Topics In Parallel And Distributed Computing
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Topics in Parallel and Distributed Computing
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.
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
Parallel and Distributed Computation: Numerical Methods
Author: Dimitri Bertsekas
language: en
Publisher: Athena Scientific
Release Date: 2015-03-01
This highly acclaimed work, first published by Prentice Hall in 1989, is a comprehensive and theoretically sound treatment of parallel and distributed numerical methods. It focuses on algorithms that are naturally suited for massive parallelization, and it explores the fundamental convergence, rate of convergence, communication, and synchronization issues associated with such algorithms. This is an extensive book, which aside from its focus on parallel and distributed algorithms, contains a wealth of material on a broad variety of computation and optimization topics. It is an excellent supplement to several of our other books, including Convex Optimization Algorithms (Athena Scientific, 2015), Nonlinear Programming (Athena Scientific, 1999), Dynamic Programming and Optimal Control (Athena Scientific, 2012), Neuro-Dynamic Programming (Athena Scientific, 1996), and Network Optimization (Athena Scientific, 1998). The on-line edition of the book contains a 95-page solutions manual.