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An introduction to parallel algorithms
TLDR
This book provides an introduction to the design and analysis of parallel algorithms, with the emphasis on the application of the PRAM model of parallel computation, with all its variants, to algorithm analysis.Abstract:
Written by an authority in the field, this book provides an introduction to the design and analysis of parallel algorithms. The emphasis is on the application of the PRAM (parallel random access machine) model of parallel computation, with all its variants, to algorithm analysis. Special attention is given to the selection of relevant data structures and to algorithm design principles that have proved to be useful. Features *Uses PRAM (parallel random access machine) as the model for parallel computation. *Covers all essential classes of parallel algorithms. *Rich exercise sets. *Written by a highly respected author within the field. 0201548569B04062001read more
Citations
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Book ChapterDOI
A Functional Approach to External Graph Algorithms
TL;DR: This work presents a new approach for designing external graph algorithms and uses it to design simple external algorithms for computing connected components, minimum spanning trees, bottleneck minimum spanning Trees, and maximal matchings in undirected graphs and multi-graphs.
Parallelization of the Vehicle Routing Problem with Time Windows
TL;DR: A parallel algorithm based on the sequential algorithm developed in the previous part of the dissertation is developed and analyzed and a number of techniques to improve the performance of the column-generation framework are proposed and analyzed.
Proceedings ArticleDOI
Low depth cache-oblivious algorithms
TL;DR: This paper describes several cache-oblivious algorithms with optimal work, polylogarithmic depth, and sequential cache complexities that match the best sequential algorithms, including the first such algorithms for sorting and for sparse-matrix vector multiply on matrices with good vertex separators.
Journal ArticleDOI
GPU-Quicksort: A practical Quicksort algorithm for graphics processors
Daniel Cederman,Philippas Tsigas +1 more
TL;DR: GPU-Quicksort, an efficient Quicksort algorithm suitable for highly parallel multicore graphics processors, is described and shown that in CUDA, NVIDIA's programing platform for general-purpose computations on graphical processors, it performs better than the fastest-known sorting implementations for graphics processors.
Proceedings ArticleDOI
Converting high probability into nearly-constant time—with applications to parallel hashing
Yossi Matias,Uzi Vishkin +1 more
TL;DR: In this article, the authors presented a new paradigm for efficient randomized parallel algorithms that need only O(log n) time, where n is the expected number of operations, where x is the number of non-idle processors.
References
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Book
Introduction to Parallel Algorithms and Architectures: Arrays, Trees, Hypercubes
TL;DR: This chapter discusses sorting on a Linear Array with a Systolic and Semisystolic Model of Computation, which automates the very labor-intensive and therefore time-heavy and expensive process of manually sorting arrays.
Book
Computer Architecture and Parallel Processing
Kai Hwang,Faye A. Briggs +1 more
TL;DR: The authors have divided the use of computers into the following four levels of sophistication: data processing, information processing, knowledge processing, and intelligence processing.
Journal ArticleDOI
Data parallel algorithms
W. Daniel Hillis,Guy L. Steele +1 more
TL;DR: The success of data parallel algorithms—even on problems that at first glance seem inherently serial—suggests that this style of programming has much wider applicability than was previously thought.
Proceedings ArticleDOI
Parallelism in random access machines
Steven Fortune,James C. Wyllie +1 more
TL;DR: A model of computation based on random access machines operating in parallel and sharing a common memory is presented and can accept in polynomial time exactly the sets accepted by nondeterministic exponential time bounded Turing machines.
Journal ArticleDOI
The Parallel Evaluation of General Arithmetic Expressions
TL;DR: It is shown that arithmetic expressions with n ≥ 1 variables and constants; operations of addition, multiplication, and division; and any depth of parenthesis nesting can be evaluated in time 4 log 2 + 10(n - 1) using processors which can independently perform arithmetic operations in unit time.