The Parallel Process

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.

How do we build resilient children who can handle life's challenges? As parents today, we often feel that our role is to protect our children from the world: to cushion them when they fall, to lift them over obstacles, and to remove sharp rocks from their path. But controling a child’s entire environment and keeping all pain at bay isn’t feasible—we can’t prepare the world for our children, so instead we should focus on preparing our children for the world. “The solution is not removing impediments from our children’s lives,” writes Krissy Pozatek, “it is compassionately encouraging them to be brave.” We need to show our kids how to navigate their own terrain. If our kids face small hurdles, small pains, at a young age and learn to overcome these obstacles, they will be much better equipped to face larger trouble later in life. Early lessons in problem solving teach self-confidence and self-reliance—and show us that our kids are tougher than we think. Krissy draws her lessons from her experience guiding children in wilderness therapy and from her Buddhist practice—showing us that all life is as unpredictable as mountain weather, that impermanence is the only constant, and that the most loving act a parent can do is fearlessly ready their child to face the wilderness. For parents of children of all ages.

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.

Parallel processing is not only a general topic of interest for computer scientists and researchers in artificial intelligence, but it is gaining more and more attention in the community of scientists studying natural language and its processing (computational linguists, AI researchers, psychologists). The growing need to integrate large divergent bodies of knowledge in natural language processing applications, or the belief that massively parallel systems are the only ones capable of handling the complexities and subtleties of natural language, are just two examples of the reasons for this increasing interest.

This book introduces a process calculus for parallel, distributed and reactive systems. It describes the conceptual foundations as well as the mathematical theory behind a programming language, and a number of application examples. The chosen approach provides a framework for understanding the semantics of parallel and distributed systems. Moreover, it can be directly applied to practical problems.

Overview and Goals This book is dedicated to scheduling for parallel processing. Presenting a research ?eld as broad as this one poses considerable dif?culties. Scheduling for parallel computing is an interdisciplinary subject joining many ?elds of science and te- nology. Thus, to understand the scheduling problems and the methods of solving them it is necessary to know the limitations in related areas. Another dif?culty is that the subject of scheduling parallel computations is immense. Even simple search in bibliographical databases reveals thousands of publications on this topic. The - versity in understanding scheduling problems is so great that it seems impossible to juxtapose them in one scheduling taxonomy. Therefore, most of the papers on scheduling for parallel processing refer to one scheduling problem resulting from one way of perceiving the reality. Only a few publications attempt to arrange this ?eld of knowledge systematically. In this book we will follow two guidelines. One guideline is a distinction - tween scheduling models which comprise a set of scheduling problems solved by dedicated algorithms. Thus, the aim of this book is to present scheduling models for parallel processing, problems de?ned on the grounds of certain scheduling models, and algorithms solving the scheduling problems. Most of the scheduling problems are combinatorial in nature. Therefore, the second guideline is the methodology of computational complexity theory. Inthisbookwepresentfourexamplesofschedulingmodels. Wewillgodeepinto the models, problems, and algorithms so that after acquiring some understanding of them we will attempt to draw conclusions on their mutual relationships.

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 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.