Sinopsis
Take an in-depth look at techniques for the design and analysis of parallel algorithms with this new text. The broad, balanced coverage of important core topics includes sorting and graph algorithms, discrete optimization techniques, and scientific computing applications. The authors focus on parallel algorithms for realistic machine models while avoiding architectures that are unrealizable in practice. They provide numerous examples and diagrams illustrating potentially difficult subjects and conclude each chapter with an extensive list of bibliographic references. In addition, problems of varying degrees of difficulty challenge readers at different levels. Introduction to Parallel Computing is an ideal tool for students and professionals who want insight into problem-solving with parallel computers. Features: *Presents parallel algorithms in terms of a small set of basic data communication operations, greatly simplifying the design and understanding of these algorithms. *Emphasizes practical issues of performance, efficiency, and scalability. *Provides a self-contained discussion of the basic concepts of parallel computer architectures. *Covers algorithms for scientific computation, such as dense and sparse matrix computations, linear system solving, finite elements, and FFT. *Discusses algorithms for combinatorial optimization, including branch-and-bound, unstructured tree search, and dynamic programming. *Incorporates various parallel programming models and languages as well as illustrative examples for commercially-available computers. Audience: Junior/Senior/Graduate Computer Science and Computer Engineering majors Professional/Reference Courses: Distributed Computing Parallel Programming Parallel Algorithms Prerequisites: Operating Systems and Analysis of Algorithms 0805331700B04062001
Reseña del editor
Take an in-depth look at techniques for the design and analysis of parallel algorithms with this new text. The broad, balanced coverage of important core topics includes sorting and graph algorithms, discrete optimization techniques, and scientific computing applications. The authors focus on parallel algorithms for realistic machine models while avoiding architectures that are unrealizable in practice. They provide numerous examples and diagrams illustrating potentially difficult subjects and conclude each chapter with an extensive list of bibliographic references. In addition, problems of varying degrees of difficulty challenge readers at different levels. Introduction to Parallel Computing is an ideal tool for students and professionals who want insight into problem-solving with parallel computers. Features: *Presents parallel algorithms in terms of a small set of basic data communication operations, greatly simplifying the design and understanding of these algorithms. *Emphasizes practical issues of performance, efficiency, and scalability. *Provides a self-contained discussion of the basic concepts of parallel computer architectures. *Covers algorithms for scientific computation, such as dense and sparse matrix computations, linear system solving, finite elements, and FFT. *Discusses algorithms for combinatorial optimization, including branch-and-bound, unstructured tree search, and dynamic programming. *Incorporates various parallel programming models and languages as well as illustrative examples for commercially-available computers. Audience: Junior/Senior/Graduate Computer Science and Computer Engineering majors Professional/Reference Courses: Distributed Computing Parallel Programming Parallel Algorithms Prerequisites: Operating Systems and Analysis of Algorithms 0805331700B04062001
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