Topics In Parallel And Distributed Computing

Topics in Parallel and Distributed Computing provides resources and guidance for those learning PDC as well as those teaching students new to the discipline. The pervasiveness of computing devices containing multicore CPUs and GPUs, including home and office PCs, laptops, and mobile devices, is making even common users dependent on parallel processing. Certainly, it is no longer sufficient for even basic programmers to acquire only the traditional sequential programming skills. The preceding trends point to the need for imparting a broad-based skill set in PDC technology. However, the rapid changes in computing hardware platforms and devices, languages, supporting programming environments, and research advances, poses a challenge both for newcomers and seasoned computer scientists. This edited collection has been developed over the past several years in conjunction with the IEEE technical committee on parallel processing (TCPP), which held several workshops and discussions on learning parallel computing and integrating parallel concepts into courses throughout computer science curricula. Contributed and developed by the leading minds in parallel computing research and instruction Provides resources and guidance for those learning PDC as well as those teaching students new to the discipline Succinctly addresses a range of parallel and distributed computing topics Pedagogically designed to ensure understanding by experienced engineers and newcomers Developed over the past several years in conjunction with the IEEE technical committee on parallel processing (TCPP), which held several workshops and discussions on learning parallel computing and integrating parallel concepts

This book introduces beginning undergraduate students of computing and computational disciplines to modern parallel and distributed programming languages and environments, including map-reduce, general-purpose graphics processing units (GPUs), and graphical user interfaces (GUI) for mobile applications. The book also guides instructors via selected essays on what and how to introduce parallel and distributed computing topics into the undergraduate curricula, including quality criteria for parallel algorithms and programs, scalability, parallel performance, fault tolerance, and energy efficiency analysis. The chapters designed for students serve as supplemental textual material for early computing core courses, which students can use for learning and exercises. The illustrations, examples, and sequences of smaller steps to build larger concepts are also tools that could be inserted into existing instructor material. The chapters intended for instructors are written at a teaching level and serve as a rigorous reference to include learning goals, advice on presentation and use of the material, within early and advanced undergraduate courses. Since Parallel and Distributed Computing (PDC) now permeates most computing activities, imparting a broad-based skill set in PDC technology at various levels in the undergraduate educational fabric woven by Computer Science (CS) and Computer Engineering (CE) programs as well as related computational disciplines has become essential. This book and others in this series aim to address the need for lack of suitable textbook support for integrating PDC-related topics into undergraduate courses, especially in the early curriculum. The chapters are aligned with the curricular guidelines promulgated by the NSF/IEEE-TCPP Curriculum Initiative on Parallel and Distributed Computing for CS and CE students and with the CS2013 ACM/IEEE Computer Science Curricula.

This book introduces beginning undergraduate students of computing and computational disciplines to modern parallel and distributed programming languages and environments, including map-reduce, general-purpose graphics processing units (GPUs), and graphical user interfaces (GUI) for mobile applications. The book also guides instructors via selected essays on what and how to introduce parallel and distributed computing topics into the undergraduate curricula, including quality criteria for parallel algorithms and programs, scalability, parallel performance, fault tolerance, and energy efficiency analysis. The chapters designed for students serve as supplemental textual material for early computing core courses, which students can use for learning and exercises. The illustrations, examples, and sequences of smaller steps to build larger concepts are also tools that could be inserted into existing instructor material. The chapters intended for instructors are written at a teaching level and serve as a rigorous reference to include learning goals, advice on presentation and use of the material, within early and advanced undergraduate courses. Since Parallel and Distributed Computing (PDC) now permeates most computing activities, imparting a broad-based skill set in PDC technology at various levels in the undergraduate educational fabric woven by Computer Science (CS) and Computer Engineering (CE) programs as well as related computational disciplines has become essential. This book and others in this series aim to address the need for lack of suitable textbook support for integrating PDC-related topics into undergraduate courses, especially in the early curriculum. The chapters are aligned with the curricular guidelines promulgated by the NSF/IEEE-TCPP Curriculum Initiative on Parallel and Distributed Computing for CS and CE students and with the CS2013 ACM/IEEE Computer Science Curricula.

Here, authors from academia and practice provide practitioners, scientists and graduates with basic methods and paradigms, as well as important issues and trends across the spectrum of parallel and distributed processing. In particular, they cover such fundamental topics as efficient parallel algorithms, languages for parallel processing, parallel operating systems, architecture of parallel and distributed systems, management of resources, tools for parallel computing, parallel database systems and multimedia object servers, as well as the relevant networking aspects. A chapter is dedicated to each of parallel and distributed scientific computing, high-performance computing in molecular sciences, and multimedia applications for parallel and distributed systems.

Developing correct and efficient software is far more complex for parallel and distributed systems than it is for sequential processors. Some of the reasons for this added complexity are: the lack of a universally acceptable parallel and distributed programming paradigm, the criticality of achieving high performance, and the difficulty of writing correct parallel and distributed programs. These factors collectively influence the current status of parallel and distributed software development tools efforts. Tools and Environments for Parallel and Distributed Systems addresses the above issues by describing working tools and environments, and gives a solid overview of some of the fundamental research being done worldwide. Topics covered in this collection are: mainstream program development tools, performance prediction tools and studies; debugging tools and research; and nontraditional tools. Audience: Suitable as a secondary text for graduate level courses in software engineering and parallel and distributed systems, and as a reference for researchers and practitioners in industry.

* Comprehensive introduction to the fundamental results in the mathematical foundations of distributed computing * Accompanied by supporting material, such as lecture notes and solutions for selected exercises * Each chapter ends with bibliographical notes and a set of exercises * Covers the fundamental models, issues and techniques, and features some of the more advanced topics

An all-inclusive survey of the fundamentals of parallel and distributed computing. The use of parallel and distributed computing has increased dramatically over the past few years, giving rise to a variety of projects, implementations, and buzzwords surrounding the subject. Although the areas of parallel and distributed computing have traditionally evolved separately, these models have overlapping goals and characteristics. Parallel and Distributed Computing surveys the models and paradigms in this converging area of parallel and distributed computing and considers the diverse approaches within a common text. Covering a comprehensive set of models and paradigms, the material also skims lightly over more specific details and serves as both an introduction and a survey. Novice readers will be able to quickly grasp a balanced overview with the review of central concepts, problems, and ideas, while the more experienced researcher will appreciate the specific comparisons between models, the coherency of the parallel and distributed computing field, and the discussion of less well-known proposals. Other topics covered include: * Data parallelism * Shared-memory programming * Message passing * Client/server computing * Code mobility * Coordination, object-oriented, high-level, and abstract models * And much more Parallel and Distributed Computing is a perfect tool for students and can be used as a foundation for parallel and distributed computing courses. Application developers will find this book helpful to get an overview before choosing a particular programming style to study in depth, and researchers and programmers will appreciate the wealth of information concerning the various areas of parallel and distributed computing.

Advancements in microprocessor architecture, interconnection technology, and software development have fueled rapid growth in parallel and distributed computing. However, this development is only of practical benefit if it is accompanied by progress in the design, analysis and programming of parallel algorithms. This concise textbook provides, in one place, three mainstream parallelization approaches, Open MPP, MPI and OpenCL, for multicore computers, interconnected computers and graphical processing units. An overview of practical parallel computing and principles will enable the reader to design efficient parallel programs for solving various computational problems on state-of-the-art personal computers and computing clusters. Topics covered range from parallel algorithms, programming tools, OpenMP, MPI and OpenCL, followed by experimental measurements of parallel programs’ run-times, and by engineering analysis of obtained results for improved parallel execution performances. Many examples and exercises support the exposition.