential rise with each new generation of hardware and that (in stark contrast to software) computer hardware will function correctly as soon as it comes off the assembly line, has its down side. It has led to unprecedented hardware complexity and almost intolerable dev- opment costs. The challenge facing current and future computer designers is to institute simplicity where we now have complexity; to use fundamental theories being developed in this area to gain performance and ease-of-use benefits from simpler circuits; to understand the interplay between technological capabilities and limitations, on the one hand, and design decisions based on user and application requirements on the other.

Programming Massively Parallel Processors
Programming Massively Parallel Processors

Author: David B. Kirk

Publisher: Newnes

Published: 2012-12-31

Total Pages: 519

ISBN-13: 0123914183

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Programming Massively Parallel Processors: A Hands-on Approach, Second Edition, teaches students how to program massively parallel processors. It offers a detailed discussion of various techniques for constructing parallel programs. Case studies are used to demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs. This guide shows both student and professional alike the basic concepts of parallel programming and GPU architecture. Topics of performance, floating-point format, parallel patterns, and dynamic parallelism are covered in depth. This revised edition contains more parallel programming examples, commonly-used libraries such as Thrust, and explanations of the latest tools. It also provides new coverage of CUDA 5.0, improved performance, enhanced development tools, increased hardware support, and more; increased coverage of related technology, OpenCL and new material on algorithm patterns, GPU clusters, host programming, and data parallelism; and two new case studies (on MRI reconstruction and molecular visualization) that explore the latest applications of CUDA and GPUs for scientific research and high-performance computing. This book should be a valuable resource for advanced students, software engineers, programmers, and hardware engineers. - New coverage of CUDA 5.0, improved performance, enhanced development tools, increased hardware support, and more - Increased coverage of related technology, OpenCL and new material on algorithm patterns, GPU clusters, host programming, and data parallelism - Two new case studies (on MRI reconstruction and molecular visualization) explore the latest applications of CUDA and GPUs for scientific research and high-performance computing

Parallel Processing from Applications to Systems
Parallel Processing from Applications to Systems

Author: Dan I. Moldovan

Publisher: Elsevier

Published: 2014-06-28

Total Pages: 586

ISBN-13: 1483297519

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This text provides one of the broadest presentations of parallel processing available, including the structure of parallelprocessors and parallel algorithms. The emphasis is on mapping algorithms to highly parallel computers, with extensive coverage of array and multiprocessor architectures. Early chapters provide insightful coverage on the analysis of parallel algorithms and program transformations, effectively integrating a variety of material previously scattered throughout the literature. Theory and practice are well balanced across diverse topics in this concise presentation. For exceptional clarity and comprehension, the author presents complex material in geometric graphs as well as algebraic notation. Each chapter includes well-chosen examples, tables summarizing related key concepts and definitions, and a broad range of worked exercises. - Overview of common hardware and theoretical models, including algorithm characteristics and impediments to fast performance - Analysis of data dependencies and inherent parallelism through program examples, building from simple to complex - Graphic and explanatory coverage of program transformations - Easy-to-follow presentation of parallel processor structures and interconnection networks, including parallelizing and restructuring compilers - Parallel synchronization methods and types of parallel operating systems - Detailed descriptions of hypercube systems - Specialized chapters on dataflow and on AI architectures

Parallel Processing and Parallel Algorithms
Parallel Processing and Parallel Algorithms

Author: Seyed H Roosta

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 579

ISBN-13: 1461212200

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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.

Patterns for Parallel Programming
Patterns for Parallel Programming

Author: Timothy G. Mattson

Publisher: Pearson Education

Published: 2004-09-15

Total Pages: 786

ISBN-13: 0321630033

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The Parallel Programming Guide for Every Software Developer From grids and clusters to next-generation game consoles, parallel computing is going mainstream. Innovations such as Hyper-Threading Technology, HyperTransport Technology, and multicore microprocessors from IBM, Intel, and Sun are accelerating the movement's growth. Only one thing is missing: programmers with the skills to meet the soaring demand for parallel software. That's where Patterns for Parallel Programming comes in. It's the first parallel programming guide written specifically to serve working software developers, not just computer scientists. The authors introduce a complete, highly accessible pattern language that will help any experienced developer "think parallel"-and start writing effective parallel code almost immediately. Instead of formal theory, they deliver proven solutions to the challenges faced by parallel programmers, and pragmatic guidance for using today's parallel APIs in the real world. Coverage includes: Understanding the parallel computing landscape and the challenges faced by parallel developers Finding the concurrency in a software design problem and decomposing it into concurrent tasks Managing the use of data across tasks Creating an algorithm structure that effectively exploits the concurrency you've identified Connecting your algorithmic structures to the APIs needed to implement them Specific software constructs for implementing parallel programs Working with today's leading parallel programming environments: OpenMP, MPI, and Java Patterns have helped thousands of programmers master object-oriented development and other complex programming technologies. With this book, you will learn that they're the best way to master parallel programming too.

Introduction to Parallel Programming
Introduction to Parallel Programming

Author: Subodh Kumar

Publisher: Cambridge University Press

Published: 2022-07-31

Total Pages:

ISBN-13: 1009276301

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In modern computer science, there exists no truly sequential computing system; and most advanced programming is parallel programming. This is particularly evident in modern application domains like scientific computation, data science, machine intelligence, etc. This lucid introductory textbook will be invaluable to students of computer science and technology, acting as a self-contained primer to parallel programming. It takes the reader from introduction to expertise, addressing a broad gamut of issues. It covers different parallel programming styles, describes parallel architecture, includes parallel programming frameworks and techniques, presents algorithmic and analysis techniques and discusses parallel design and performance issues. With its broad coverage, the book can be useful in a wide range of courses; and can also prove useful as a ready reckoner for professionals in the field.

Parallel Processing for Scientific Computing
Parallel Processing for Scientific Computing

Author: Michael A. Heroux

Publisher: SIAM

Published: 2006-01-01

Total Pages: 421

ISBN-13: 9780898718133

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Parallel processing has been an enabling technology in scientific computing for more than 20 years. This book is the first in-depth discussion of parallel computing in 10 years; it reflects the mix of topics that mathematicians, computer scientists, and computational scientists focus on to make parallel processing effective for scientific problems. Presently, the impact of parallel processing on scientific computing varies greatly across disciplines, but it plays a vital role in most problem domains and is absolutely essential in many of them. Parallel Processing for Scientific Computing is divided into four parts: The first concerns performance modeling, analysis, and optimization; the second focuses on parallel algorithms and software for an array of problems common to many modeling and simulation applications; the third emphasizes tools and environments that can ease and enhance the process of application development; and the fourth provides a sampling of applications that require parallel computing for scaling to solve larger and realistic models that can advance science and engineering.

Programming Models for Parallel Computing
Programming Models for Parallel Computing

Author: Pavan Balaji

Publisher: MIT Press

Published: 2015-11-06

Total Pages: 488

ISBN-13: 0262528819

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An overview of the most prominent contemporary parallel processing programming models, written in a unique tutorial style. With the coming of the parallel computing era, computer scientists have turned their attention to designing programming models that are suited for high-performance parallel computing and supercomputing systems. Programming parallel systems is complicated by the fact that multiple processing units are simultaneously computing and moving data. This book offers an overview of some of the most prominent parallel programming models used in high-performance computing and supercomputing systems today. The chapters describe the programming models in a unique tutorial style rather than using the formal approach taken in the research literature. The aim is to cover a wide range of parallel programming models, enabling the reader to understand what each has to offer. The book begins with a description of the Message Passing Interface (MPI), the most common parallel programming model for distributed memory computing. It goes on to cover one-sided communication models, ranging from low-level runtime libraries (GASNet, OpenSHMEM) to high-level programming models (UPC, GA, Chapel); task-oriented programming models (Charm++, ADLB, Scioto, Swift, CnC) that allow users to describe their computation and data units as tasks so that the runtime system can manage computation and data movement as necessary; and parallel programming models intended for on-node parallelism in the context of multicore architecture or attached accelerators (OpenMP, Cilk Plus, TBB, CUDA, OpenCL). The book will be a valuable resource for graduate students, researchers, and any scientist who works with data sets and large computations. Contributors Timothy Armstrong, Michael G. Burke, Ralph Butler, Bradford L. Chamberlain, Sunita Chandrasekaran, Barbara Chapman, Jeff Daily, James Dinan, Deepak Eachempati, Ian T. Foster, William D. Gropp, Paul Hargrove, Wen-mei Hwu, Nikhil Jain, Laxmikant Kale, David Kirk, Kath Knobe, Ariram Krishnamoorthy, Jeffery A. Kuehn, Alexey Kukanov, Charles E. Leiserson, Jonathan Lifflander, Ewing Lusk, Tim Mattson, Bruce Palmer, Steven C. Pieper, Stephen W. Poole, Arch D. Robison, Frank Schlimbach, Rajeev Thakur, Abhinav Vishnu, Justin M. Wozniak, Michael Wilde, Kathy Yelick, Yili Zheng