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CS1760: Multiprocessor Synchronization
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We ship from multiple locations. Prompt customer service. This revised edition incorporates much-demanded updates throughout the book, based on feedback and corrections reported from classrooms since Learn the fundamentals of programming multiple threads accessing shared memory Explore mainstream concurrent data structures and the key elements of their design, as well as synchronization techniques from simple locks to transactional memory systems Visit the companion site and download source code, example Java programs, and materials to support and enhance the learning experience.
Would you like to save your cart? Explains principles and strategies to learn parallel programming with OpenCL, from understanding the four abstraction models to thoroughly testing and debugging complete applications. Covers image processing, web plugins, particle simulations, video editing, performance optimization, and more. Shows how OpenCL maps to an example target architecture and explains some of the tradeoffs associated with mapping to various architecturesAddresses a range of fundamental programming techniques, with multiple examples and case studies that demonstrate OpenCL extensions for a variety of hardware platforms.
Michael McCool. Structured Parallel Programming offers the simplest way for developers to learn patterns for high-performance parallel programming. Written by parallel computing experts and industry insiders Michael McCool, Arch Robison, and James Reinders, this book explains how to design and implement maintainable and efficient parallel algorithms using a composable, structured, scalable, and machine-independent approach to parallel computing. It presents both theory and practice, and provides detailed concrete examples using multiple programming models. The patterns-based approach offers structure and insight that developers can apply to a variety of parallel programming modelsDevelops a composable, structured, scalable, and machine-independent approach to parallel computingIncludes detailed examples in both Cilk Plus and the latest Threading Building Blocks, which support a wide variety of computers.
James Reinders. High Performance Parallelism Pearls shows how to leverage parallelism on processors and coprocessors with the same programming — illustrating the most effective ways to better tap the computational potential of systems with Intel Xeon Phi coprocessors and Intel Xeon processors or other multicore processors. The book includes examples of successful programming efforts, drawn from across industries and domains such as chemistry, engineering, and environmental science. Each chapter in this edited work includes detailed explanations of the programming techniques used, while showing high performance results on both Intel Xeon Phi coprocessors and multicore processors.
Learn from dozens of new examples and case studies illustrating "success stories" demonstrating not just the features of these powerful systems, but also how to leverage parallelism across these heterogeneous systems. Robert Oshana. This book provides a set of practical processes and techniques used for multicore software development. It is written with a focus on solving day to day problems using practical tips and tricks and industry case studies to reinforce the key concepts in multicore software development.
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We received over regular submissions, a record for DISC. These s- missions were read and evaluated by the program committee, with the help of external reviewers when needed.
Overall, the quality of the submissions was excellent, and we were unable to accept many deserving papers. Arie Fouren is the student author. David B.
The Art Of Multiprocessor Programming, Revised Reprint
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. New coverage of CUDA 5. How to Think About Algorithms. Jeff Edmonds. This textbook, for second- or third-year students of computer science, presents insights, notations, and analogies to help them describe and think about algorithms like an expert, without grinding through lots of formal proof.
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Solutions to many problems are provided to let students check their progress, while class-tested PowerPoint slides are on the web for anyone running the course. By looking at both the big picture and easy step-by-step methods for developing algorithms, the author guides students around the common pitfalls. He stresses paradigms such as loop invariants and recursion to unify a huge range of algorithms into a few meta-algorithms.
The book fosters a deeper understanding of how and why each algorithm works. These insights are presented in a careful and clear way, helping students to think abstractly and preparing them for creating their own innovative ways to solve problems. An Introduction to Parallel Programming. Peter Pacheco.
An Introduction to Parallel Programming is the first undergraduate text to directly address compiling and running parallel programs on the new multi-core and cluster architecture. It explains how to design, debug, and evaluate the performance of distributed and shared-memory programs. The author Peter Pacheco uses a tutorial approach to show students how to develop effective parallel programs with MPI, Pthreads, and OpenMP, starting with small programming examples and building progressively to more challenging ones.
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The text is written for students in undergraduate parallel programming or parallel computing courses designed for the computer science major or as a service course to other departments; professionals with no background in parallel computing. Takes a tutorial approach, starting with small programming examples and building progressively to more challenging examplesFocuses on designing, debugging and evaluating the performance of distributed and shared-memory programsExplains how to develop parallel programs using MPI, Pthreads, and OpenMP programming models.
Distributed Computing Through Combinatorial Topology. Distributed Computing Through Combinatorial Topology describes techniques for analyzing distributed algorithms based on award winning combinatorial topology research. The authors present a solid theoretical foundation relevant to many real systems reliant on parallelism with unpredictable delays, such as multicore microprocessors, wireless networks, distributed systems, and Internet protocols.
Named a Notable Computer Book for Computing Methodologies by Computing ReviewsGathers knowledge otherwise spread across research and conference papers using consistent notations and a standard approach to facilitate understandingPresents unique insights applicable to multiple computing fields, including multicore microprocessors, wireless networks, distributed systems, and Internet protocols Synthesizes and distills material into a simple, unified presentation with examples, illustrations, and exercises.